def quiver(self,**kwargs):
""" graphics.quiver() as a property """
graphics.quiver(self._obj,**kwargs)
def streamplot(self, **kwargs):
""" graphics.quiver(streamlines=True) """
graphics.quiver(self._obj, streamlines=True,**kwargs)
def main():
data_selectbox = st.sidebar.selectbox('Which dataset?',
('Demo', 'Jet', 'Canopy'))
streamlines = st.sidebar.checkbox('Streamlines?', value=False)
colorbar = st.sidebar.checkbox('Color bar?', value=False)
average = st.sidebar.checkbox('Average?', value=False)
st.subheader('Create data set')
"""
`data = io.create_sample_dataset()`
"""
st.subheader('Present data as Pandas DataFrame')
"""
`data.to_dataframe()`
"""
data = load_data(data_selectbox)
st.write(data.to_dataframe())
st.subheader('Plot some vector fields using matpotlib quiver')
"""
` graphics.quiver(data.isel(t=0)`
"""
# progress_bar = st.progress(0)
# status_text = st.empty()
# chart = st.line_chart(np.random.randn(10, 2))
# for i in range(len(data)):
# # Update progress bar.
# progress_bar.progress(i)
# new_rows = np.random.randn(10, 2)
# # Update status text.
# status_text.text(
# 'The latest random number is: %s' % new_rows[-1, 1])
# # Append data to the chart.
# # chart.add_rows(new_rows)
# graphics.quiver(data.isel(t=i))
# st.pyplot()
# # Pretend we're doing some computation that takes time.
# time.sleep(1)
# status_text.text('Done!')
# st.balloons()
# fig, ax = plt.subplots()
if average:
fig, ax = graphics.quiver(data.piv.average,
streamlines=streamlines,
colbar=colorbar,
colbar_orient='vertical')
else:
t = st.selectbox('Frame number', range(len(data.t)))
fig, ax = graphics.quiver(data.isel(t=t),
streamlines=streamlines,
colbar=colorbar,
colbar_orient='vertical')
the_plot = st.pyplot(plt)
def quiver(self, **kwargs):
""" graphics.quiver() as a property """
fig, ax = graphics.quiver(self._obj, **kwargs)
return fig, ax
def test_quiver():
graphics.quiver(_d)
tmp=r.values
#plt.quiver(x,y,u,v)
#print(tmp)
x,y,u,v = tmp[:,0],tmp[:,1],tmp[:,2],tmp[:,3] #(each col as a list)
#print(x)
rows = np.unique(y).shape[0]
cols = np.unique(x).shape[0]
x1 = x.reshape(rows,cols)
y1 = y.reshape(rows,cols)
u1 = u.reshape(rows,cols)
v1 = v.reshape(rows,cols)
d = io.from_arrays(x1,y1,u1,v1,np.ones_like(u1))
fig,axes=plt.subplots(figsize=(28,20))
graphics.quiver(d.isel(t=0),nthArr=3, arrScale=10) #arrScale scales arrows
d.piv.vec2scal(property='curl')
fig, ax = plt.subplots(figsize=(56,40))
graphics.contour_plot(d)
from scipy.ndimage.filters import gaussian_filter
d.piv.vorticity()
tmp2 = gaussian_filter(d.isel(t=0)['w'],0.7)
fig, ax = plt.subplots(figsize=(8,6))
levels = np.linspace(np.min(tmp),np.max(tmp), 10)
#c = ax.contourf(r.x,r.y,tmp, levels=levels,
#cmap = plt.get_cmap('RdYlBu'))
def test_quiver():
graphics.quiver(_d)
_d.piv.quiver()