add peptide plotting example

templates/05_SecB_fit_dG.py

@@ -3,17 +3,20 @@
 # %%
 from pathlib import Path
 
+import numpy as np
+import proplot as pplt
+import yaml
+
 from pyhdx.datasets import HDXDataSet
-from pyhdx.models import HDXMeasurement
-from pyhdx.fitting import fit_gibbs_global, fit_rates_weighted_average
 from pyhdx.fileIO import csv_to_dataframe, save_fitresult
+from pyhdx.fitting import fit_gibbs_global, fit_rates_weighted_average
 from pyhdx.local_cluster import default_client
-import yaml
+from pyhdx.models import HDXMeasurement
 
 # %%
 
 guess = False  # Set to True to redo initial guesses
-fit_kwargs = {"epochs": 10000, "lr": 1e4, "stop_loss": 1e-6}
+fit_kwargs = {"epochs": 200000, "lr": 1e4, "stop_loss": 1e-6}
 
 # %%
 current_dir = Path(__file__).parent
@@ -55,3 +58,36 @@
 fr_torch.to_file(output_dir / "SecB_fit_result.csv", fmt="csv")
 
 save_fitresult(output_dir / "SecB_fit", fr_torch)
+
+# %%
+# evaluate the fit, plot measured and fitted D-uptake for a few peptides
+NUM_EVAL_POINTS = 100
+all_timepoints = np.concatenate([hdxm.timepoints for hdxm in fr_torch.hdxm_set])
+
+elem = all_timepoints[np.nonzero(all_timepoints)]
+start = np.log10(elem.min())
+end = np.log10(elem.max())
+pad = (end - start) * 0.1
+time = np.logspace(start - pad, end + pad, num=NUM_EVAL_POINTS, endpoint=True)
+
+
+# %%
+# evaluate the fitted model at timepoints
+d_calc = fr_torch(time)  # Ns x Np x Nt
+d_calc
+# %%
+# choose which sample to plot
+# here we plot the first one (= SecB tetramer)
+sample_idx = 0
+hdxm = fr_torch.hdxm_set.hdxm_list[sample_idx]
+d_calc_s = d_calc[sample_idx]
+
+# %%
+# make a subplot grid to plot the first 24 peptides
+fig, axes = pplt.subplots(ncols=4, nrows=6)
+for i, ax in enumerate(axes):
+    ax.plot(time, d_calc_s[i], color="r")
+    ax.scatter(hdxm.timepoints, hdxm.d_exp.iloc[i], color="k")
+
+axes.format(xscale="log", xlabel="Time (s)", ylabel="Corrected D-uptake", ylim=(0, None))
+# %%