import pandas as pd
pd.read_csv('C:\Users\Lucya\OneDrive\Escritorio')
import matplotlib.pyplot as plt
# Your data
datos_fila1 = [0.73, 0.763, 0.782, 0.889, 0.815, 0.924, 0.875, 0.918, 0.956, 0.941, 1.058, 1.006, 1.006, 1.136, 1.053, 1.195, 1.1, 1.133, 1.15, 1.166, 1.31, 1.212, 1.231, 1.249, 1.301, 1.44, 1.33, 1.381, 1.367, 1.395, 1.527, 1.442, 1.493, 1.487, 1.493, 1.656, 1.568, 1.606, 1.602, 1.629, 1.701, 1.685, 1.718, 1.697, 1.76, 1.881, 1.793, 1.793, 1.823, 1.868, 1.963, 1.91, 1.962, 1.941, 1.99, 2.084, 2.018, 2.069, 2.061, 2.097, 2.233, 2.149, 2.177, 2.17, 2.205, 2.312, 2.271, 2.289, 2.288, 2.327, 2.419, 2.374, 2.411, 2.388, 2.458, 2.481, 2.5, 2.523, 2.537, 2.565, 2.654, 2.565, 2.594, 2.553, 2.594, 2.643, 2.584, 2.58, 2.553, 2.57, 2.643, 2.598, 2.598, 2.569, 2.598, 2.598, 2.589, 2.608, 2.571, 2.612, 2.692, 2.603, 2.58, 2.579, 2.617, 2.603, 2.603, 2.598, 2.617, 2.622, 2.702, 2.634, 2.681, 2.639, 2.657, 2.757, 2.675, 2.669, 2.658, 2.704, 2.769, 2.687, 2.722, 2.746, 2.734, 2.758, 2.734, 2.74, 2.758, 2.781, 2.819, 2.781, 2.799, 2.799, 2.828, 2.834, 2.817, 2.864, 2.876, 2.87, 2.882, 2.876, 2.923, 2.929, 2.929, 2.946, 2.905, 2.911, 2.917, 2.923, 2.935, 2.917, 2.946, 2.901, 2.929, 2.964, 2.988, 3.017, 2.945, 3.005, 2.988, 2.994, 3.017, 3.035, 3.047, 3.065, 3.023, 3.059, 3.013, 3.106, 3.116, 3.118, 3.124, 3.155, 3.059, 3.181, 3.082, 3.053, 3.129, 3.124, 3.219, 3.194, 3.159, 3.129, 3.147, 3.194, 3.183, 3.171, 3.194, 3.23, 3.261, 3.247, 3.253, 3.205, 3.318, 3.324, 3.318, 3.348, 3.377, 3.348, 3.348, 3.36, 3.407, 3.155, 3.401, 3.436, 3.454, 3.442, 3.419, 3.43, 3.383, 3.419, 3.43, 3.466, 3.442, 3.495, 3.507, 3.495, 3.513, 3.519, 3.495, 3.525, 3.59, 3.537, 3.548, 3.457, 3.596, 3.584, 3.584, 3.607, 3.494, 3.59, 3.598, 3.602, 3.619, 3.526, 3.661, 3.64, 3.455, 3.602, 3.606, 3.649, 3.67, 3.631, 3.678, 3.598, 3.678, 3.696, 3.69, 3.726, 3.737, 3.743, 3.738, 3.749, 3.749, 3.656, 3.773, 3.749, 3.773, 3.808, 3.802, 3.814, 3.849, 3.664, 3.867, 3.891, 3.832, 3.844, 3.849, 3.844, 3.769, 3.867, 3.891, 3.938, 3.891, 3.792, 3.879, 3.914, 3.897, 3.885, 3.92, 3.956, 3.979, 3.985, 3.968, 3.991, 4.032, 3.985, 3.973, 3.962, 3.968, 3.973, 4.009, 3.985, 3.985, 4.056, 4.032, 4.044, 4.056, 4.08, 4.115, 4.08, 4.08, 4.08, 4.097, 4.023, 4.056, 4.068, 4.08, 4.103, 4.127, 4.174, 4.176, 4.174, 4.168, 4.18, 4.18, 4.174, 4.209, 4.209, 4.215, 4.198, 4.221, 4.221, 4.209, 4.221, 4.239, 4.204, 4.251, 4.245, 4.251, 4.269, 4.292, 4.333, 4.328, 4.345, 4.363, 4.328, 4.345, 4.357, 4.434, 4.387, 4.398, 4.381, 4.322, 4.259, 4.457, 4.452, 4.398, 4.44, 4.303,
]
datos_fila2 = [0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.102, 0.104, 0.106, 0.108, 0.11, 0.112, 0.114, 0.116, 0.118, 0.12, 0.122, 0.124, 0.126, 0.128, 0.13, 0.132, 0.134, 0.136, 0.138, 0.14, 0.142, 0.144, 0.146, 0.148, 0.15, 0.152, 0.154, 0.156, 0.158, 0.16, 0.162, 0.164, 0.166, 0.168, 0.17, 0.172, 0.174, 0.176, 0.178, 0.18, 0.182, 0.184, 0.186, 0.188, 0.19, 0.192, 0.194, 0.196, 0.198, 0.2, 0.202, 0.204, 0.206, 0.208, 0.21, 0.212, 0.214, 0.216, 0.218, 0.22, 0.222, 0.224, 0.226, 0.228, 0.23, 0.232, 0.234, 0.236, 0.238, 0.24, 0.242, 0.244, 0.246, 0.248, 0.25, 0.252, 0.254, 0.256, 0.258, 0.26, 0.262, 0.264, 0.266, 0.268, 0.27, 0.272, 0.274, 0.276, 0.278, 0.28, 0.282, 0.284, 0.286, 0.288, 0.29, 0.292, 0.294, 0.296, 0.298, 0.3, 0.302, 0.304, 0.306, 0.308, 0.31, 0.312, 0.314, 0.316, 0.318, 0.32, 0.322, 0.324, 0.326, 0.328, 0.33, 0.332, 0.334, 0.336, 0.338, 0.34, 0.342, 0.344, 0.346, 0.348, 0.35, 0.352, 0.354, 0.356, 0.358, 0.36, 0.362, 0.364, 0.366, 0.368, 0.37, 0.372, 0.374, 0.376, 0.378, 0.38, 0.382, 0.384, 0.386, 0.388, 0.39, 0.394, 0.398, 0.402, 0.406, 0.41, 0.414, 0.418, 0.422, 0.424, 0.426, 0.428, 0.43, 0.432, 0.434, 0.436, 0.438, 0.44, 0.442, 0.444, 0.446, 0.448, 0.45, 0.452, 0.454, 0.456, 0.458, 0.46, 0.462, 0.464, 0.466, 0.468, 0.47, 0.472, 0.474, 0.476, 0.478, 0.48, 0.482, 0.484, 0.486, 0.488, 0.49, 0.492, 0.494, 0.496, 0.498, 0.5, 0.502, 0.504, 0.506, 0.508, 0.51, 0.512, 0.514, 0.516, 0.518, 0.52, 0.522, 0.524, 0.526, 0.528, 0.53, 0.532, 0.534, 0.536, 0.538, 0.54, 0.542, 0.544, 0.546, 0.548, 0.55, 0.552, 0.554, 0.556, 0.558, 0.56, 0.562, 0.564, 0.566, 0.568, 0.57, 0.572, 0.574, 0.576, 0.578, 0.58, 0.582, 0.584, 0.586, 0.588, 0.59, 0.592, 0.594, 0.596, 0.598, 0.6, 0.602, 0.604, 0.606, 0.608, 0.61, 0.612, 0.614, 0.616, 0.618, 0.62, 0.622, 0.624, 0.626, 0.628, 0.63, 0.632, 0.634, 0.636, 0.638, 0.64, 0.642, 0.644, 0.646, 0.648, 0.65, 0.652, 0.654, 0.656, 0.658, 0.66, 0.662, 0.664, 0.666, 0.668, 0.67, 0.672, 0.674, 0.676, 0.678, 0.68, 0.682, 0.684, 0.686, 0.688, 0.69, 0.692, 0.694, 0.696, 0.698, 0.7, 0.702, 0.704, 0.706, 0.708, 0.71, 0.712, 0.714, 0.716,
]
# Plot position vs time
plt.plot(datos_fila2, datos_fila1, marker='o', linestyle='-')
plt.xlabel('Time (s)')
plt.ylabel('Position X (m)')
plt.title('Position vs Time')
plt.grid(True)
plt.show()
Loading...
Loading...
print ('Experimental values')
from tabulate import tabulate
table_data = [['Velocity before collision (m/s)', 'Velocity after collision (m/s)'],['11.44 ± 0.010','3.49 ± 0.02']]
print(tabulate(table_data, headers='firstrow'))
import pandas as pd
# Your data
datos_fila1 = [0.156, 0.156, 0.156, 0.156, 0.156, 0.157, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.162, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.158, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.159, 0.157, 0.16, 0.157, 0.157, 0.159, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.158, 0.16, 0.161, 0.162, 0.165, 0.164, 0.165, 0.166, 0.168, 0.169, 0.168, 0.17, 0.172, 0.174, 0.176, 0.175, 0.177, 0.176, 0.178, 0.185, 0.179, 0.18, 0.18, 0.183, 0.185, 0.178, 0.185, 0.185, 0.187, 0.194, 0.189, 0.19, 0.19, 0.192, 0.193, 0.185, 0.194, 0.196, 0.196, 0.197, 0.188, 0.197, 0.199, 0.2, 0.201, 0.201, 0.202, 0.202, 0.203, 0.204, 0.205, 0.207, 0.208, 0.209, 0.207, 0.209, 0.212, 0.211, 0.211, 0.211, 0.215, 0.216, 0.217, 0.217, 0.213, 0.217, 0.218, 0.218, 0.218, 0.216, 0.219, 0.219, 0.218, 0.22, 0.224, 0.222, 0.223, 0.224, 0.224, 0.225, 0.227, 0.228, 0.224, 0.229, 0.228, 0.219, 0.232, 0.232, 0.232, 0.23, 0.233, 0.233, 0.234, 0.234, 0.234, 0.225, 0.237, 0.238, 0.239, 0.239, 0.239, 0.239, 0.241, 0.241, 0.241, 0.244, 0.244, 0.244, 0.246, 0.251, 0.247, 0.249, 0.249, 0.25, 0.249, 0.241, 0.252, 0.254, 0.252, 0.252, 0.257, 0.257, 0.256, 0.257, 0.253, 0.248, 0.261, 0.261, 0.261, 0.259, 0.253, 0.263, 0.264, 0.264, 0.262, 0.255, 0.267, 0.267, 0.268, 0.271, 0.268, 0.269, 0.27, 0.269, 0.273, 0.271, 0.271, 0.274, 0.275, 0.272, 0.274, 0.275, 0.277, 0.279, 0.275, 0.279, 0.281, 0.281, 0.281, 0.278, 0.272, 0.271, 0.272, 0.284, 0.282, 0.287, 0.287, 0.285, 0.288, 0.284, 0.29, 0.291, 0.291, 0.292, 0.289, 0.283, 0.281, 0.277, 0.295, 0.292, 0.286, 0.285, 0.285, 0.299, 0.293, 0.301, 0.301, 0.296, 0.302, 0.298, 0.303, 0.302, 0.304, 0.305, 0.303, 0.298, 0.309, 0.302, 0.309, 0.303, 0.309, 0.311, 0.305, 0.313, 0.309, 0.308, 0.314, 0.319, 0.315, 0.318, 0.306, 0.318, 0.313, 0.32, 0.316, 0.311, 0.321, 0.322, 0.324, 0.32, 0.316, 0.326, 0.319, 0.327, 0.323, 0.318, 0.327, 0.325, 0.328, 0.328, 0.329, 0.331, 0.325, 0.331, 0.329, 0.324, 0.335, 0.335, 0.335, 0.335, 0.336, 0.339, 0.333, 0.339, 0.34, 0.329, 0.34, 0.341, 0.342, 0.337, 0.334, 0.343, 0.341, 0.345, 0.347, 0.347, 0.347, 0.348, 0.346, 0.352, 0.35, 0.352, 0.346, 0.35, 0.351, 0.354, 0.354, 0.349, 0.356, 0.352, 0.346, 0.359, 0.354, 0.36, 0.355, 0.362, 0.363, 0.358, 0.364, 0.358,
] # Your list of data
datos_fila2 = [0.000, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.102, 0.104, 0.106, 0.108, 0.11, 0.112, 0.114, 0.116, 0.118, 0.12, 0.122, 0.124, 0.126, 0.128, 0.13, 0.132, 0.134, 0.136, 0.138, 0.14, 0.142, 0.144, 0.146, 0.148, 0.15, 0.152, 0.154, 0.156, 0.158, 0.16, 0.162, 0.164, 0.166, 0.168, 0.17, 0.172, 0.174, 0.176, 0.178, 0.18, 0.182, 0.184, 0.186, 0.188, 0.19, 0.192, 0.194, 0.196, 0.198, 0.2, 0.202, 0.204, 0.206, 0.208, 0.21, 0.212, 0.214, 0.216, 0.218, 0.22, 0.222, 0.224, 0.226, 0.228, 0.23, 0.232, 0.234, 0.236, 0.238, 0.24, 0.242, 0.244, 0.246, 0.248, 0.25, 0.252, 0.254, 0.256, 0.258, 0.26, 0.262, 0.264, 0.266, 0.268, 0.27, 0.272, 0.274, 0.276, 0.278, 0.28, 0.282, 0.284, 0.286, 0.288, 0.29, 0.292, 0.294, 0.296, 0.298, 0.3, 0.302, 0.304, 0.306, 0.308, 0.31, 0.312, 0.314, 0.316, 0.318, 0.32, 0.322, 0.324, 0.326, 0.328, 0.33, 0.332, 0.334, 0.336, 0.338, 0.34, 0.342, 0.344, 0.346, 0.348, 0.35, 0.352, 0.354, 0.356, 0.358, 0.36, 0.362, 0.364, 0.366, 0.368, 0.37, 0.372, 0.374, 0.376, 0.378, 0.38, 0.382, 0.384, 0.386, 0.388, 0.39, 0.392, 0.394, 0.396, 0.398, 0.4, 0.402, 0.404, 0.406, 0.408, 0.41, 0.412, 0.414, 0.416, 0.418, 0.42, 0.422, 0.424, 0.426, 0.428, 0.43, 0.432, 0.434, 0.436, 0.438, 0.44, 0.442, 0.444, 0.446, 0.448, 0.45, 0.452, 0.454, 0.456, 0.458, 0.46, 0.462, 0.464, 0.466, 0.468, 0.47, 0.472, 0.474, 0.476, 0.478, 0.48, 0.482, 0.484, 0.486, 0.488, 0.49, 0.492, 0.494, 0.496, 0.498, 0.5, 0.502, 0.504, 0.506, 0.508, 0.51, 0.512, 0.514, 0.516, 0.518, 0.52, 0.522, 0.524, 0.526, 0.528, 0.53, 0.532, 0.534, 0.536, 0.538, 0.54, 0.542, 0.544, 0.546, 0.548, 0.55, 0.552, 0.554, 0.556, 0.558, 0.56, 0.562, 0.564, 0.566, 0.568, 0.57, 0.572, 0.574, 0.576, 0.578, 0.58, 0.582, 0.584, 0.586, 0.588, 0.59, 0.592, 0.594, 0.596, 0.598, 0.6, 0.602, 0.604, 0.606, 0.608, 0.61, 0.612, 0.614, 0.616, 0.618, 0.62, 0.622, 0.624, 0.626, 0.628, 0.63, 0.632, 0.634, 0.636, 0.638, 0.64, 0.642, 0.644, 0.646, 0.648, 0.65, 0.652, 0.654, 0.656, 0.658, 0.66, 0.662, 0.664, 0.666, 0.668, 0.67, 0.672, 0.674, 0.676, 0.678, 0.68, 0.682, 0.684, 0.686, 0.688, 0.69, 0.692, 0.694, 0.696, 0.698, 0.7, 0.702, 0.704, 0.706, 0.708, 0.71, 0.712, 0.714, 0.716, 0.718, 0.72,
] # Your list of data
# Create a DataFrame with two rows
data = [datos_fila1, datos_fila2]
df = pd.DataFrame(data)
# Create a dictionary with titles for each row
titles = {
'Position X (m)': datos_fila1,
'Time (s)': datos_fila2
}
# Set DataFrame index and assign titles to each row
df = pd.DataFrame(titles)
# Show the DataFrame
print(df)
import matplotlib.pyplot as plt
# Your data
datos_fila1 = [0.156, 0.156, 0.156, 0.156, 0.156, 0.157, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.162, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.158, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.159, 0.157, 0.16, 0.157, 0.157, 0.159, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.163, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.157, 0.158, 0.16, 0.161, 0.162, 0.165, 0.164, 0.165, 0.166, 0.168, 0.169, 0.168, 0.17, 0.172, 0.174, 0.176, 0.175, 0.177, 0.176, 0.178, 0.185, 0.179, 0.18, 0.18, 0.183, 0.185, 0.178, 0.185, 0.185, 0.187, 0.194, 0.189, 0.19, 0.19, 0.192, 0.193, 0.185, 0.194, 0.196, 0.196, 0.197, 0.188, 0.197, 0.199, 0.2, 0.201, 0.201, 0.202, 0.202, 0.203, 0.204, 0.205, 0.207, 0.208, 0.209, 0.207, 0.209, 0.212, 0.211, 0.211, 0.211, 0.215, 0.216, 0.217, 0.217, 0.213, 0.217, 0.218, 0.218, 0.218, 0.216, 0.219, 0.219, 0.218, 0.22, 0.224, 0.222, 0.223, 0.224, 0.224, 0.225, 0.227, 0.228, 0.224, 0.229, 0.228, 0.219, 0.232, 0.232, 0.232, 0.23, 0.233, 0.233, 0.234, 0.234, 0.234, 0.225, 0.237, 0.238, 0.239, 0.239, 0.239, 0.239, 0.241, 0.241, 0.241, 0.244, 0.244, 0.244, 0.246, 0.251, 0.247, 0.249, 0.249, 0.25, 0.249, 0.241, 0.252, 0.254, 0.252, 0.252, 0.257, 0.257, 0.256, 0.257, 0.253, 0.248, 0.261, 0.261, 0.261, 0.259, 0.253, 0.263, 0.264, 0.264, 0.262, 0.255, 0.267, 0.267, 0.268, 0.271, 0.268, 0.269, 0.27, 0.269, 0.273, 0.271, 0.271, 0.274, 0.275, 0.272, 0.274, 0.275, 0.277, 0.279, 0.275, 0.279, 0.281, 0.281, 0.281, 0.278, 0.272, 0.271, 0.272, 0.284, 0.282, 0.287, 0.287, 0.285, 0.288, 0.284, 0.29, 0.291, 0.291, 0.292, 0.289, 0.283, 0.281, 0.277, 0.295, 0.292, 0.286, 0.285, 0.285, 0.299, 0.293, 0.301, 0.301, 0.296, 0.302, 0.298, 0.303, 0.302, 0.304, 0.305, 0.303, 0.298, 0.309, 0.302, 0.309, 0.303, 0.309, 0.311, 0.305, 0.313, 0.309, 0.308, 0.314, 0.319, 0.315, 0.318, 0.306, 0.318, 0.313, 0.32, 0.316, 0.311, 0.321, 0.322, 0.324, 0.32, 0.316, 0.326, 0.319, 0.327, 0.323, 0.318, 0.327, 0.325, 0.328, 0.328, 0.329, 0.331, 0.325, 0.331, 0.329, 0.324, 0.335, 0.335, 0.335, 0.335, 0.336, 0.339, 0.333, 0.339, 0.34, 0.329, 0.34, 0.341, 0.342, 0.337, 0.334, 0.343, 0.341, 0.345, 0.347, 0.347, 0.347, 0.348, 0.346, 0.352, 0.35, 0.352, 0.346, 0.35, 0.351, 0.354, 0.354, 0.349, 0.356, 0.352, 0.346, 0.359, 0.354, 0.36, 0.355, 0.362, 0.363, 0.358, 0.364, 0.358,
]
datos_fila2 = [0.000, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.102, 0.104, 0.106, 0.108, 0.11, 0.112, 0.114, 0.116, 0.118, 0.12, 0.122, 0.124, 0.126, 0.128, 0.13, 0.132, 0.134, 0.136, 0.138, 0.14, 0.142, 0.144, 0.146, 0.148, 0.15, 0.152, 0.154, 0.156, 0.158, 0.16, 0.162, 0.164, 0.166, 0.168, 0.17, 0.172, 0.174, 0.176, 0.178, 0.18, 0.182, 0.184, 0.186, 0.188, 0.19, 0.192, 0.194, 0.196, 0.198, 0.2, 0.202, 0.204, 0.206, 0.208, 0.21, 0.212, 0.214, 0.216, 0.218, 0.22, 0.222, 0.224, 0.226, 0.228, 0.23, 0.232, 0.234, 0.236, 0.238, 0.24, 0.242, 0.244, 0.246, 0.248, 0.25, 0.252, 0.254, 0.256, 0.258, 0.26, 0.262, 0.264, 0.266, 0.268, 0.27, 0.272, 0.274, 0.276, 0.278, 0.28, 0.282, 0.284, 0.286, 0.288, 0.29, 0.292, 0.294, 0.296, 0.298, 0.3, 0.302, 0.304, 0.306, 0.308, 0.31, 0.312, 0.314, 0.316, 0.318, 0.32, 0.322, 0.324, 0.326, 0.328, 0.33, 0.332, 0.334, 0.336, 0.338, 0.34, 0.342, 0.344, 0.346, 0.348, 0.35, 0.352, 0.354, 0.356, 0.358, 0.36, 0.362, 0.364, 0.366, 0.368, 0.37, 0.372, 0.374, 0.376, 0.378, 0.38, 0.382, 0.384, 0.386, 0.388, 0.39, 0.392, 0.394, 0.396, 0.398, 0.4, 0.402, 0.404, 0.406, 0.408, 0.41, 0.412, 0.414, 0.416, 0.418, 0.42, 0.422, 0.424, 0.426, 0.428, 0.43, 0.432, 0.434, 0.436, 0.438, 0.44, 0.442, 0.444, 0.446, 0.448, 0.45, 0.452, 0.454, 0.456, 0.458, 0.46, 0.462, 0.464, 0.466, 0.468, 0.47, 0.472, 0.474, 0.476, 0.478, 0.48, 0.482, 0.484, 0.486, 0.488, 0.49, 0.492, 0.494, 0.496, 0.498, 0.5, 0.502, 0.504, 0.506, 0.508, 0.51, 0.512, 0.514, 0.516, 0.518, 0.52, 0.522, 0.524, 0.526, 0.528, 0.53, 0.532, 0.534, 0.536, 0.538, 0.54, 0.542, 0.544, 0.546, 0.548, 0.55, 0.552, 0.554, 0.556, 0.558, 0.56, 0.562, 0.564, 0.566, 0.568, 0.57, 0.572, 0.574, 0.576, 0.578, 0.58, 0.582, 0.584, 0.586, 0.588, 0.59, 0.592, 0.594, 0.596, 0.598, 0.6, 0.602, 0.604, 0.606, 0.608, 0.61, 0.612, 0.614, 0.616, 0.618, 0.62, 0.622, 0.624, 0.626, 0.628, 0.63, 0.632, 0.634, 0.636, 0.638, 0.64, 0.642, 0.644, 0.646, 0.648, 0.65, 0.652, 0.654, 0.656, 0.658, 0.66, 0.662, 0.664, 0.666, 0.668, 0.67, 0.672, 0.674, 0.676, 0.678, 0.68, 0.682, 0.684, 0.686, 0.688, 0.69, 0.692, 0.694, 0.696, 0.698, 0.7, 0.702, 0.704, 0.706, 0.708, 0.71, 0.712, 0.714, 0.716, 0.718, 0.72,
]
# Plot position vs time
plt.plot(datos_fila2, datos_fila1, marker='o', linestyle='-')
plt.xlabel('Time (s)')
plt.ylabel('Position X (m)')
plt.title('Position vs Time')
plt.grid(True)
plt.show()
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print ('Experimental values')
from tabulate import tabulate
table_data = [['Velocity before collision (m/s)', 'Velocity after collision (m/s)'],['(6 ± 5)E-3','(5.43 ± 0.02)']]
print(tabulate(table_data, headers='firstrow'))
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