update mpc state example

src/examples/plot_mpc_state.py

@@ -4,28 +4,30 @@
 
 This will open an interactive 3d plot of the solar system.
 
-This plots all comets from the MPC, along with the last 365 days of their orbit.
+This plots all of the Hildas from the MPC, along with the last 90 days of their orbit.
 """
 
 import neospy
 import matplotlib.pyplot as plt
 import numpy as np
 
 # Set the X/Y/Z scale
-zoom = 4
+zoom = 4.0
 
-# Fetch all known comet orbits
-orb = neospy.mpc.fetch_known_comet_orbit_data()
+# Fetch all known asteroid orbits
+orb = neospy.mpc.fetch_known_orbit_data()
+
+# Subset to the Hildas
+orb = orb[orb["group_name"] == "Hilda"]
+
+# convert the table of data to State objects
+states = neospy.mpc.table_to_states(orb)
 
 # Every object in the MPC orbit file has its own epoch of fit.
 # This means that some objects epochs may be months or years away from
 # others. In order to bring all of these objects to the same epoch, the
-# function below finds the most common epoch in the data, and uses n-body
-# integration to calculate the position of all objects that the median
-# epoch. If all known objects are included, then this may take a little
-# while to compute.
-states = neospy.mpc.table_to_states(orb)
-
+# function below will propagate the states to the first state's epoch.
+states = neospy.propagate_two_body(states, states[0].jd)
 
 # Grab the positions from this subset
 pos = np.array([p.pos for p in states])
@@ -46,13 +48,10 @@
     ax.plot(pos[0], pos[1], pos[2], color="black", alpha=0.2)
 
 for state in states:
-    # Skip fragments
-    if "  " in state.desig:
-        continue
     jd = states[0].jd
-    jds = np.linspace(jd - 365, jd, 100)
+    jds = np.linspace(jd - 90, jd, 100)
     pos = np.array([neospy.propagate_two_body([state], jd)[0].pos for jd in jds]).T
-    ax.plot(pos[0], pos[1], pos[2], color="black", alpha=0.2, lw=0.2)
+    ax.plot(pos[0], pos[1], pos[2], color="black", alpha=0.1, lw=0.2)
 
 ax.scatter(0, 0, 0, color="red")
 ax.set_xticks([-zoom, 0, zoom])