Analyze with IMAS-Python¶

import imas.training

# Open input data entry
entry = imas.training.get_training_db_entry()

# 1. Read and print the time of the equilibrium IDS for the whole scenario
#    This explicitly converts the data from the old DD version on disk, to the
#    new DD version of the environment that you have loaded!
equilibrium = entry.get("equilibrium")  # All time slices
# 2. Print the time array:
print(equilibrium.time)

# 3. Load the core_profiles IDS
core_profiles = entry.get("core_profiles")
# 4. When you inspect the core_profiles.time array, you'll find that item [1]
#    corresponds to t ~ 433s.
# 5. Print the electron temperature
print(core_profiles.profiles_1d[1].electrons.temperature)

# Close input data entry
entry.close()

import numpy as np
import imas.training


# Find nearest value and index in an array
def find_nearest(a, a0):
    "Element in nd array `a` closest to the scalar value `a0`"
    idx = np.abs(a - a0).argmin()
    return a[idx], idx


# Open input data entry
entry = imas.training.get_training_db_entry()

# Read the time array from the equilibrium IDS
eq = entry.get("equilibrium")
time_array = eq.time

# Find the index of the desired time slice in the time array
t_closest, t_index = find_nearest(time_array, 433)
print("Time index = ", t_index)
print("Time value = ", t_closest)

# Close input data entry
entry.close()

import imas.training

# Open input data entry
entry = imas.training.get_training_db_entry()

# Read n_e profile and the associated normalised toroidal flux coordinate at
t = 443  # seconds

cp = entry.get_slice("core_profiles", t, imas.ids_defs.CLOSEST_INTERP)

# profiles_1d should only contain the requested slice
assert len(cp.profiles_1d) == 1

ne = cp.profiles_1d[0].electrons.density
rho = cp.profiles_1d[0].grid.rho_tor_norm
print("ne =", ne)
print("rho =", rho)

# Close the datafile
entry.close()

import os

import matplotlib
import imas.training

# To avoid possible display issues when Matplotlib uses a non-GUI backend
if "DISPLAY" not in os.environ:
    matplotlib.use("agg")
else:
    matplotlib.use("TKagg")

import matplotlib.pyplot as plt

# Open input data entry
entry = imas.training.get_training_db_entry()

# Read n_e profile and the associated normalised toroidal flux coordinate at
t = 443  # seconds

cp = entry.get_slice("core_profiles", t, imas.ids_defs.CLOSEST_INTERP)

# profiles_1d should only contain the requested slice
assert len(cp.profiles_1d) == 1

ne = cp.profiles_1d[0].electrons.density
rho = cp.profiles_1d[0].grid.rho_tor_norm

# Plot the figure
fig, ax = plt.subplots()
ax.plot(rho, ne)
ax.set_ylabel(r"$n_e$")
ax.set_xlabel(r"$\rho_{tor, norm}$")
ax.ticklabel_format(axis="y", scilimits=(-1, 1))
plt.show()

import os

import matplotlib
import numpy

# To avoid possible display issues when Matplotlib uses a non-GUI backend
if "DISPLAY" not in os.environ:
    matplotlib.use("agg")
else:
    matplotlib.use("TKagg")

from matplotlib import pyplot as plt

import imas
from imas.ids_defs import MDSPLUS_BACKEND

database, pulse, run, user = "ITER", 134173, 106, "public"
data_entry = imas.DBEntry(
    MDSPLUS_BACKEND, database, pulse, run, user, data_version="3"
)
data_entry.open()
# Enable lazy loading with `lazy=True`:
core_profiles = data_entry.get("core_profiles", lazy=True)

# No data has been read from the lowlevel backend yet
# The time array is loaded only when we access it on the following lines:
time = core_profiles.time
print(f"Time has {len(time)} elements, between {time[0]} and {time[-1]}")

# Find the electron temperature at rho=0 for all time slices
electon_temperature_0 = numpy.array(
    [p1d.electrons.temperature[0] for p1d in core_profiles.profiles_1d]
)

# Plot the figure
fig, ax = plt.subplots()
ax.plot(time, electon_temperature_0)
ax.set_ylabel("$T_e$")
ax.set_xlabel("$t$")
plt.show()