updated model figures to new analysis with the right units

plotstyle.py

@@ -1,5 +1,7 @@
+import numpy as np
 import matplotlib as mpl
 import plottools.plottools as pt
+from spectral import diag_projection, peak_size
 from plottools.spines import spines_params
 from plottools.labels import labels_params
 from plottools.colors import lighter, darker
@@ -16,6 +18,66 @@ def significance_str(p):
         return '$p<0.001$'
 
 
+def noise_files(data_path, cell_name, alpha=None):
+    if alpha is None:
+        file_pattern = f'{cell_name}-chi2-split-*.npz'
+    else:
+        file_pattern = f'{cell_name}-chi2-noise-{1000*alpha:03.0f}-*.npz'
+    files = sorted(data_path.glob(file_pattern), key=lambda x: x.stem)
+    if len(files) == 0:
+        return None, 0
+    nums = [int(fn.stem.split('-')[-1]) for fn in files]
+    idxs = np.argsort(nums)
+    files = [files[i] for i in idxs]
+    nums = [nums[i] for i in idxs]
+    return files, nums
+
+
+def plot_chi2(ax, s, freqs, chi2, fcutoff, rate=None):
+    ax.set_visible(True)
+    ax.set_aspect('equal')
+    i0 = np.argmin(freqs < 0)
+    i1 = np.argmax(freqs > fcutoff)
+    if i1 == 0:
+        i1 = len(freqs)
+    freqs = freqs[i0:i1]
+    chi2 = 1e-4*chi2[i0:i1, i0:i1]  # Hz/%^2
+    vquantile = 0.996
+    vmax = np.quantile(chi2, vquantile)
+    ten = 10**np.floor(np.log10(vmax))
+    for fac, delta in zip([1, 2, 3, 4, 6, 8, 10],
+                          [0.5, 1, 1, 2, 3, 4, 5]):
+        if fac*ten >= vmax:
+            #vmax = prev_fac*ten
+            #ten *= prev_delta
+            vmax = fac*ten
+            ten *= delta
+            break
+        prev_fac = fac
+        prev_delta = delta
+    pc = ax.pcolormesh(freqs, freqs, chi2, vmin=0, vmax=vmax,
+                       rasterized=True)
+    ax.set_xlim(0, fcutoff)
+    ax.set_ylim(0, fcutoff)
+    ax.set_xticks_delta(100)
+    ax.set_yticks_delta(100)
+    ax.set_xlabel('$f_1$', 'Hz')
+    ax.set_ylabel('$f_2$', 'Hz')
+    if rate is not None:
+        dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
+        nli, nlirel, nlif = peak_size(dfreqs, diag, rate, median=False)
+        ax.text(0.95, 0.88, f'SI($r$)={nli:.1f}', ha='right', zorder=50,
+                color='white', fontsize='medium', transform=ax.transAxes)
+    cax = ax.inset_axes([1.04, 0, 0.05, 1])
+    cax.set_spines_outward('lrbt', 0)
+    cb = ax.get_figure().colorbar(pc, cax=cax)
+    cb.outline.set_color('none')
+    cb.outline.set_linewidth(0)
+    cax.set_ylabel(r'$|\chi_2|$', r'Hz/\%$^2$')
+    cax.set_yticks_delta(ten)
+    return cax
+
+
 def plot_style():
     palette = pt.palettes['muted']
     lwthick = 1.6