chore: clean up template file

templates/01_load_secb_data.py

@@ -1,10 +1,12 @@
 """Load a HDX-MS dataset (peptide list with D-uptake per peptide, csv format)"""
 # %%
 from pathlib import Path
+
 import numpy as np
-from pyhdx import read_dynamx, HDXMeasurement
-from pyhdx.process import apply_control, correct_d_uptake
+
+from pyhdx import HDXMeasurement, read_dynamx
 from pyhdx.datasets import filter_peptides
+from pyhdx.process import apply_control, correct_d_uptake
 
 # %%
 
@@ -41,155 +43,3 @@
 
 # Create HDX Measurement object with addtional experimental metadata (sequence, pH, temperature)
 hdxm = HDXMeasurement(peptides_corrected, sequence=sequence, pH=pH, temperature=temperature)
-
-#%%
-data = hdxm.data
-data['id'] = data['start'].apply(str) + '_' + data['end'].apply(str)
-peptides = data['id'].unique()
-peptides
-#%%
-
-peptide = peptides[15]
-peptide = '118_125'
-
-df_f = data[data['id'] == peptide]
-row_0 = df_f.iloc[0]
-start, end = row_0['start'], row_0['end']
-start, end
-
-df_f
-
-#%%
-from hdxrate import k_int_from_sequence
-seq = df_f['_sequence'].iloc[0]
-
-k_int_from_sequence(seq, temperature, pH, d_percentage=90)
-
-exposure = np.array(df_f['exposure'])[1:]
-upt = np.array(df_f['uptake_corrected'])[1:]
-
-if exposure[0] != 0:
-    exposure = np.insert(exposure, 0, 0)
-    upt = np.insert(upt, 0, 0)
-
-exposure, upt
-
-#%%
-import proplot as pplt
-fig, ax = pplt.subplots()
-ax.scatter(exposure, upt)
-ax.format(xscale='log')
-pplt.show()
-
-#%%
-peptide_info = hdxm.coverage.protein.loc[start:end]
-k_int = peptide_info['k_int'][peptide_info['exchanges']]
-k_arr = np.array(k_int)
-
-k_eff = np.exp(np.log(k_arr).mean())
-
-k_int = np.array(peptide_info['k_int'])
-k_int
-
-# TODO i need the inverse of this function to find the time difference; then take the log of it
-d0 = 1 - np.exp(np.divide(-k_int[:, np.newaxis]*exposure[np.newaxis, :], 2))
-d0
-
-#%%
-np.log10(10)
-
-#%%
-
-np.log(2.72)
-
-#%%
-
-hdxm.metadata
-
-#%%
-
-data_wide = data.pivot(index='id', columns='exposure', values='uptake_corrected')
-
-max_d = data[data['id'] == peptide]['ex_residues'].iloc[0]
-
-import proplot as pplt
-
-t_eval = np.linspace(0, 6000*10, 100, endpoint=True)
-k = 1e-3
-d_eval = max_d * (1 - np.exp(-k*t_eval))
-#%%
-
-from scipy.interpolate import PchipInterpolator
-
-s = data_wide.loc[peptide]
-x = np.array(s.index)
-y = np.array(s)
-
-x = np.append(x, x[-1]*5)
-y = np.append(y, y[-1])
-
-from scipy.interpolate import make_interp_spline
-
-bspl = make_interp_spline(x, y, k=2)
-
-
-#%%
-# parameterized spline
-p = np.stack((x, y))
-
-u_unif = x
-
-dp = p[:, 1:] - p[:, :-1]      # 2-vector distances between points
-l = (dp**2).sum(axis=0)        # squares of lengths of 2-vectors between points
-u_cord = np.sqrt(l).cumsum()   # cumulative sums of 2-norms
-u_cord = np.r_[0, u_cord]      # the first point is parameterized at zero
-
-u_c = np.r_[0, np.cumsum((dp**2).sum(axis=0)**0.25)]
-
-from scipy.interpolate import make_interp_spline
-import matplotlib.pyplot as plt
-
-fig, ax = plt.subplots(1, 3, figsize=(8, 3))
-parametrizations = ['uniform', 'cord length', 'centripetal']
-for j, u in enumerate([u_unif, u_cord, u_c]):
-   spl = make_interp_spline(u, p, axis=1)    # note p is a 2D array
-   uu = np.linspace(u[0], u[-1], 51)
-   xx, yy = spl(uu)
-   ax[j].plot(xx, yy, '--')
-   ax[j].plot(p[0, :], p[1, :], 'o')
-   ax[j].set_title(parametrizations[j])
-
-#plt.show()
-
-xx, uu
-#%%
-
-spl_p = make_interp_spline(u_c, p, axis=1)  
-uu = np.linspace(u_c[0], u_c[-1], 51)
-xx, yy = spl_p(uu)
-
-spl = PchipInterpolator(x, y)
-
-fig, ax = pplt.subplots()
-ax.scatter(data_wide.loc[peptide])
-ax.plot(t_eval, d_eval, color='k', ls='--')
-ax.plot(t_eval, spl(t_eval), color='g', ls='--')
-#ax.plot(t_eval, bspl(t_eval), color='b', ls='--')
-ax.plot(xx, yy, color='c', ls='--')
-# ax.axhline(max_d, color='r', ls='--')
-# ax.format(xscale='log')
-ax.format(xlim=(0, 100), ylim=(0, 20))
-
-#%%
-exposures = np.array(data_wide.columns)
-exposures
-
-#%%
-
-- np.log(1/2), np.log(2)
-
-#%%
-hdxm.data_wide.iloc[0].xs('d_uptake')
-
-#%%
-