不須啟動GUI執行PyAEDT電路模擬

 

#啟動非圖形模式
from pyaedt import Circuit
circuit = Circuit(non_graphical=True)

#輸出100級LC電路網表
with open('d:/demo/lc.net', 'w') as f:
    for i in range(100):
        f.writelines(f'L{i} net_{i} net_{i+1} 1e-9\n')
        f.writelines(f'C{i} net_{i+1} 0 5e-12\n')

#將網表加入到電路當中    
circuit.add_netlist_datablock('d:/demo/lc.net')

#加上ports
circuit.modeler.components.create_interface_port('net_0', (0,0))
circuit.modeler.components.create_interface_port('net_100', (0.01,0))

#加上模擬設定並執行模擬
lna = circuit.create_setup('mylna', circuit.SETUPS.NexximLNA)
lna.props['SweepDefinition']['Data']= 'LINC 0Hz 1GHz 101'
circuit.analyse_nominal()

#取值並繪圖
data = circuit.post.get_solution_data('db(Z11)')
data.plot(math_formula='re')

#釋放license
circuit.release_desktop()

生成帶有約束條件之線性規劃曲面

 

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d

fig = plt.figure(figsize=(8,6))
ax3d = plt.axes(projection="3d")

def func(x, y):
    result = (3*x+4*y)
    return result

constraint = lambda x, y: (x+y<5)*(x+y>1)

xdata = np.linspace(0,3,100)
ydata = np.linspace(0,3,100)
X,Y = np.meshgrid(xdata,ydata)
Z = func(X, Y)/constraint(X, Y)


ax3d = plt.axes(projection='3d')
ax3d.plot_surface(X, Y, Z,cmap='jet')
ax3d.set_title('Surface Plot in Matplotlib')
ax3d.set_xlabel('X')
ax3d.set_ylabel('Y')
ax3d.set_zlabel('Z')

plt.show()

建立參數化複雜結構

參數化之複雜結構過往都要透過UDP來編寫，困難度很高。PyAEDT可以用幾行程式碼達到要求。以下提供四個範例給各位參考。

from pyaedt import Hfss

hfss = Hfss(specified_version='2022.1', designname='case1')

hfss['Nx'] = '4'
hfss['wx'] = '1mm'
hfss['wy'] = '10mm'
u = 1

models = []
for i in range(100):
    x = hfss.modeler.create_rectangle(2,
                                      [f'if(Nx>{i}, {u}*wx, wx)', '0mm', '0mm'],
                                      [f'if(Nx>{i}, {i+1}*wx, wx)', 'wy'],
                                      name='rect')
    x.color = (255, 0, 0)
    u += i + 2
    models.append(x)

hfss.modeler.unite(models)

# %%

from pyaedt import Hfss

hfss = Hfss(specified_version='2022.1', designname='case2')

hfss['Nr'] = 4
hfss['dr'] = '1mm'
hfss['dz'] = '1mm'
hfss['ratio'] = 0.7

rings = []
for i in range(0, 20):
    c1 = hfss.modeler.create_circle(2, (0, 0, f'if({i}<Nr, {i}*dz, 0)'), f'if({i}<Nr, {2*i+1}*dr, 1*dr)',
                                    is_covered=False, name=f'c_{2*i}')
    c2 = hfss.modeler.create_circle(2, (0, 0, f'if({i}<Nr, ({i}+ratio)*dz, ratio*dz)'), f'if({i}<Nr, {2*i+2}*dr, 2*dr)',
                                    is_covered=False, name=f'c_{2*i+1}')
    ring = hfss.modeler.connect([c1, c2])

sheets = hfss.modeler.get_objects_in_group('Sheets')
hfss.modeler.unite(sheets)

# %%

hfss = Hfss(specified_version='2022.1', designname='case3')
N = 4

hfss['dx'] = '4mm'
hfss['dy'] = '3mm'
hfss['dz1'] = '0.2mm'
hfss['dz2'] = '0.1mm'
hfss['configuration'] = [1] * (N ** 2)

create_rectangle = hfss.modeler.create_rectangle
keys = [(i, j) for i in range(N) for j in range(N)]

x = create_rectangle(2, ('0mm', '0mm', '0mm'), (f'{N}*dx', f'{N}*dy'), name='m0')
hfss.modeler.thicken_sheet(x, 'dz2')
for n, (nx, ny) in enumerate(keys):
    x = create_rectangle(2,
                         (f'if(configuration[{n}]==1, {nx}*dx, 0)', f'if(configuration[{n}]==1, {ny}*dy, 0)',
                          f'if(configuration[{n}]==1, 1mm, 0mm)'),
                         (f'if(configuration[{n}]==1, dx, {N}*dx)', f'if(configuration[{n}]==1, dy, {N}*dy)'),
                         name=f'mp{nx}_{ny}_0')
    hfss.modeler.thicken_sheet(x, f'if(configuration[{n}]==1, dz1, dz2)')

# %%
from pyaedt import Hfss

hfss = Hfss(specified_version='2022.1', designname='case4')

hfss['Nr'] = 6
hfss['R0'] = '100um'
hfss['W0'] = '20um'
hfss['Pitch'] = '40um'
hfss['Nn'] = 4
hfss['T0'] = '5um'

locations = []
for i in range(200):
    locations.append(
        (f'if({i}<Nr*Nn, (R0+(Pitch/cos(pi/Nr))*{i}/Nr)*cos(2*pi*{i}/Nr), (R0+(Pitch/cos(pi/Nr))*Nn)*cos(2*pi*Nn))',
         f'if({i}<Nr*Nn, (R0+(Pitch/cos(pi/Nr))*{i}/Nr)*sin(2*pi*{i}/Nr), (R0+(Pitch/cos(pi/Nr))*Nn)*sin(2*pi*Nn))',
         '0mm'))

hfss.modeler.create_polyline(locations, xsection_type='Line', xsection_width='W0', name='spiral')
hfss.modeler.thicken_sheet('spiral', 'T0')

如何列出特定目錄底下所有.aedt專案與專案包含的設計名稱

當累積的專案數量很多時，想要查找某個設計是放在哪一個.aedt檔當中相當花時間，用下面pyaedt腳本可以輸出所有專案與專案當中的設計名稱與設計類型(HFSS, Q3D, Circuit...)。不須開啟GUI便可完成輸出。

folder = r"D:\demo\Examples"

import os
import pyaedt
from pyaedt import Desktop

aedts = []
for dirPath, dirNames, fileNames in os.walk(folder):
    for f in fileNames:
        if f.endswith('.aedt'):
            aedts.append(os.path.join(dirPath, f))

info = {}
desktop = Desktop(specified_version='2022.1', non_graphical=True)
for n, i in enumerate(aedts):
    if os.path.isfile(i + '.lock'):
        os.remove(i + '.lock')
    print(f'{n}/{len(aedts)}:{i}')
    try:
        oproject = desktop.odesktop.OpenProject(i)
        designs = oproject.GetChildNames()
        types = [oproject.GetChildObject(j).GetDesignType() for j in designs]
        info[i] = list(zip(designs, types))
        oproject.Close()
    except:
        pass
desktop.close_desktop()

# %%
design_map = {'HFSS': pyaedt.Hfss,
              'HFSS 3D Layout Design': pyaedt.Hfss3dLayout,
              'Q3D Extractor': pyaedt.Q3d,
              '2D Extractor': pyaedt.Q2d,
              'Circuit Design': pyaedt.Circuit,
              'Maxwell 2D': pyaedt.Maxwell2d,
              'Maxwell 3D': pyaedt.Maxwell3d,
              'Icepak': pyaedt.Icepak,
              'Twin Builder': pyaedt.TwinBuilder,
              'Mechanical': pyaedt.Mechanical,
              'EMIT': pyaedt.Emit,
              'RMxprt': pyaedt.Rmxprt
              }

k = 0
result = {}
for i, design_info in info.items():
    result[i] = []
    for m, n in design_info:
        try:
            app = design_map[n](i, m, specified_version='2022.1')
            image_path = os.path.abspath(f'{k}.jpg')
            app.export_design_preview_to_jpg(image_path)
            k += 1
            app.close_project()

            result[i].append((m, n, image_path))
        except:
            raise

# %%

css = '''
<style>
* {
  box-sizing: border-box;
}

/* Create three equal columns that floats next to each other */
.column {
  float: left;
  width: 25%;
  padding: 10px;
  height: 300px; /* Should be removed. Only for demonstration */
}

/* Clear floats after the columns */
.row:after {
  content: "";
  display: table;
  clear: both;
}
</style>
'''

with open('summary.html', 'w') as f:
    f.write(css)
    for aedt_path, data in result.items():
        f.write(f'<H2 style="color:blue;">{aedt_path}</H2>\n')
        f.write('<div class="row">\n')
        for design_name, design_type, image in data:
            f.write('<div class="column">\n')
            f.write(f'<H3>{design_name} ({design_type})</H3>\n')
            f.write(f'<img src="{image}" width="200">\n')
            f.write('</div>\n')
        f.write('</div>\n')
os.system('summary.html')
print(result)

如何在AEDT比較不同專案設計的S參數

在AEDT比較不同專案設計的S參數

prj_design = [('bp_filter', 'HFSSDesign1', 'S(2,1)'), 
              ('OptimTee', 'TeeModel', 'S(1,1)') ]


#%%
from pyaedt import Hfss
import matplotlib.pyplot as plt

color = ['r', 'b', 'g', 'y', 'm', 'c']

cases = []
for prj, design, quantity in prj_design:
    cases.append((Hfss(projectname=prj, designname=design, specified_version='2022.1',), quantity))

#%%

fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})
for (i, q), c in zip(cases, color):
    result = i.post.get_report_data(f'polar({q})')
    ax.plot(result.data_imag(), result.data_real(), color=c)

fig.show()

#%%
fig, ax = plt.subplots()
plt.grid()
for (i, q), c in zip(cases, color):
    result = i.post.get_report_data(f'db({q})')
    ax.plot(result.sweeps['Freq'], result.data_real(), color=c)

fig.show()

#%%
fig, ax = plt.subplots()
plt.grid()
for (i, q), c in zip(cases, color):
    result = i.post.get_report_data(f'ang_deg({q})')
    ax.plot(result.sweeps['Freq'], result.data_real(), color=c)

fig.show()

如何輸出HFSS近場資料

import numpy as np
import matplotlib.pyplot as plt
from pyaedt import Hfss
hfss = Hfss(specified_version='2022.1')

data = hfss.post.get_solution_data_per_variation('Near Fields', 'Setup1 : LastAdaptive', "Rectangle1", expression='NearEX')
print(data.units_data)

x_real = data.solutions_data_real['NearEX'].values()
x_imag = data.solutions_data_imag['NearEX'].values()
data.sweeps['_u']
NearEX = [complex(i, j) for i, j in zip(x_real, x_imag)]

NearEX = np.array(NearEX)
x = np.reshape(np.absolute(NearEX), (len(data.sweeps['_u']), len(data.sweeps['_v'])))

u_min, u_max = min(data.sweeps['_u'])*1000, max(data.sweeps['_u'])*1000
v_min, v_max = min(data.sweeps['_v']), max(data.sweeps['_v'])

plt.xlabel('u')
plt.ylabel('v')
plt.imshow(x, cmap='jet', extent = [u_min, u_max, v_min, v_max])

(圖一) Python輸出近場

(圖二) HFSS近場

如何輸出HFSS遠場資料

以下方法可以用在HFSS任何遠場物理量輸出，使用np.ndarray可以加快矩陣運算速度。

import numpy as np
import matplotlib.pyplot as plt
from pyaedt import Hfss
hfss = Hfss(specified_version='2022.1')

data = hfss.post.get_far_field_data(['rETheta', 'rEPhi'], 
                                    'Setup1 : LastAdaptive', 
                                    '3D')
print(data.units_data)

x_real = data.solutions_data_real['rETheta']
x_imag = data.solutions_data_imag['rETheta']
rETheta = [complex(x_real[i], x_imag[i]) for i in x_real]


x_real = data.solutions_data_real['rEPhi']
x_imag = data.solutions_data_imag['rEPhi']
rEPhi = [complex(x_real[i], x_imag[i]) for i in x_real]

rEPhi = np.array(rEPhi)
x = np.reshape(np.absolute(rEPhi), (361, 181))

plt.xlabel('Phi')
plt.ylabel('Theta')
plt.imshow(x.T, cmap='jet')

(圖一) Python輸出

(圖二)HFSS輸出