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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 We load the data file obtained from the optimization (in low-thrust) of many legs (GTOC7) for the maximum initial mass
Step1:  Visualizing the data
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Python Code:
a = pk.load(open("slide14.pkl","rb"), encoding='latin1')
print("DATA = ", a)
print("ONE ROW = ", a[0])
Explanation: We load the data file obtained from the optimization (in low-thrust) of many legs (GTOC7) for the maximum initial mass: 
ast1, ast2, $t_1$, $t_2$, $\Delta V_L$, $m^$, $m^_L$, $m^*_D$
We want to study how well $m^_D$ and $m^_L$ approximate $m^*$
End of explanation
plt.rcParams['figure.figsize'] = [6,4]
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
plt.rcParams['legend.fontsize'] = 12
plt.figure()
skip=20
plt.scatter(  a[::skip,4], a[::skip,5], marker='x', alpha=1,edgecolor="none", label="$m^*$ - ground truth")
plt.scatter(  a[::skip,4], a[::skip,6], marker='.', color='b',alpha=1,edgecolor="none", label="$m_L^*$ - Lambert's approximation")
plt.scatter(  a[::skip,4], a[::skip,7], marker='.', color='r',alpha=1,edgecolor="none", label="$m_D^*$ - MIMA")
#plt.hexbin(  a[:,5]/a[:,4], a[:,7] / a[:,6], bins='log')
plt.ylabel("Kg")
plt.xlabel("$ \Delta V_L$, [m/s]")
plt.ylim(200,2000)
plt.xlim(1000,6000)
plt.legend()
plt.tight_layout(1)
plt.savefig(open("slide14.png", "w"))
b = a[a[:,5] < 2000]
b = b[b[:,5] > 500]
RMSE = np.sqrt(np.mean((b[:,5] - b[:,7])**2))
RMSE_LAMBERT = np.sqrt(np.mean((b[:,5] - b[:,6])**2))
MAE = np.mean(np.abs((b[:,5] - b[:,7])))
MAE_LAMBERT = np.mean(np.abs((b[:,5] - b[:,6])))
print(RMSE)
print(RMSE_LAMBERT)
print(MAE)
print(MAE_LAMBERT)
b[0]
len(b)
Explanation: Visualizing the data
End of explanation
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