Main
()
| 28 | return matrix |
| 29 | |
| 30 | def main(): |
| 31 | "Main" |
| 32 | matrix = read_values("./output_landmarks.txt") |
| 33 | matrix = np.array(matrix, dtype=float) |
| 34 | z_values = matrix[:, 2] |
| 35 | print z_values |
| 36 | delta_z = 0.01 |
| 37 | min_z = -0.5 |
| 38 | max_z = 2.0 |
| 39 | array_elements = int(math.ceil((max_z - min_z) / delta_z)) |
| 40 | |
| 41 | # the histogram of the data |
| 42 | # n, bins, patches = plt.hist(z_values, bins=array_elements, density=False, |
| 43 | # facecolor='g', alpha=0.75) |
| 44 | n, bins, patches = plt.hist(z_values, bins=100, density=False, |
| 45 | facecolor='g', alpha=0.75) |
| 46 | |
| 47 | plt.xlabel('Z') |
| 48 | plt.ylabel('Number of Landmarks') |
| 49 | plt.title('Histogram of Landmarks by Z component') |
| 50 | plt.axis([min_z, max_z, min(n), max(n)]) |
| 51 | plt.grid(True) |
| 52 | plt.show() |
| 53 | |
| 54 | matrix_normals = read_values("./output_normals.txt") |
| 55 | matrix_normals = np.array(matrix_normals, dtype=float) |
| 56 | theta = normal_to_theta(matrix_normals) |
| 57 | |
| 58 | number_bins = 50 |
| 59 | n, bins, patches = plt.hist(theta, bins=number_bins, density=False, |
| 60 | facecolor='g', alpha=0.75) |
| 61 | |
| 62 | plt.xlabel('Theta[rad]') |
| 63 | plt.ylabel('Number of normals') |
| 64 | plt.title('Histogram of Normals by theta') |
| 65 | plt.axis([-math.pi/2, math.pi/2, 0, max(n)+10]) |
| 66 | plt.grid(True) |
| 67 | plt.show() |
| 68 | |
| 69 | if __name__ == "__main__": |
| 70 | main() |
no test coverage detected