У меня есть набор координат xy, которые генерируют контур.Для приведенного ниже кода эти координаты относятся к группам A и B в df.Я также создал отдельный xy cooridnate, который вызывается из C1_X и C1_Y.Однако это не используется при создании самого контура.Это отдельная координата xy.
Вопрос: Можно ли вернуть значение z контура по координате C1_X C1_Y?
Iнашли отдельный вопрос, который похож: многомерная сплайн-интерполяция в Python Scipy? .Цифра в этом вопросе отображает то, что я надеюсь вернуть, но мне просто нужно z-значение для одной координаты xy.
contour в этом вопросе нормализовано, поэтому значения находятся в пределах от -1 до 1.Я надеюсь вернуть z-значение для C1_X и C1_Y, которое является белой точкой рассеяния, показанной на рисунке под кодом.
Я попытался вернуть z-значение для этой точки, используя:
# Attempt at returning the z-value for C1
f = RectBivariateSpline(X, Y, normPDF)
z = f(d['C1_X'], d['C1_Y'])
print(z)
Но я возвращаю ошибку: raise TypeError('x must be strictly increasing')
TypeError: x must be strictly increasing
Я прокомментировалэта функция, поэтому код работает.
Примечание: этот код написан для анимации.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as sts
import matplotlib.animation as animation
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy.interpolate import RectBivariateSpline
DATA_LIMITS = [0, 15]
def datalimits(*data):
return DATA_LIMITS
def mvpdf(x, y, xlim, ylim, radius=1, velocity=0, scale=0, theta=0):
X,Y = np.meshgrid(np.linspace(*xlim), np.linspace(*ylim))
XY = np.stack([X, Y], 2)
PDF = sts.multivariate_normal([x, y]).pdf(XY)
return X, Y, PDF
def mvpdfs(xs, ys, xlim, ylim, radius=None, velocity=None, scale=None, theta=None):
PDFs = []
for i,(x,y) in enumerate(zip(xs,ys)):
X, Y, PDF = mvpdf(x, y, xlim, ylim)
PDFs.append(PDF)
return X, Y, np.sum(PDFs, axis=0)
fig, ax = plt.subplots(figsize = (10,6))
ax.set_xlim(DATA_LIMITS)
ax.set_ylim(DATA_LIMITS)
line_a, = ax.plot([], [], 'o', c='red', alpha = 0.5, markersize=5,zorder=3)
line_b, = ax.plot([], [], 'o', c='blue', alpha = 0.5, markersize=5,zorder=3)
scat = ax.scatter([], [], s=5**2,marker='o', c='white', alpha = 1,zorder=3)
lines=[line_a,line_b]
scats=[scat]
cfs = None
def plotmvs(tdf, xlim=datalimits(df['X']), ylim=datalimits(df['Y']), fig=fig, ax=ax):
global cfs
if cfs:
for tp in cfs.collections:
tp.remove()
df = tdf[1]
PDFs = []
for (group, gdf), group_line in zip(df.groupby('group'), (line_a, line_b)):
group_line.set_data(*gdf[['X','Y']].values.T)
X, Y, PDF = mvpdfs(gdf['X'].values, gdf['Y'].values, xlim, ylim)
PDFs.append(PDF)
for (group, gdf), group_line in zip(df.groupby('group'), lines+scats):
if group in ['A','B']:
group_line.set_data(*gdf[['X','Y']].values.T)
kwargs = {
'xlim': xlim,
'ylim': ylim
}
X, Y, PDF = mvpdfs(gdf['X'].values, gdf['Y'].values, **kwargs)
PDFs.append(PDF)
#plot white scatter point from C1_X, C1_Y
elif group in ['C']:
gdf['X'].values, gdf['Y'].values
scat.set_offsets(gdf[['X','Y']].values)
# normalize PDF by shifting and scaling, so that the smallest value is -1 and the largest is 1
normPDF = (PDFs[0]-PDFs[1])/max(PDFs[0].max(),PDFs[1].max())
''' Attempt at returning z-value for C1_X, C1_Y '''
''' This is the function that I am trying to write that will '''
''' return the contour value '''
#f = RectBivariateSpline(X[::-1, :], Y[::-1, :], normPDF[::-1, :])
#z = f(d['C1_X'], d['C1_Y'])
#print(z)
cfs = ax.contourf(X, Y, normPDF, cmap='jet', alpha = 1, levels=np.linspace(-1,1,10),zorder=1)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
cbar = fig.colorbar(cfs, ax=ax, cax=cax)
cbar.set_ticks([-1,-0.8,-0.6,-0.4,-0.2,0,0.2,0.4,0.6,0.8,1])
return cfs.collections + [scat] + [line_a,line_b]
''' Sample Dataframe '''
n = 1
time = range(n)
d = ({
'A1_X' : [3],
'A1_Y' : [6],
'A2_X' : [6],
'A2_Y' : [10],
'B1_X' : [12],
'B1_Y' : [2],
'B2_X' : [14],
'B2_Y' : [4],
'C1_X' : [4],
'C1_Y' : [6],
})
# a list of tuples of the form ((time, group_id, point_id, value_label), value)
tuples = [((t, k.split('_')[0][0], int(k.split('_')[0][1:]), k.split('_')[1]), v[i])
for k,v in d.items() for i,t in enumerate(time) ]
df = pd.Series(dict(tuples)).unstack(-1)
df.index.names = ['time', 'group', 'id']
#Code will eventually operate with multiple frames
interval_ms = 1000
delay_ms = 2000
ani = animation.FuncAnimation(fig, plotmvs, frames=df.groupby('time'), interval=interval_ms, repeat_delay=delay_ms,)
plt.show()
Я надеюсь вернуть zзначение для белой точки рассеяния.В предполагаемом выходе отобразится нормализованное значение z (-1,1) для C1_X, C1_Y.
При визуальном осмотре это может быть между 0.6 и 0.8
Редактировать 2:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as sts
import matplotlib.animation as animation
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy.interpolate import RectBivariateSpline
import matplotlib.transforms as transforms
DATA_LIMITS = [-85, 85]
def datalimits(*data):
return DATA_LIMITS # dmin - spad, dmax + spad
def rot(theta):
theta = np.deg2rad(theta)
return np.array([
[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]
])
def getcov(radius=1, scale=1, theta=0):
cov = np.array([
[radius*(scale + 1), 0],
[0, radius/(scale + 1)]
])
r = rot(theta)
return r @ cov @ r.T
def mvpdf(x, y, xlim, ylim, radius=1, velocity=0, scale=0, theta=0):
X,Y = np.meshgrid(np.linspace(*xlim), np.linspace(*ylim))
XY = np.stack([X, Y], 2)
x,y = rot(theta) @ (velocity/2, 0) + (x, y)
cov = getcov(radius=radius, scale=scale, theta=theta)
PDF = sts.multivariate_normal([x, y], cov).pdf(XY)
return X, Y, PDF
def mvpdfs(xs, ys, xlim, ylim, radius=None, velocity=None, scale=None, theta=None):
PDFs = []
for i,(x,y) in enumerate(zip(xs,ys)):
kwargs = {
'radius': radius[i] if radius is not None else 0.5,
'velocity': velocity[i] if velocity is not None else 0,
'scale': scale[i] if scale is not None else 0,
'theta': theta[i] if theta is not None else 0,
'xlim': xlim,
'ylim': ylim
}
X, Y, PDF = mvpdf(x, y,**kwargs)
PDFs.append(PDF)
return X, Y, np.sum(PDFs, axis=0)
fig, ax = plt.subplots(figsize = (10,6))
ax.set_xlim(DATA_LIMITS)
ax.set_ylim(DATA_LIMITS)
line_a, = ax.plot([], [], 'o', c='red', alpha = 0.5, markersize=3,zorder=3)
line_b, = ax.plot([], [], 'o', c='blue', alpha = 0.5, markersize=3,zorder=3)
lines=[line_a,line_b] ## this is iterable!
offset = lambda p: transforms.ScaledTranslation(p/82.,0, plt.gcf().dpi_scale_trans)
trans = plt.gca().transData
scat = ax.scatter([], [], s=5,marker='o', c='white', alpha = 1,zorder=3,transform=trans+offset(+2) )
scats=[scat]
cfs = None
def plotmvs(tdf, xlim=None, ylim=None, fig=fig, ax=ax):
global cfs
if cfs:
for tp in cfs.collections:
tp.remove()
df = tdf[1]
if xlim is None: xlim = datalimits(df['X'])
if ylim is None: ylim = datalimits(df['Y'])
PDFs = []
for (group, gdf), group_line in zip(df.groupby('group'), lines+scats):
if group in ['A','B']:
group_line.set_data(*gdf[['X','Y']].values.T)
kwargs = {
'radius': gdf['Radius'].values if 'Radius' in gdf else None,
'velocity': gdf['Velocity'].values if 'Velocity' in gdf else None,
'scale': gdf['Scaling'].values if 'Scaling' in gdf else None,
'theta': gdf['Rotation'].values if 'Rotation' in gdf else None,
'xlim': xlim,
'ylim': ylim
}
X, Y, PDF = mvpdfs(gdf['X'].values, gdf['Y'].values, **kwargs)
PDFs.append(PDF)
elif group in ['C']:
gdf['X'].values, gdf['Y'].values
scat.set_offsets(gdf[['X','Y']].values)
normPDF = (PDFs[0]-PDFs[1])/max(PDFs[0].max(),PDFs[1].max())
def get_contour_value_of_point(point_x, point_y, X, Y, Z, precision=10000):
CS = ax.contour(X, Y, Z, 100)
containing_levels = []
for cc, lev in zip(CS.collections, CS.levels):
for pp in cc.get_paths():
if pp.contains_point((point_x, point_y)):
containing_levels.append(lev)
if max(containing_levels) == 0:
return 0
else:
if max(containing_levels) > 0:
lev = max(containing_levels)
adj = 1. / precision
elif max(containing_levels) < 0:
lev = min(containing_levels)
adj = -1. / precision
is_inside = True
while is_inside:
CS = ax.contour(X, Y, Z, [lev])
for pp in CS.collections[0].get_paths():
if not pp.contains_point((point_x, point_y)):
is_inside = False
if is_inside:
lev += adj
return lev - adj
print(get_contour_value_of_point(d['C1_X'], d['C1_Y'], X, Y, normPDF))
cfs = ax.contourf(X, Y, normPDF, cmap='viridis', alpha = 1, levels=np.linspace(-1,1,10),zorder=1)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
cbar = fig.colorbar(cfs, ax=ax, cax=cax)
cbar.set_ticks([-1,-0.8,-0.6,-0.4,-0.2,0,0.2,0.4,0.6,0.8,1])
return cfs.collections + [scat] + [line_a,line_b]
''' Sample Dataframe '''
n = 10
time = range(n)
d = ({
'A1_X' : [3],
'A1_Y' : [6],
'A2_X' : [6],
'A2_Y' : [10],
'B1_X' : [12],
'B1_Y' : [2],
'B2_X' : [14],
'B2_Y' : [4],
'C1_X' : [4],
'C1_Y' : [6],
})
# a list of tuples of the form ((time, group_id, point_id, value_label), value)
tuples = [((t, k.split('_')[0][0], int(k.split('_')[0][1:]), k.split('_')[1]), v[i])
for k,v in d.items() for i,t in enumerate(time) ]
df = pd.Series(dict(tuples)).unstack(-1)
df.index.names = ['time', 'group', 'id']
#Code will eventually operate with multiple frames
interval_ms = 1000
delay_ms = 2000
ani = animation.FuncAnimation(fig, plotmvs, frames=df.groupby('time'), interval=interval_ms, repeat_delay=delay_ms,)
plt.show()