Included tests

firestarx35 · Aug 20, 2023 · c9d661d · c9d661d
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diff --git a/README.md b/README.md
@@ -1,27 +1,29 @@
 # McPy - A Micromagnetic Monte Carlo Simulation package
 
-McPy is a package developed for monte carlo simulations in micromagnetics to study magnetic pseudoparticles. This has been developed as part of my Master's thesis "Parallelisation and Optimisation of Monte Carlo Simulation in Nanomagnetism" to develop an extension of Ubermag(https://ubermag.github.io/index.html), a micromagnetic simulations package. Ubermag is a collection of several independent Python packages that can be used independently as well as in combination to be used for other physics simulations such as Fluid Dynamics.
+McPy is a package developed for monte carlo simulations in micromagnetics to study magnetic pseudoparticles. This has been developed as part of my Master's thesis "Parallelisation and Optimisation of Monte Carlo Simulation in Nanomagnetism" to develop an extension of [Ubermag](https://ubermag.github.io/index.html), a micromagnetic simulations package. Ubermag is a collection of several independent Python packages that can be used independently as well as in combination to be used for other physics simulations such as Fluid Dynamics.
 
 Ubermag already contains well maintained energy minimisation solvers but the goal of this package is to extend the capabilities of Ubermag by adding a non-pertubative approach to energy minimisation that has not been implemented till now.
 
 #### Why Monte Carlo in micromagnetics?
 
 Here are several papers validating the effectiveness of monte carlo in this field
- - 
- - 
- - 
+ - [Simulating anti-skyrmions on a lattice](https://www.nature.com/articles/s41598-022-22043-0)
+ - [The skyrmion lattice phase in three dimensional chiral magnets from Monte Carlo simulations](https://arxiv.org/abs/1304.6580)
+
 
 ## Installation:
-- ### Requirements
+
+### Requirements
         - Python==3.10.11
-        - Ubermag with default oommfc driver : Follow the installation guide at (https://ubermag.github.io/installation.html)
+        - Ubermag with default oommfc driver : Follow the installation guide at [Ubermag Installation](https://ubermag.github.io/installation.html)
         - NumPy==1.24.3
         - Numba==0.57.0
         - cupy-cuda12x==12.1.0
 
-Installation of abovepackages can be achieved by running the below command
+Installation of above packages can be achieved by running the following command
+
+```bash 
 
-```
 conda env create -f environment.yml
 
 ```
@@ -30,8 +32,10 @@ conda env create -f environment.yml
 ## Tests:
 To run the automated tests
 
-```
-python -m unittest montecarlo/test
+```bash
+
+python -m unittest tests.py
+
 ```
 
 ## Guide:
@@ -53,7 +57,7 @@ python -m unittest montecarlo/test
     - `bloch_point.ipynb`:
     - `Instructios.ipynb`:
 
-![Project Structure](images/project%20structure.png)
+<!-- ![Project Structure](images/project%20structure.png) -->
 
 ## Documentation
 You can find the documentation at `docs/html/index.html`. 

diff --git a/TODO b/TODO
@@ -0,0 +1,3 @@
+Disc exchang energy in numpy only. And correct it in enery_validation.ipynb
+Testing new spin.\
+Check if host PC has Numba installed if not then revert back to numpy
diff --git a/environment.yml b/environment.yml
@@ -3,9 +3,8 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-  - python=3.8
+  - python=3.10
   - numpy
   - numba
   - ubermag
-  # - pip
-  # - -r requirements.txt
+
diff --git a/mcpy/__pycache__/Driver.cpython-38.pyc b/mcpy/__pycache__/Driver.cpython-38.pyc
diff --git a/mcpy/__pycache__/System.cpython-38.pyc b/mcpy/__pycache__/System.cpython-38.pyc
diff --git a/mcpy/__pycache__/__init__.cpython-310.pyc b/mcpy/__pycache__/__init__.cpython-310.pyc
diff --git a/mcpy/__pycache__/__init__.cpython-38.pyc b/mcpy/__pycache__/__init__.cpython-38.pyc
diff --git a/mcpy/__pycache__/driver.cpython-310.pyc b/mcpy/__pycache__/driver.cpython-310.pyc
diff --git a/mcpy/__pycache__/system.cpython-310.pyc b/mcpy/__pycache__/system.cpython-310.pyc
diff --git a/mcpy/driver.py b/mcpy/driver.py
@@ -3,7 +3,7 @@
 from numba import njit, prange
 from tqdm import tqdm
 from mcpy.energies.numpy_energies import numpy_delta
-
+# from mcpy.system import Grid
 
 """This module contains the main functions for implementing the Metropolis-Hastings Monte Carlo simulations. 
 By changing the spin each iteartion by random angle with uniform distribution."""
@@ -13,14 +13,14 @@
 K_B = 1.38064852e-23  # Boltzmann constant
 
 
-def driver_numpy(N, grid, zeeman_H, temperature):
+def driver_numpy(N: int, grid, zeeman_H: np.ndarray, temperature: np.float64) -> np.ndarray:
     """ Monte Carlo driver function for Numpy implementation
 
-    Args: 
+    Args:
         N (int): Number of Monte Carlo steps
-        grid (Grid): mcpy.Grid object
+        grid (mcpy.system.Grid): Grid object
         zeeman_H (np.ndarray): Zeeman field
-        temperature (float): Temperature in Kelvin
+        temperature (np.float64): Temperature in Kelvin
 
     Returns:
         grid (np.ndarray): Relaxed system
@@ -97,24 +97,26 @@ def driver_numpy(N, grid, zeeman_H, temperature):
 
 
 @njit(fastmath=True)
-def driver_numba(N, grid, energy_func, zeeman_H, anisotropy_K, anisotropy_u, exchange_A, dmi_D, Dtype, Ms, dx, dy, dz, temperature):
+def driver_numba(N: int, grid: np.ndarray, energy_func, zeeman_H: np.ndarray, anisotropy_K: np.float64,
+                 anisotropy_u: np.ndarray, exchange_A: np.float64, dmi_D: np.ndarray, Dtype: str, Ms: np.float64,
+                 dx: np.float64, dy: np.float64, dz: np.float64, temperature: np.float64) -> np.ndarray:
     """ Monte Carlo driver function for Numba implementation
 
-    Args: 
+    Args:
         N (int): Number of Monte Carlo steps
-        grid (np.ndarray): 3D array of spins
-        energy_func (function): Energy function to be used
+        grid (np.ndarray): 3D array of spins(vector field)
+        energy_func (function): Energy function
         zeeman_H (np.ndarray): Zeeman field
-        anisotropy_K (np.ndarray): Anisotropy constant
+        anisotropy_K (np.float64): Anisotropy constant
         anisotropy_u (np.ndarray): Anisotropy axis
-        exchange_A (np.ndarray): Exchange constant
-        dmi_D (np.ndarray): Dzyaloshinskii-Moriya constant
-        Dtype (np.dtype): DMI type or Crystal class
-        Ms (float): Saturation magnetisation
-        dx (float): Grid spacing in x direction
-        dy (float): Grid spacing in y direction
-        dz (float): Grid spacing in z direction
-        temperature (float): Temperature in Kelvin
+        exchange_A (np.float64): Exchange constant
+        dmi_D (np.ndarray): DMI constant
+        Dtype (str): DMI crystal class
+        Ms (np.float64): Saturation magnetisation
+        dx (np.float64): Grid spacing in x direction
+        dy (np.float64): Grid spacing in y direction
+        dz (np.float64): Grid spacing in z direction
+        temperature (np.float64): Temperature in Kelvin
 
     Returns:
         grid (np.ndarray): Relaxed system
@@ -191,21 +193,29 @@ def driver_numba(N, grid, energy_func, zeeman_H, anisotropy_K, anisotropy_u, exc
 
 
 @njit(fastmath=True)
-def random_spin_uniform(v, alpha):
-    # Sample the cosine of the polar angle uniformly
+def random_spin_uniform(v: np.ndarray, alpha: np.float64) -> np.ndarray:
+    """Generates a random spin vector with uniform distribution
+
+    Args:
+        v (np.ndarray): Vector
+        alpha (np.float64): Angle
+
+    Returns:
+        v_proposal (np.ndarray): Random spin vector
+    """
+
+    # cos of the polar angle
     cos_del0 = np.random.uniform(np.cos(alpha), 1)
     del_phi = np.random.uniform(0, 2 * np.pi)
 
-    # Derive the polar angle from its cosine value
-    del0 = np.arccos(cos_del0)
+    del0 = np.arccos(cos_del0)  # polar angle
     dx = np.sin(del0) * np.cos(del_phi)
     dy = np.sin(del0) * np.sin(del_phi)
     dz = np.cos(del0)
 
-    # Combine the unit vector with the original vector
     v_proposal = v + np.array([dx, dy, dz])
 
-    # Normalising
+    # normalise
     v_proposal = v_proposal / np.sqrt(np.sum(v_proposal ** 2))
 
     return v_proposal
diff --git a/mcpy/energies/__pycache__/__init__.cpython-310.pyc b/mcpy/energies/__pycache__/__init__.cpython-310.pyc
diff --git a/mcpy/energies/__pycache__/__init__.cpython-38.pyc b/mcpy/energies/__pycache__/__init__.cpython-38.pyc
diff --git a/mcpy/energies/__pycache__/numba_energies.cpython-310.pyc b/mcpy/energies/__pycache__/numba_energies.cpython-310.pyc
diff --git a/mcpy/energies/__pycache__/numba_energies.cpython-38.pyc b/mcpy/energies/__pycache__/numba_energies.cpython-38.pyc
diff --git a/mcpy/energies/__pycache__/numpy_energies.cpython-310.pyc b/mcpy/energies/__pycache__/numpy_energies.cpython-310.pyc
diff --git a/mcpy/energies/__pycache__/numpy_energies.cpython-38.pyc b/mcpy/energies/__pycache__/numpy_energies.cpython-38.pyc