Updates figures files and fixes a minor typo in README

README.md

@@ -21,7 +21,6 @@ pip install -r requirements.txt
 
 ### Run code
 ```
-python tmain.py
 python -i src/distribution_tools/tmain.py
 ```
 

src/distribution_tools/model_imu_data.py

@@ -21,6 +21,7 @@ print(result.keys()) # let's check what objects we got
 
 stats.probplot(numpy_data, dist="norm", plot=plt)
 plt.show()
+plt.title('Probability Plot for IMU Dataset')
 
 
 realization = numpy_data
@@ -30,9 +31,9 @@ df = 1000
 skew = 0
 
 theta = np.array([loc, scale, df, skew])
-#
-# res = minimize(getObjectiveFunction(realization, use_loglikelihood=True), x0=theta,
-#                method='Nelder-Mead')
+
+res = minimize(getObjectiveFunction(realization, use_loglikelihood=True), x0=theta,
+               method='Nelder-Mead')
 
 N = 1_000
 xmin = -0.05
@@ -40,16 +41,12 @@ xmax = 0.05
 extent =  xmax - xmin
 xvals = np.linspace(xmin - 0.1 * extent, xmax + 0.1 * extent, N)
 
-# plt.figure()
-# est_pdf = tspdf_1d(xvals, res.x[0], res.x[1], res.x[2], res.x[3])
-# plt.hist(realization, bins=500, density=True, color='green', alpha=0.5)
-# plt.plot(xvals, est_pdf, linestyle='--', label='Estimated skew t', linewidth=3, alpha=0.5)
-# plt.xlim([xmin, xmax])
-# plt.legend()
+plt.figure()
+est_pdf = tspdf_1d(xvals, res.x[0], res.x[1], res.x[2], res.x[3])
+plt.hist(realization, bins=500, density=True, color='green', alpha=0.5)
+plt.plot(xvals, est_pdf, linestyle='--', label='Estimated skew t', linewidth=3, alpha=0.5)
+plt.xlim([xmin, xmax])
+plt.title(r'IMU Data Modeled Using Skew-$t$ Distribution')
+plt.legend()
 
-loc = 2.72e-5
-scale = 2.25e-6
-df = 1
-skew = 2.8e-3
 
-# median = ts_invcdf(np.array([0.25, 0.5, 0.75]), loc, scale, df, skew)
\ No newline at end of file

src/distribution_tools/model_sift_error.py

+import numpy as np
+import matplotlib.pyplot as plt; plt.ion()
+import scipy.stats as stats
+
+from scipy.optimize import minimize
+from tskew.tskew import getObjectiveFunction, tspdf_1d
+
+import pyreadr
+
+esti_delta_r = np.load('../../data/deltar_sift.npy')
+true_delta_r = np.load('../../data/deltar_tru_sift.npy')
+
+error = esti_delta_r - true_delta_r
+
+fig, axs = plt.subplots(1, 2, sharex=True)
+fig.suptitle('True and estimated frame-to-frame displacements')
+
+axs[0].plot(true_delta_r, label=r'True $\Delta r$', linewidth=5)
+axs[0].plot(esti_delta_r,linestyle='--', label=r'Est. $\Delta r$', linewidth=5)
+axs[0].set_xlabel('Frame index')
+axs[0].set_ylabel('Distance (m)')
+axs[0].set_title('Overlayed')
+axs[0].legend()
+
+axs[1].plot(error)
+axs[1].set_xlabel('Frame index')
+axs[1].set_ylabel('Error (m)')
+axs[1].set_title('Difference')
+
+plt.figure()
+stats.probplot(error, dist="norm", plot=plt)
+plt.title('Probability Plot for Visual Odometry Errors')
+plt.show()
+
+
+realization = error
+loc = np.mean(realization)
+scale = np.var(realization)
+df = 10
+skew = 0
+
+theta = np.array([loc, scale, df, skew])
+
+res = minimize(getObjectiveFunction(realization, use_loglikelihood=True), x0=theta,
+               method='Nelder-Mead')
+
+N = 1_000
+xmin = np.min(realization)
+xmax = np.max(realization)
+extent =  xmax - xmin
+xvals = np.linspace(xmin - 0.1 * extent, xmax + 0.1 * extent, N)
+
+plt.figure()
+est_pdf = tspdf_1d(xvals, res.x[0], res.x[1], res.x[2], res.x[3])
+plt.hist(realization, bins=100, density=True, color='green', alpha=0.5)
+plt.plot(xvals, est_pdf, linestyle='--', label='Estimated skew t', linewidth=3, alpha=0.5)
+plt.xlim([xmin, xmax])
+plt.title(r'Modeling Visual Odometry Errors using Skew-$t$ Distribution')
+plt.legend()
+
+
+