def calculate_model_zeta(self,
station,
start_time,
end_time,
figure=True,
*args,
**kwargs):
wind_zeta_data = ReadData(self.wind_path,
types='fvcom',
variables=['time', 'zeta'])
tide_zeta_data = ReadData(self.tide_path,
types='fvcom',
variables=['time', 'zeta'])
index = wind_zeta_data.find_nearest_point('zeta', station)
time_range = pd.date_range(start_time, end_time, freq='H')
time_stamp = date2num(time_range)
start_index = np.where(wind_zeta_data.data.time[:] == time_stamp[0])
end_index = np.where(wind_zeta_data.data.time[:] == time_stamp[-1])
zeta_wind = wind_zeta_data.data.zeta[start_index[0][0]: end_index[0][0]+1, index] - \
tide_zeta_data.data.zeta[start_index[0][0]:end_index[0][0]+1, index]
if figure:
zeta_fig = PlotFigure(cartesian=True)
zeta_fig.plot_lines(time_stamp,
zeta_wind,
time_series=True,
*args,
**kwargs)
return zeta_wind
ncep_slp_data = ReadData(select_ncep_slp_path,
types='ncep',
variables=['time', 'slp'],
extents=[105, 135, 5, 45])
ncep_data = PassiveStore()
setattr(ncep_data, 'lon', ncep_wind_data.data.lon)
setattr(ncep_data, 'lat', ncep_wind_data.data.lat)
setattr(ncep_data, 'time', ncep_wind_data.data.time)
setattr(ncep_data, 'u10', ncep_wind_data.data.u10)
setattr(ncep_data, 'v10', ncep_wind_data.data.v10)
setattr(ncep_data, 'slp', ncep_slp_data.data.slp)
ptime = pltdate.num2date(ncep_data.time, tz=None)
ncep_wind_speed = np.sqrt(ncep_data.u10**2 + ncep_data.v10**2)
# # 插值前ncep画图
X, Y = np.meshgrid(ncep_data.lon, ncep_data.lat)
ncep_plot = PlotFigure(extents=[105, 135, 5, 45], title='ncep')
ncep_plot.plot_surface(X, Y, ncep_wind_speed[0, :, :])
ncep_plot.show()
# 网格数据路径
grid_path = r'H:\fvcom\fvcom_input_file\sms.2dm'
# 表面强迫的插值
prep = FvcomPrep(grid_path)
interped_data = prep.interp_surface_forcing(ncep_data)
wind_speed = np.sqrt(interped_data.uwnd**2 + interped_data.vwnd**2)
# 插值后ncep画图
fig_obj = PlotFigure(grid_path=grid_path,
figsize=(12, 8),
extents=[105, 135, 5, 45])
fig_obj.plot_surface(fig_obj.grid.lon, fig_obj.grid.lat, wind_speed[0, :])
fig_obj.show()
select_start_indx = np.where(
gauge_data.data.time == select_time_stamp[0])[0][0]
select_end_indx = np.where(gauge_data.data.time == select_time_stamp[-1])[0][0]
select_gauge_zeta = gauge_zeta[select_start_indx:select_end_indx +
1] - gauge_mean_zeta
# 选择时间范围的模式数据（减去平均）
select_start_mindx = \
np.where(model_tide_data.data.time == select_time_stamp[0])[0][0]
select_end_mindx = \
np.where(model_tide_data.data.time == select_time_stamp[-1])[0][0]
select_model_zeta = specific_mzeta[select_start_mindx:select_end_mindx + 1]
# %% 画图
plot_obj = PlotFigure(cartesian=True,
y_label='Sea Level Anomalies(m)',
legend_label=('Tide-Gauge', 'Model'),
grid=True,
time_interval=2)
plot1 = plot_obj.plot_lines(select_time_stamp,
select_gauge_zeta,
time_series=True,
color='b')
plot2 = plot_obj.plot_lines(select_time_stamp,
select_model_zeta,
linestyle='--',
color='r')
plot_obj.set_legend(plot1, plot2)
output = r'D:\2018_2019_research\china_sea_1993\images\final\before\kanmen_gmzeta.svg'
plot_obj.save(output)
# plot_obj.show()
from fvcom_tools_packages.read_data import ReadData
from fvcom_tools_packages.fvcom_plot import PlotFigure
import numpy as np
# 准备数据
ncep_path = r'H:\ncep\uv\splanl.gdas.19930801-19930805.grb2.nc'
ecmwf_path = r'E:\ecmwf\wind\1993\interim_uv1993_08.nc'
extents = [105, 135, 5, 40]
ncep_data = ReadData(ncep_path,
types='ncep',
variables=['lon', 'lat', 'time', 'wind'],
extents=extents)
ncep_wind_speed = np.sqrt(ncep_data.data.u10[:]**2 + ncep_data.data.v10[:]**2)
ecmwf_data = ReadData(ecmwf_path,
types='ecmwf',
variables=['lon', 'lat', 'time', 'wind'],
extents=extents)
ecmwf_wind_speed = np.sqrt(ecmwf_data.data.u10[:]**2 +
ecmwf_data.data.v10[:]**2)
time_index = 0
# ncep画图
X, Y = np.meshgrid(ncep_data.data.lon, ncep_data.data.lat)
ncep_plot = PlotFigure(extents=extents, title='ncep')
ncep_plot.plot_surface(X, Y, ncep_wind_speed[0, :, :])
ncep_plot.show()
# ecmwf画图
X, Y = np.meshgrid(ecmwf_data.data.lon, ecmwf_data.data.lat)
ncep_plot = PlotFigure(extents=extents, title='ecmwf')
ncep_plot.plot_surface(X, Y, ecmwf_wind_speed[0, :, :])
ncep_plot.show()
wind_spd = site_wind_data['wind_spd'].to_numpy()
wind_dir = site_wind_data['wind_dir'].to_numpy()
# 数据中的角度和quiver的角度不一样，需要加180度
wind_dir = wind_dir + 180
# 选择数据
start_time = date2num(datetime(1993, 9, 28, 2))
end_time = date2num(datetime(1993, 10, 12, 14))
start_index = np.where(start_time == time_stamp)[0][0]
end_index = np.where(end_time == time_stamp)[0][0]
select_wind_spd = wind_spd[start_index:end_index + 1]
select_wind_dir = wind_dir[start_index:end_index + 1]
select_wind_time = time_stamp[start_index:end_index + 1]
# # 画风向时间序列图
# plot_obj = PlotFigure(figsize=(12, 6),cartesian=True, title='Da Chen')
# plot_obj.plot_windDir_time(select_wind_time, select_wind_spd, select_wind_dir)
# output_name = r'D:\2018_2019_research\china_sea_1993\images\papar_images\site_station\{}.pdf'.format(station)
# # plot_obj.save(output_name)
# plot_obj.show()
# 画风速时间序列图
spd_plot = PlotFigure(cartesian=True,
title='Yu Huan',
grid=True,
y_label='Wind Speed(m/s)')
spd_plot.plot_lines(select_wind_time, select_wind_spd, time_series=True)
output_name = r'D:\2018_2019_research\china_sea_1993\images\papar_images\site_station\{}_spd.pdf'.format(
station)
spd_plot.save(output_name)
# spd_plot.show()
from fvcom_tools_packages.read_data import ReadData
import numpy as np
from matplotlib.dates import num2date
"""
功能：绘制研究区域（包含台风路径、高度计路径以及验潮站）
"""
# %% 数据准备
altimeter_path = r'E:\altimeter_data\ctoh.sla.ref.TP.chinasea.088.nc'
typhoon_path = r'E:\best_track\bwp1993\bwp261993.txt'
alti_data = ReadData(altimeter_path, types='alti', alti_cycle=39)
tp_data = ReadData(typhoon_path, types='bwp')
# %% 画图
plot_obj = PlotFigure(figsize=(10, 8),
extents=[115, 138, 15, 35],
depth=True,
tick_inc=[4, 4])
# 高度计轨迹
plot_obj.plot_lines(alti_data.data.alti_lon,
alti_data.data.alti_lat,
color='r',
lw=4)
# 台风轨迹
plot_obj.plot_lines(tp_data.data.tp_lon,
tp_data.data.tp_lat,
color='k',
marker='o',
mfc='r',
mec='b',
lw=4,
ms=10)
start_time_stamp = date2num(start_time)
end_time = datetime(1993, 10, 12, 14)
end_time_stamp = date2num(end_time)
start_time_indx = np.where(site_time == start_time_stamp)[0][0]
end_time_indx = np.where(site_time == end_time_stamp)[0][0]
select_site_time = site_time[start_time_indx:end_time_indx + 1]
select_site_wind_spd = site_data.data.wind_spd[start_time_indx:end_time_indx +
1]
select_site_wind_dir = site_data.data.wind_dir[start_time_indx:end_time_indx +
1]
select_model_uwind = model_uwinded[start_time_indx:end_time_indx + 1]
select_model_vwind = model_vwinded[start_time_indx:end_time_indx + 1]
select_model_wind = model_winded[start_time_indx:end_time_indx + 1]
# 只比较风速大小
spd_plot = PlotFigure(title='Sheng Si',
cartesian=True,
grid=True,
y_label='Wind Speed(m/s)',
legend_label=('site', 'model'),
time_interval=2)
plot1 = spd_plot.scatter(select_site_time, select_site_wind_spd, c='r')
plot2 = spd_plot.plot_lines(select_site_time,
select_model_wind,
time_series=True)
spd_plot.set_legend(plot1, plot2)
output_name = r'D:\2018_2019_research\china_sea_1993\images\final\before\{}_MSwind_ncep.svg'.format(
site_data.data.station)
spd_plot.save(output_name)
spd_plot.show()
prep_obj = FvcomPrep(grid_path)
interped_zeta = prep_obj.interp_temp_spatial(model_wind_data.lon,
model_wind_data.lat,
model_wind_data.data.time,
model_zeta,
interp_alti_lon,
interp_alti_lat,
interp_alti_time,
time_single=True,
regular=False)
# 计算出模式中对于高度计点的三个月平均值，并计算最后的模式zeta
position_indx = np.ones(len(interp_alti_lat))
i = 0
for ilon, ilat in zip(interp_alti_lon, interp_alti_lat):
position = (ilon, ilat)
indx_tmp = prep_obj.find_nearest_point('zeta', position)
position_indx[i] = indx_tmp.astype(int)
i += 1
select_zeta = np.asarray([model_zeta[:, x.astype(int)] for x in position_indx])
mean_zeta = np.mean(select_zeta, axis=1)
model_final_zeta = interped_zeta - np.mean(model_zeta)
# %% 画图
plot_obj = PlotFigure(cartesian=True,
x_label='Latitude(deg N)',
y_label='Sea Surface Height Anomalies (m)',
legend_label=('CTOH', 'Model'))
plot_obj.plot_lines(interp_alti_lat, model_final_zeta)
plot_obj.plot_lines(select_alti_lat, select_alti_sla_39)
plot_obj.show()
#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
# __NAME__ = plot_sms_grid.py
# __AUTHOR__ = 'QIU ZHOU'
# __TIME__ = 2019/7/16 10:01
"""
功能：画sms网格图
"""
from fvcom_tools_packages.fvcom_plot import PlotFigure
#%% 数据准备
sms_path = r'E:\fvcom\fvcom_input_file\sms.2dm'
#%% 画图
plot_obj = PlotFigure(grid_path=sms_path,
extents=[105, 135, 6, 40],
tick_inc=[5, 5])
plot_obj.plot_sms_grid(lw=0.1)
output = r'D:\2018_2019_research\china_sea_1993\images\final\before\grid.svg'
plot_obj.save(output, dpi=300)
plot_obj.show()
start_time = date2num(datetime(1993, 9, 28, 2))
end_time = date2num(datetime(1993, 10, 12, 14))
start_index = np.where(start_time == time_stamp)[0][0]
end_index = np.where(end_time == time_stamp)[0][0]
select_temp = temp[start_index:end_index + 1]
select_max_temp = max_temp[start_index:end_index + 1]
select_min_temp = min_temp[start_index:end_index + 1]
select_time = time_stamp[start_index:end_index + 1]
select_press = press[start_index:end_index + 1]
select_max_press = max_press[start_index:end_index + 1]
select_min_press = min_press[start_index:end_index + 1]
# Temp-Press画图
TP_plot = PlotFigure(cartesian=True,
title='Yu Huan',
y_label='Temperature(℃)',
grid=True,
legend_label=('Temperture', 'Pressure'))
TP_plot.plot_lines(select_time,
select_temp,
time_series=True,
double_yaxis=True,
double_y=select_press,
double_ylabel='Air Pressure(hPa)')
output_name = r'D:\2018_2019_research\china_sea_1993\images\papar_images\site_station\{}_TP.pdf'.format(
station)
# TP_plot.save(output_name)
TP_plot.show()
# Max_temp画图
MT_plot = PlotFigure(cartesian=True,
freq='H')
select_time_stamp = date2num(select_time)
select_start_indx = np.where(gauge_data.data.time == select_time_stamp[0])[0][0]
select_end_indx = np.where(gauge_data.data.time == select_time_stamp[-1])[0][0]
select_gauge_zeta = gauge_zeta[select_start_indx:select_end_indx + 1]
# %% 验潮站相对于高度计过境时间时的增水和前期最大增水）
alti_pass_time = datetime(1993, 10, 8, 8, 32)
alti_pass_date = alti_pass_time.strftime('%m-%dT%H:%M')
alti_pass_stime = date2num(alti_pass_time)
zeta_func = interp1d(select_time_stamp, select_gauge_zeta)
gauge_zeta_alti = zeta_func(alti_pass_stime)
point_first = 46 # 62手动选出厦门的第一个峰值的点，坎门的是46
# %% 画图
plot_obj = PlotFigure(cartesian=True, grid=True,
y_label='Sea Level Anomalies(m)',
time_interval=2,
extents=[select_time_stamp[0],select_time_stamp[-1],
-0.2, 0.8])
plot_obj.plot_lines(select_time_stamp, select_gauge_zeta,
time_series=True, color='b')
plot_obj.vline(alti_pass_stime)
plot_obj.vline(select_time_stamp[point_first])
plot_obj.annotate(alti_pass_date, xy=(alti_pass_stime, gauge_zeta_alti),
xytext=(alti_pass_stime + 1, gauge_zeta_alti + 0.1))
plot_obj.annotate(select_time[point_first].strftime('%m-%dT%H:%M'),
xy=(select_time_stamp[point_first], select_gauge_zeta[point_first]),
xytext=(
select_time_stamp[point_first] + 1, select_gauge_zeta[point_first] + 0.1))
out_put = r'D:\2018_2019_research\china_sea_1993\images\final\before\gauge_xiamen.svg'
plot_obj.save(out_put)
plot_obj.show()
select_lat = depth_lat[lat_index_down:lat_index_up + 1]
select_lon = depth_lon[lon_index_left:lon_index_right + 1]
select_depth = depth[lat_index_down:lat_index_up + 1,
lon_index_left:lon_index_right + 1]
# 插值水深
interp_depth = []
func = interp2d(select_lon, select_lat, select_depth)
for i in range(len(lat)):
z = func(lon[i], lat[i])
interp_depth.append(z)
# 计算距离
distance = []
for j in range(len(lat)):
dis = distance_on_sphere(lon[0], lat[0], lon[j], lat[j]) / 1000
distance.append(dis)
# 画图
plot_obj = PlotFigure(cartesian=True,
x_label='Distance(km)',
y_label='Depth(m)',
extents=[0, 500, -2500, 0])
plot_obj.plot_lines(distance, interp_depth)
plot_obj.plot_lines()
plot_obj.ax.xaxis.set_ticks_position('top')
plot_obj.ax.xaxis.set_label_position('top')
plot_obj.ax.spines['bottom'].set_visible(False)
plot_obj.ax.spines['right'].set_visible(False)
output = r'D:\2018_2019_research\china_sea_1993\images\final\before\section2.svg'
# plot_obj.save(output)
plot_obj.show()
extents=[105, 135, 5, 40])
# 选择画图的时间范围
start_time = datetime(1993, 9, 25, 0)
end_time = datetime(1993, 10, 12, 18)
start_time_stamp = date2num(start_time)
end_time_stamp = date2num(end_time)
start_indx = np.where(start_time_stamp == temp_data.data.time)[0][0]
end_indx = np.where(end_time_stamp == temp_data.data.time)[0][0]
select_t2m = temp_data.data.t2m[start_indx:end_indx + 1, :, :]
select_sst = temp_data.data.sst[start_indx:end_indx + 1, :, :]
select_time = temp_data.data.time[start_indx:end_indx + 1]
# 作图
for itime in range(len(select_time)):
title = num2date(select_time[itime]).strftime('%Y-%m-%dT%H')
temp_plot = PlotFigure(extents=[110, 135, 15, 40],
tick_inc=[4, 5],
title=title,
cb_label='℃',
cb_lim=[18, 36])
temp_plot.plot_surface(temp_data.data.lon, temp_data.data.lat,
select_t2m[itime, :, :])
temp_plot.fill_region(city='Fujian', taiwan=True)
out_dir = r'D:\2018_2019_research\china_sea_1993\images\papar_images\ecmwf_t2m'
outname = 't2m' + num2date(
select_time[itime]).strftime('%Y%m%dT%H') + '.png'
out_path = os.path.join(out_dir, outname)
temp_plot.save(out_path, dpi=300)
# temp_plot.show()