def SMMsplot(x,y,allStates):
import os
os.environ['QT_QPA_PLATFORM_PLUGIN_PATH'] = 'C:/Users/maart/Anaconda3/Library/plugins/platforms'
numPmsd = 4
numPmss = 4
minLen = 10
p = np.linspace(0.5, 6, 12) # power
# Calculations for whole dataset
arDiffSw = []
arSmssSw = []
arDiffF = []
arSmssF = []
arDiffS = []
arSmssS = []
arDiffI = []
arSmssI = []
for i in range(len(x)):
if len(x[i]) > max(numPmsd, numPmss, minLen):
dif, _, smss, _ = getMSDandMSS([x[i]], [y[i]], numPmsd, numPmss, p)
if (dif >= 0) and (smss >= 0):
if list(allStates[i]).count(allStates[i][0]) != len(allStates[i]):
arDiffSw.append(dif)
arSmssSw.append(smss)
elif allStates[i][0] == 0:
arDiffF.append(dif)
arSmssF.append(smss)
elif allStates[i][0] == 1:
arDiffS.append(dif)
arSmssS.append(smss)
else:
arDiffI.append(dif)
arSmssI.append(smss)
arDiff = arDiffSw + arDiffF + arDiffS + arDiffI
arSmss = arSmssSw + arSmssF + arSmssS + arSmssI
# Calculations per state
x0, y0, x1, y1, x2, y2 = getTrackPieces(x, y, allStates)
arDiff0 = []
arSmss0 = []
for xx0, yy0 in zip(x0, y0):
if len(xx0) > max(numPmsd, numPmss, minLen):
dif0, _, smss0, _ = getMSDandMSS([xx0], [yy0], numPmsd, numPmss, p)
if dif0 >= 0 and (smss0 >= 0):
arDiff0.append(dif0)
arSmss0.append(smss0)
arDiff1 = []
arSmss1 = []
for xx1, yy1 in zip(x1, y1):
if len(xx1) > max(numPmsd, numPmss, minLen):
dif1, mss1, smss1, _ = getMSDandMSS([xx1], [yy1], numPmsd, numPmss, p)
if dif1 >= 0 and (smss1 >= 0):
arDiff1.append(dif1)
arSmss1.append(smss1)
arDiff2 = []
arSmss2 = []
for xx2, yy2 in zip(x2, y2):
if len(xx2) > max(numPmsd, numPmss, minLen):
dif2, mss2, smss2, _ = getMSDandMSS([xx2], [yy2], numPmsd, numPmss, p)
if dif2 >= 0 and (smss2 >= 0):
arDiff2.append(dif2)
arSmss2.append(smss2)
plt.style.use(['classic', 'seaborn-darkgrid'])
plt.rcParams['figure.figsize'] = (12, 6)
# Plot total (whole tracks)
data = np.column_stack((arDiff, arSmss))
df = pd.DataFrame(data, columns = [r'$D$ $\mathrm{[\mu m^2/s]}$', r'$S_{\mathrm{MSS}}$'])
g = sns.JointGrid(r'$D$ $\mathrm{[\mu m^2/s]}$',r'$S_{\mathrm{MSS}}$', data = df)
ax = g.ax_joint
ax.set_xscale('log')
g.plot_joint(plt.scatter, color = 'darkorange', alpha = 0.04, edgecolor = 'darkorange')
ax.axhspan(0.4, 0.6, facecolor = 'slateblue', edgecolor = 'none', alpha = 0.1)
ax.set_ylim(0, 1)
g.ax_marg_x.set_xscale('log')
g.ax_marg_x.hist(df[r'$D$ $\mathrm{[\mu m^2/s]}$'], color = 'darkorange', edgecolor = 'none', alpha = 0.4, \
bins = np.logspace(np.log10(min(arDiff)), np.log10(max(arDiff)), 50))
g.ax_marg_y.hist(df[r'$S_{\mathrm{MSS}}$'], color = 'darkorange', edgecolor = 'none', alpha = 0.4, \
orientation = 'horizontal', bins = np.linspace(min(arSmss), max(arSmss), 50))
g.ax_marg_y.axhspan(0.4, 0.6, facecolor = 'slateblue', edgecolor = 'none', alpha = 0.1)
orange_patch = mpatches.Patch(color = 'darkorange', alpha = 0.4, label = 'Total')
ax.legend(handles = [orange_patch], loc = 'upper left')
plt.xlabel(r'$D$ $\mathrm{[\mu m^2/s]}$', fontdict = font, size = 'large')
plt.ylabel(r'$S_{\mathrm{MSS}}$', fontdict = font, size = 'large')
# Plot per trackstate (whole tracks only fast / only slow / only immobile / switching)
dataSw = np.column_stack((arDiffSw, arSmssSw))
dfSw = pd.DataFrame(dataSw, columns = [r'$D$ $\mathrm{[\mu m^2/s]}$', r'$S_{\mathrm{MSS}}$'])
gSw = sns.JointGrid(r'$D$ $\mathrm{[\mu m^2/s]}$', r'$S_{\mathrm{MSS}}$', data = dfSw)
axSw = gSw.ax_joint
axSw.set_xscale('log')
gSw.plot_joint(plt.scatter, color = 'green', alpha = 0.04, edgecolor = 'green')
axSw.scatter(arDiffF, arSmssF, color = 'r', alpha = 0.04, edgecolor = 'r')
axSw.scatter(arDiffS, arSmssS, color = 'royalblue', alpha = 0.04, edgecolor = 'royalblue')
axSw.scatter(arDiffI, arSmssI, color = 'darkblue', alpha = 0.04, edgecolor = 'darkblue')
axSw.axhspan(0.4, 0.6, facecolor = 'slateblue', edgecolor = 'none', alpha = 0.1)
axSw.set_ylim(0, 1)
gSw.ax_marg_x.set_xscale('log')
gSw.ax_marg_x.hist(dfSw[r'$D$ $\mathrm{[\mu m^2/s]}$'], color = 'green', edgecolor = 'none', alpha = 0.4, \
bins = np.logspace(np.log10(min(arDiffSw)), np.log10(max(arDiffSw)), 50))
gSw.ax_marg_x.hist(arDiffF, color = 'red', edgecolor = 'none', alpha = 0.4, \
bins = np.logspace(np.log10(min(arDiffF)), np.log10(max(arDiffF)), 50))
gSw.ax_marg_x.hist(arDiffS, color = 'royalblue', edgecolor = 'none', alpha = 0.4, \
bins = np.logspace(np.log10(min(arDiffS)), np.log10(max(arDiffS)), 50))
gSw.ax_marg_x.hist(arDiffI, color = 'darkblue', edgecolor = 'none', alpha = 0.4, \
bins = np.logspace(np.log10(min(arDiffI)), np.log10(max(arDiffI)), 50))
gSw.ax_marg_y.hist(dfSw[r'$S_{\mathrm{MSS}}$'], color = 'green', edgecolor = 'none', alpha = 0.4, \
orientation = 'horizontal', bins = np.linspace(min(arSmssSw), max(arSmssSw), 50))
gSw.ax_marg_y.hist(arSmssF, color = 'red', edgecolor = 'none', alpha = 0.4, \
orientation = 'horizontal', bins = np.linspace(min(arSmssF), max(arSmssF), 50))
gSw.ax_marg_y.hist(arSmssS, color = 'royalblue', edgecolor = 'none', alpha = 0.4, \
orientation = 'horizontal', bins = np.linspace(min(arSmssS), max(arSmssS), 50))
gSw.ax_marg_y.hist(arSmssI, color = 'darkblue', edgecolor = 'none', alpha = 0.4, \
orientation = 'horizontal', bins = np.linspace(min(arSmssI), max(arSmssI), 50))
gSw.ax_marg_y.axhspan(0.4, 0.6, facecolor = 'slateblue', edgecolor = 'none', alpha = 0.1)
red_patch = mpatches.Patch(color = 'red', alpha = 0.4, label = 'Only fast')
royalblue_patch = mpatches.Patch(color = 'royalblue', alpha = 0.4, label = 'Only slow')
darkblue_patch = mpatches.Patch(color = 'darkblue', alpha = 0.4, label = 'Only immobile')
green_patch = mpatches.Patch(color = 'green', alpha = 0.4, label = 'Switching')
axSw.legend(handles = [red_patch, royalblue_patch, darkblue_patch, green_patch], loc = 'upper left')
plt.xlabel(r'D $\mathrm{(\mu m^2/sec)}$', fontdict = font, size = 'large')
plt.ylabel(r'$S_{\mathrm{MSS}}$', fontdict = font, size = 'large')
# Plot split (trackpieces fast / slow / immobile)
data0 = np.column_stack((arDiff0, arSmss0))
df0 = pd.DataFrame(data0, columns = [r'$D$ $\mathrm{[\mu m^2/s]}$', r'$S_{\mathrm{MSS}}$'])
f = sns.JointGrid(r'$D$ $\mathrm{[\mu m^2/s]}$', r'$S_{\mathrm{MSS}}$', data = df0)
ax0 = f.ax_joint
ax0.set_xscale('log')
f.plot_joint(plt.scatter, color = 'r', alpha = 0.1, edgecolor = 'r')
ax0.scatter(arDiff1, arSmss1, color = 'royalblue', alpha = 0.1, edgecolor = 'royalblue')
ax0.scatter(arDiff2, arSmss2, color = 'darkblue', alpha = 0.1, edgecolor = 'darkblue')
ax0.axhspan(0.4, 0.6, facecolor = 'slateblue', edgecolor = 'none', alpha = 0.1)
ax0.set_ylim(0, 1)
f.ax_marg_x.set_xscale('log')
f.ax_marg_x.hist(df0[r'$D$ $\mathrm{[\mu m^2/s]}$'], color = 'red', edgecolor = 'none', alpha = 0.4, \
bins = np.logspace(np.log10(min(arDiff0)), np.log10(max(arDiff0)), 50))
f.ax_marg_x.hist(arDiff1, color = 'royalblue', edgecolor = 'none', alpha = 0.4, \
bins = np.logspace(np.log10(min(arDiff1)), np.log10(max(arDiff1)), 50))
f.ax_marg_x.hist(arDiff2, color = 'darkblue', edgecolor = 'none', alpha = 0.4, \
bins = np.logspace(np.log10(min(arDiff2)), np.log10(max(arDiff2)), 50))
f.ax_marg_y.hist(df0[r'$S_{\mathrm{MSS}}$'], color = 'red', edgecolor = 'none', alpha = 0.4, \
orientation = 'horizontal', bins = np.linspace(min(arSmss0), max(arSmss0), 50))
f.ax_marg_y.hist(arSmss1, color = 'royalblue', edgecolor = 'none', alpha = 0.4, orientation = 'horizontal', \
bins = np.linspace(min(arSmss1), max(arSmss1), 50))
f.ax_marg_y.hist(arSmss2, color = 'darkblue', edgecolor = 'none', alpha = 0.4, orientation = 'horizontal', \
bins = np.linspace(min(arSmss2), max(arSmss2), 50))
f.ax_marg_y.axhspan(0.4, 0.6, facecolor = 'slateblue', edgecolor = 'none', alpha = 0.1)
red_patch = mpatches.Patch(color = 'red', alpha = 0.4, label = 'Fast tracklets')
royalblue_patch = mpatches.Patch(color = 'royalblue', alpha = 0.4, label = 'Slow tracklets')
darkblue_patch = mpatches.Patch(color = 'darkblue', alpha = 0.4, label = 'Immobile tracklets')
ax0.legend(handles = [red_patch, royalblue_patch, darkblue_patch], loc = 'upper left')
plt.xlabel(r'$D$ $\mathrm{[\mu m^2/s]}$', fontdict = font, size = 'large')
plt.ylabel(r'$S_{\mathrm{MSS}}$', fontdict = font, size = 'large')
return plt
#plt.show()
