I applied Pandas.rolling().median(), and it has a delay or phase shift (green line).
If I use Scipy.signal.medfilt() the results are not shifted (yellow line).
Why, the results are not the same?
P.S. I've tried to look into medfilt implementation, which uses sigtools._order_filterND, which I assume is not in python.
CODE:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import medfilt
x = np.zeros(100)
x[30:70] = 2
x+= .1 * np.random.randn(100)
y1 = medfilt(x, kernel_size=5)
plt.plot(x, '.-', label='original signal');
plt.plot(y1, '.-', alpha=.5, label='medfilt');
plt.plot(pd.Series(x).rolling(5).median(), label='rolling window');
plt.legend();
Have you tried to center the window while rolling?
Pandas.rolling(center=True).median()
Related
is there a way to get TwoSlopeNorm in combination with base 2 ticks on the colorbar?
An example is something like this where you have normal linear scaling:
import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np
x = np.arange(-50,100,1)
y = x.copy()
c = x.copy()
scatter_plot = plt.scatter(x, y, c=c, cmap='bwr', norm=matplotlib.colors.TwoSlopeNorm(vmin=-50, vcenter=0, vmax=100))
cbar = plt.colorbar(scatter_plot)
plt.show()
I know based on a previous question of mine that SymLogNorm supports base2, but it looks like this is not the case for TwoSlopeNorm. Does anyone have a suggestion on how to do it?
I'm using inset_axes() to control the placement of my colorbar legend. The label hangs off the plot just a little bit. Is there a way to just nudge it over without having to do bbox_to_anchor()? Some way to do an offset from the loc parameter? I do want to keep it in the lower left.
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
set1 = ax2.scatter(df.x, df.y,
edgecolors = 'none',
c = df.recommended_net_preferred_for_analysis_meters,
norm = mcolors.LogNorm(), cmap='jet')
cbaxes = inset_axes(ax2, width="30%", height="3%", loc=3)
plt.colorbar(set1, cax=cbaxes, format = '%1.2f', orientation='horizontal')
cbaxes.xaxis.set_ticks_position("top")
I have a data set of positive values and zero's that I would like to show on the log scale. To represent zero's I use 'symlog' option, but all zero values are mapped into one point on swarmplot. How to fix it?
import numpy as np
import seaborn as sns
import pandas as pd
import random
import matplotlib.pyplot as plt
n = 100
x = np.concatenate(([0]*n,np.linspace(0,1,n),[5]*n,np.linspace(10,100,n),np.linspace(100,1000,n)),axis=None)
data = pd.DataFrame({'value': x, 'category': random.choices([0,1,2,3], k=len(x))})
f, ax = plt.subplots(figsize=(10, 6))
ax.set_yscale("symlog",linthreshy=1.e-2)
ax.set_ylim(ymax=1000)
sns.swarmplot(x="category", y="value", data=data)
sns.despine(left=True)
link to the resulting plot
I create a heatmap with the following snippet:
import numpy as np
import matplotlib.pyplot as plt
d = np.random.normal(.4,2,(10,10))
plt.imshow(d,cmap=plt.cm.RdBu)
plt.colorbar()
plt.show()
The result is plot below:
Now, since the middle point of the data is not 0, the cells in which the colormap has value 0 are not white, but rather a little reddish.
How do I force the colormap so that max=blue, min=red and 0=white?
Use a DivergingNorm.
Note: From matplotlib 3.2 onwards DivergingNorm is renamed to TwoSlopeNorm.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
d = np.random.normal(.4,2,(10,10))
norm = mcolors.DivergingNorm(vmin=d.min(), vmax = d.max(), vcenter=0)
plt.imshow(d, cmap=plt.cm.RdBu, norm=norm)
plt.colorbar()
plt.show()
A previous SO post (Change colorbar gradient in matplotlib) wanted a solution for a more complicated situation, but one of the answers talked about the MidpointNormalize subclass in the matplotlib documentation. With that, the solution becomes:
import matplotlib as mpl
import numpy as np
import matplotlib.pyplot as plt
class MidpointNormalize(mpl.colors.Normalize):
## class from the mpl docs:
# https://matplotlib.org/users/colormapnorms.html
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
super().__init__(vmin, vmax, clip)
def __call__(self, value, clip=None):
# I'm ignoring masked values and all kinds of edge cases to make a
# simple example...
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
d = np.random.normal(.4,2,(10,10))
plt.imshow(d,cmap=plt.cm.RdBu,norm=MidpointNormalize(midpoint=0))
plt.colorbar()
plt.show()
Kudos to Joe Kington for writing the subclass, and to Rutger Kassies for pointing out the answer.
I ran the following code to get two plots next to each other (it is a minimal working example that you can copy):
import pandas as pd
import numpy as np
from matplotlib.pylab import plt
comp1 = np.random.normal(0,1,size=200)
values = pd.Series(comp1)
plt.close("all")
f = plt.figure()
plt.show()
sp1 = f.add_subplot(2,2,1)
values.hist(bins=100, alpha=0.5, color="r", normed=True)
sp2 = f.add_subplot(2,2,2)
values.plot(kind="kde")
Unfortunately, I then get the following image:
This is also an interesting layout, but I wanted the figures to be next to each other. What did I do wrong? How can I correct it?
For clarity, I could also use this:
import pandas as pd
import numpy as np
from matplotlib.pylab import plt
comp1 = np.random.normal(0,1,size=200)
values = pd.Series(comp1)
plt.close("all")
fig, axes = plt.subplots(2,2)
plt.show()
axes[0,0].hist(values, bins=100, alpha=0.5, color="r", normed=True) # Until here, it works. You get a half-finished correct image of what I was going for (though it is 2x2 here)
axes[0,1].plot(values, kind="kde") # This does not work
Unfortunately, in this approach axes[0,1] refers to the subplot that has a plot method but does not know kind="kde". Please take into consideration that the in the first version plot is executed on the pandas object, whereas in the second version plot is executed on the subplot, which does not work with the kind="kde" parameter.
use ax= argument to set which subplot object to plot:
import pandas as pd
import numpy as np
from matplotlib.pylab import plt
comp1 = np.random.normal(0,1,size=200)
values = pd.Series(comp1)
plt.close("all")
f = plt.figure()
sp1 = f.add_subplot(2,2,1)
values.hist(bins=100, alpha=0.5, color="r", normed=True, ax=sp1)
sp2 = f.add_subplot(2,2,2)
values.plot(kind="kde", ax=sp2)