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adding a smoothing function

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This commit is contained in:
halvorhv 2022-04-27 10:37:02 +02:00
parent 092ecfa380
commit c9660109cb
1 changed files with 48 additions and 11 deletions
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59
nafuma/xanes/calib.py
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@ -2,9 +2,11 @@ import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import beamtime.auxillary as aux
import beamtime.xanes as xas
import beamtime.xanes.io as io
import nafuma.auxillary as aux
import nafuma.xanes as xas
import nafuma.xanes.io as io
from scipy.signal import savgol_filter
def rbkerbest():
print("ROSENBORG!<3")
@ -24,11 +26,10 @@ def finding_edge(df):
edge='Ni'
return(edge)
<<<<<<< HEAD:beamtime/xanes/calib.py
#def pre_edge_subtraction(df,filenames, options={}):
def test(innmat):
df_test= xas.io.put_in_dataframe(innmat)
print(df_test)
#print(df_test)
def pre_edge_subtraction(path, options={}):
required_options = ['print','troubleshoot']
@ -41,11 +42,6 @@ def pre_edge_subtraction(path, options={}):
filenames = xas.io.get_filenames(path)
df= xas.io.put_in_dataframe(path)
edge=finding_edge(df)
=======
def split_xanes_scan(filename, destination=None, replace=False):
#root is the path to the beamtime-folder
#destination should be the path to the processed data
>>>>>>> master:nafuma/xanes/calib.py
#Defining the end of the region used to define the background, thus start of the edge
#implement widget
@ -154,4 +150,45 @@ def post_edge_normalization(path, options={}):
df_postedge.plot(x="ZapEnergy", y=filenames,color="Green",ax=ax, legend=False)
plt.axvline(x = min(df_end["ZapEnergy"]))
return df_bkgd_sub, df_postedge
return df_bkgd_sub, df_postedge, filenames, edge
def smoothing(path, options={}):
required_options = ['print','window_length','polyorder']
default_options = {
'print': False,
'window_length': 3,
'polyorder': 2
}
options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
df_bkgd_sub, df_postedge, filenames, edge = post_edge_normalization(path)
#================= SMOOTHING
df_smooth = pd.DataFrame(df_bkgd_sub["ZapEnergy"])
df_default = pd.DataFrame(df_bkgd_sub["ZapEnergy"])
#df_smooth[filenames] = df_bkgd_sub.iloc[:,2].rolling(window=rolling_av).mean()
#df_smooth[filenames] = df_smooth[filenames].shift(-int((rolling_av)/2))
for filename in filenames:
x=savgol_filter(df_bkgd_sub[filename], options['window_length'],options['polyorder'])
df_smooth[filename] = x
x_default=savgol_filter(df_bkgd_sub[filename],default_options['window_length'],default_options['polyorder'])
df_default[filename] = x_default
if options['print'] == True:
fig, (ax1,ax2) = plt.subplots(1,2,figsize=(15,5))
x_range_zoom=[6.54,6.55] #make into widget
y_range_zoom=[20000,80000] #make into widget
df_bkgd_sub.plot(x = "ZapEnergy",y=filenames, ax=ax1, color="Red")
df_smooth.plot(x = "ZapEnergy",y=filenames, ax=ax1, color="Blue")
ax1.set_xlim(x_range_zoom)
ax1.set_ylim(y_range_zoom)
ax1.set_title("Smoothed curve (blue) vs data (red) used for further analysis")
df_bkgd_sub.plot(x = "ZapEnergy",y=filenames, ax=ax2, color="Red")
df_default.plot(x = "ZapEnergy",y=filenames, ax=ax2, color="Blue")
ax2.set_xlim(x_range_zoom)
ax2.set_ylim(y_range_zoom)
ax2.set_title("Smoothed curve (blue) vs data (red) using default window_length and polyorder")