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nicer plots

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Lukas Winkler 2019-08-20 16:17:19 +02:00
parent 41b63b3634
commit 06b4603220
Signed by: lukas
GPG key ID: 54DE4D798D244853
2 changed files with 28 additions and 16 deletions
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17
neural_network.py
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@ -69,10 +69,10 @@ else:
xrange = np.linspace(-0.5, 60.5, 100)
yrange = np.linspace(0.5, 5.5, 100)
xgrid, ygrid = np.meshgrid(xrange, yrange)
mcode = 1e23
mcode = 1e24
wpcode = 15 / 100
wtcode = 15 / 100
gammacode = 0.7
gammacode = 0.6
testinput = np.array([[np.nan, np.nan, mcode, gammacode, wtcode, wpcode]] * 100 * 100)
testinput[::, 0] = xgrid.flatten()
testinput[::, 1] = ygrid.flatten()
@ -82,8 +82,15 @@ print(testinput)
print(testinput.shape)
testoutput = model.predict(testinput)
outgrid = np.reshape(testoutput, (100, 100))
print("minmax")
print(np.nanmin(outgrid), np.nanmax(outgrid))
plt.pcolormesh(xgrid, ygrid, outgrid, cmap="Blues", vmin=0, vmax=1)
plt.colorbar()
plt.savefig("keras.png", transparent=True)
plt.imshow(outgrid, interpolation='none', cmap="Blues", aspect="auto", origin="lower", vmin=0, vmax=1,
extent=[xgrid.min(), xgrid.max(), ygrid.min(), ygrid.max()])
plt.colorbar().set_label("water retention fraction")
plt.xlabel("impact angle $\\alpha$ [$^{\circ}$]")
plt.ylabel("velocity $v$ [$v_{esc}$]")
plt.tight_layout()
plt.savefig("../arbeit/images/plots/nn2.pdf")
plt.show()

27
visualize.py
View file

@ -1,14 +1,13 @@
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import pyplot as plt, cm
from CustomScaler import CustomScaler
from interpolators.griddata import GriddataInterpolator
from interpolators.rbf import RbfInterpolator
from simulation_list import SimulationList
def main():
mcode, gamma, wt, wp = [10 ** 23, 0.6, 15 / 100, 15 / 100]
mcode, gamma, wt, wp = [10 ** 22, 0.6, 15 / 100, 15 / 100]
simlist = SimulationList.jsonlines_load()
# for s in simlist.simlist:
# if s.type!="original":
@ -25,8 +24,8 @@ def main():
scaler = CustomScaler()
scaler.fit(data)
scaled_data = scaler.transform_data(data)
interpolator = RbfInterpolator(scaled_data, values)
# interpolator = GriddataInterpolator(scaled_data, values)
# interpolator = RbfInterpolator(scaled_data, values)
interpolator = GriddataInterpolator(scaled_data, values)
alpharange = np.linspace(-0.5, 60.5, 300)
vrange = np.linspace(0.5, 5.5, 300)
@ -36,17 +35,23 @@ def main():
scaled_parameters = list(scaler.transform_parameters(parameters))
grid_result = interpolator.interpolate(*scaled_parameters)
print("minmax")
print(np.nanmin(grid_result), np.nanmax(grid_result))
plt.title("m={:3.0e}, gamma={:3.1f}, wt={:2.0f}%, wp={:2.0f}%\n".format(mcode, gamma, wt*100, wp*100))
# plt.title("m={:3.0e}, gamma={:3.1f}, wt={:2.0f}%, wp={:2.0f}%\n".format(mcode, gamma, wt*100, wp*100))
cmap = cm.Blues
cmap.set_bad('white', 1.) # show nan white
# plt.contourf(grid_alpha, grid_v, grid_result, 100, cmap="Blues", vmin=0, vmax=1)
plt.pcolormesh(grid_alpha, grid_v, grid_result, cmap="Blues", vmin=0, vmax=1)
plt.colorbar().set_label("water retention")
# plt.pcolormesh(grid_alpha, grid_v, grid_result, cmap="Blues", vmin=0, vmax=1)
plt.imshow(grid_result, interpolation='none', cmap=cmap, aspect="auto", origin="lower", vmin=0, vmax=1,
extent=[grid_alpha.min(), grid_alpha.max(), grid_v.min(), grid_v.max()])
plt.colorbar().set_label("water retention fraction")
# plt.scatter(data[:, 0], data[:, 1], c=values, cmap="Blues")
plt.xlabel("impact angle $\\alpha$")
plt.ylabel("velocity $v$")
plt.xlabel("impact angle $\\alpha$ [$^{\circ}$]")
plt.ylabel("velocity $v$ [$v_{esc}$]")
plt.tight_layout()
# plt.savefig("vis.png", transparent=True)
plt.savefig("../arbeit/images/plots/griddata1.pdf")
plt.show()