<jupyter_start><jupyter_text>Generating spin configurations via Monte Carlo - $L=100$<jupyter_code>import pandas as pd

import sys
sys.path.append('../modules')
from mc_ising2d import IsingMC
L = 100
T_min = 1.0
T_max = 3.5
num_T = 26
MC_states_per_T = 1000

MC_sweeps_per_state= 100
MC_eq_sweeps= 50
ising2D = IsingMC(L= L, 
                  T_min= T_min, 
                  T_max= T_max, 
                  num_T= num_T, 
                  MC_states_per_T= MC_states_per_T,
                  MC_sweeps_per_state= MC_sweeps_per_state,
                  MC_eq_sweeps= MC_eq_sweeps,
                  include_Tc= True)
ising2D.fit()

ising2D.plot_thermodynamics()

spin_MC = ising2D.thermodymanics_MC_df()

spin_MC.to_pickle('../data/L100_Ising2D_MC.pkl')
ising = pd.read_pickle('../data/L100_Ising2D_MC.pkl')
ising.head()
IsingMC(L= 100).plot_thermodynamics(spin_MC= ising, Tc_scaled= False)<jupyter_output>Method fit will construct states to the following temperatures: 
[3.5        3.4        3.3        3.2        3.1        3.
 2.9        2.8        2.7        2.6        2.5        2.4
 2.3        2.27918531 2.26918531 2.25918531 2.2        2.1
 2.         1.9        1.8        1.7        1.6        1.5
 1.4        1.3        1.2        1.1        1.        ]
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