207 行
8.6 KiB
Python
207 行
8.6 KiB
Python
# -*- coding: utf-8 -*-
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import warnings
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warnings.filterwarnings('ignore')
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import argparse, json
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from common import NoIndent, MyEncoder
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def _argparse():
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parser = argparse.ArgumentParser(description='tl realtime client')
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parser.add_argument('-a', '--argfile', action='store', dest='argfile', required=True, help='name of arguments file')
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return parser.parse_args()
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def main():
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parser = _argparse()
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argfile = parser.argfile
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if argfile[-5:] != '.json':
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print('输入的参数文件名有误')
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return
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with open(argfile,'r') as jsonFile:
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paramData = json.load(jsonFile)
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if ("freq" not in paramData) or ("sourceDepth" not in paramData) or\
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("receiveDepth" not in paramData) or ("modelType" not in paramData) or\
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("bathyfile" not in paramData) or ("sspfile" not in paramData) or\
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("z_min" not in paramData) or ("z_max" not in paramData) or\
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("cp_min" not in paramData) or ("cp_max" not in paramData) or\
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("cs_min" not in paramData) or ("cs_max" not in paramData) or\
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("rho_min" not in paramData) or ("rho_max" not in paramData) or\
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("ap_min" not in paramData) or ("ap_max" not in paramData) or\
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("as_min" not in paramData) or ("as_max" not in paramData) or\
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("Cb_p" not in paramData) or ("Cb_s" not in paramData) or\
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("Rhob" not in paramData) or ("Ab_p" not in paramData) or\
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("Ab_s" not in paramData) or ("png" not in paramData) or\
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("outParam" not in paramData):
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print(f'{argfile}中的参数有误，请核实！')
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return
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freq = float(paramData["freq"])
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sourceDepth = float(paramData["sourceDepth"])
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receiveDepth = float(paramData["receiveDepth"])
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modelType = paramData["modelType"]
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btyfile = paramData["bathyfile"]
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sspfile = paramData["sspfile"]
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z_min = float(paramData["z_min"])
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z_max = float(paramData["z_max"])
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cp_min = float(paramData["cp_min"])
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cp_max = float(paramData["cp_max"])
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cs_min = float(paramData["cs_min"])
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cs_max = float(paramData["cs_max"])
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rho_min = float(paramData["rho_min"])
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rho_max = float(paramData["rho_max"])
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ap_min = float(paramData["ap_min"])
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ap_max = float(paramData["ap_max"])
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as_min = float(paramData["as_min"])
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as_max = float(paramData["as_max"])
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Cb_p = float(paramData["Cb_p"])
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Cb_s = float(paramData["Cb_s"])
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Rhob = float(paramData["Rhob"])
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Ab_p = float(paramData["Ab_p"])
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Ab_s = float(paramData["Ab_s"])
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png = paramData["png"]
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outParam = paramData["outParam"]
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from scipy.interpolate import Akima1DInterpolator
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import numpy as np
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from pyat.AcousticPara import BathyM, Base, Sed, SSP_
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from pyat.tlcore import call_Bellhop, call_krakenc, call_ram
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import matplotlib.pyplot as plt
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bathym, ssp, sed, base = [], [], [], []
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with open(btyfile, 'r') as f:
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lines = f.readlines()
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if len(lines) < 1:
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print(f'{btyfile}中的参数有误，请核实！')
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return
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# 读取数据
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data = np.loadtxt(btyfile, skiprows=1, usecols=(0, 1))
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bathym = BathyM(data[:, 0], data[:, 1])
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profile_num = 0
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with open(sspfile, 'r') as f:
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try:
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line = f.readline()
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profile_num = int((line.split())[0])
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while line:
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line = f.readline()
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if '距离' in line:
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ranges, depths, vels = [], [], []
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line = f.readline()
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while line and ('分隔符' not in line):
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ranges.append(1)
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depths.append(float((line.split())[0]))
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vels.append(float((line.split())[1]))
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line = f.readline()
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ssp.append(SSP_(ranges, depths, vels))
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except Exception as err:
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print(f'{sspfile}中的参数有误，请核实！')
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print(err)
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return
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"""akima 0829"""
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z_ssp = np.array([])
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for i in range(len(ssp)):
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z_ssp = np.append(z_ssp, ssp[i].z)
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z_ssp = np.unique(z_ssp)
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z_ssp = np.sort(z_ssp)
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for i in range(len(ssp)):
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dep = ssp[i].z
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vel = ssp[i].c
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# 对剖面进行垂向插值处理
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depInterp = z_ssp[z_ssp<=ssp[i].z[-1]]
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# 应用Akima插值算法，对ssp进行插值
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func = Akima1DInterpolator(dep, vel)
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velInterp = func(depInterp)
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# 剖面赋值
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ssp[i].z = depInterp
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ssp[i].c = velInterp
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len_real = len(depInterp)-1
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#for it in range(len_real,int(deep_max)):
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# ssp[i].c = np.append(ssp[i].c,0)
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# Sediment
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z = np.array([z_min, z_max])
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cp = np.array([cp_min, cp_max])
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cs = np.array([cs_min, cs_max])
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rho = np.array([rho_min, rho_max])
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a_p = np.array([ap_min, ap_max])
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a_s = np.array([as_min, as_max])
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for it in range(profile_num):
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sed.append(Sed(z, cp, cs, rho, a_p, a_s))
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# Base
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base = []
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for it in range(profile_num):
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base.append(Base(Cb_p, Cb_s, Rhob, Ab_p, Ab_s))
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ranges, depths, TL = [], [], []
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if modelType == 'bellhop':
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sd = np.array([sourceDepth]) # Source depth
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rd = np.linspace(0, receiveDepth, int(receiveDepth / 10)) # Receiving depth
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[Pos, TL] = call_Bellhop(freq, sd, rd, bathym, ssp, sed, base)
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ranges, depths, TL = Pos.r.range, Pos.r.depth, TL[0,0,:,:]
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# Plot
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if png:
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plt.pcolor(Pos.r.range, Pos.r.depth, TL[0, 0, :, :], vmin=40, vmax=130, cmap='jet_r', shading='auto')
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plt.xlabel("Range/m")
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plt.ylabel("Depth/m")
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plt.gca().invert_yaxis()
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plt.colorbar()
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plt.show()
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print(TL[0, 0, :, :])
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elif modelType == 'kraken':
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sd = np.array([sourceDepth]) # Source depth
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rd = np.linspace(0, receiveDepth, int(receiveDepth / 10)) # Receiving depth
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[Pos, TL] = call_krakenc(freq, sd, rd, bathym, ssp, sed, base)
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ranges, depths, TL = Pos.r.range, Pos.r.depth, TL[0,0,:,:]
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# Plot
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if png:
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plt.pcolor(Pos.r.range, Pos.r.depth, TL[0, 0, :, :], vmin=40, vmax=130, cmap='jet_r', shading='auto')
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plt.xlabel("Range/m")
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plt.ylabel("Depth/m")
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plt.gca().invert_yaxis()
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plt.colorbar()
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plt.show()
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elif modelType == 'ram':
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# 调用ram模块
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[rd, rr, TL] = call_ram(freq, sourceDepth, receiveDepth, bathym, ssp, sed)
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ranges, depths, TL = rr, rd, TL
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# Plot
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if png:
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plt.pcolormesh(rr, rd, TL, vmin=60, vmax=130, cmap='jet_r', shading='auto')
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plt.xlabel("Range/m")
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plt.ylabel("Depth/m")
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plt.gca().invert_yaxis()
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plt.colorbar()
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plt.show()
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# 保存地形数据
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bathym.r = np.around(bathym.r, decimals=0)
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bathym.d = np.around(bathym.d, decimals=0)
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bathymData = {
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"param": paramData,
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"count": len(bathym.d),
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"descript": "距离、深度",
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"data": NoIndent(list(zip(bathym.r, bathym.d)))
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}
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with open(("" if outParam["outPath"]=="" else outParam["outPath"] + "/")+"tlRealtimeOut_bathym.json", 'w') as jsonFile:
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json_data = json.dumps(bathymData, cls=MyEncoder, indent=2, ensure_ascii=False)
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jsonFile.write(json_data)
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# 保存传播损失数据
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tlpoint = []
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depths = np.around(depths, decimals=0)
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ranges = np.around(ranges, decimals=0)
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TL = np.around(TL, decimals=2)
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tlpoint = list(zip(np.tile(depths.reshape(len(depths),1),len(ranges)).flatten(), np.tile(ranges,(len(depths),1)).flatten(), TL.flatten()))
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tlData = {
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"param": paramData,
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"count": len(ranges)*len(depths),
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"descript": "距离、深度、损失",
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"data": NoIndent(tlpoint)
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}
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with open(("" if outParam["outPath"]=="" else outParam["outPath"] + "/")+"tlRealtimeOut_tl.json", 'w') as jsonFile:
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json_data = json.dumps(tlData, cls=MyEncoder, indent=2, ensure_ascii=False)
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jsonFile.write(json_data)
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if __name__ == '__main__':
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main() |