Merge pull request #115 from uit-cosmo/examples

Examples

Author: Sosnowsky

blobmodel/model.py

@@ -339,9 +339,9 @@ def _compute_start_stop(self, blob: Blob, speed_up: bool, error: float):
         # ignores t_drain when calculating stop time
         stop = np.minimum(
             self._geometry.t.size,
-            start
-            + int(
-                (
+            int(
+                blob.t_init
+                + (
                     -blob.width_prop * np.log(error * np.sqrt(np.pi))
                     + self._geometry.Lx
                     - blob.pos_x

blobmodel/stochasticality.py

@@ -245,4 +245,4 @@ def is_one_dimensional(self) -> bool:
         - Perpendicular width parameters are irrelevant since perp shape should be ignored by the Bolb class.
 
         """
-        return self.velocity_y_dist == "zeros"
+        return self.velocity_y_dist == DistributionEnum.zeros

examples/1d_animation.py

@@ -6,6 +6,8 @@
     BlobShapeEnum,
 )
 
+# Example of a one-dimensional realization and animation.
+
 bf = DefaultBlobFactory()
 
 bm = Model(

examples/2_sided_exp_pulse.py

@@ -6,7 +6,8 @@
     BlobShapeEnum,
 )
 import matplotlib.pyplot as plt
-import numpy as np
+
+# Example of use of extra blob shape parameters with a double exponential blob shape.
 
 bf = DefaultBlobFactory(
     shape_param_x_parameter=0.5,

examples/2d_animation.py

@@ -1,6 +1,6 @@
-from blobmodel import Model, show_model, BlobShapeImpl, BlobShapeEnum
+from blobmodel import Model, show_model
 
-# here you can define your custom parameter distributions
+# Example of a simple two-dimensional realization and animation.
 
 bm = Model(
     Nx=100,
@@ -15,7 +15,7 @@
     t_init=10,
 )
 
-# create data
+# Make a realization and store it in a DataSet object
 ds = bm.make_realization(speed_up=True, error=1e-2)
 # show animation and save as gif
 show_model(dataset=ds, interval=100, gif_name="2d_animation.gif", fps=10)

examples/analyse_output.py

@@ -3,6 +3,8 @@
 import matplotlib.pyplot as plt
 import os
 
+# Example reading data from a file stored in "example.nc" and plotting the time mean using xarray functions.
+
 
 if not os.path.isfile("./example.nc"):
     bm = Model(

examples/blob_tilting.py

@@ -8,7 +8,8 @@
 )
 import numpy as np
 
-# To have something to compare we will first make a simulation with aligned blobs moving with velocities
+# Example with tilted blobs, to have something to compare we will first make a simulation with
+# aligned blobs moving with velocities
 
 vx = 1
 vy = 1

examples/changing_t_drain.py

@@ -8,6 +8,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
+# Example of use of a spatially varying t_drain parameter. The results are compared with the case of constant
+# t_drain.
+
 bf = DefaultBlobFactory(A_dist=DistributionEnum.deg, vy_dist=DistributionEnum.zeros)
 
 t_drain = np.linspace(2, 1, 100)
@@ -29,7 +32,7 @@
 ds = tmp.make_realization(file_name="profile_comparison.nc", speed_up=True, error=1e-2)
 
 
-def plot_cahnging_t_drain(ds):
+def plot_changing_t_drain(ds):
     x = np.linspace(0, 10, 100)
     t_p = 1
     t_w = 1 / 10
@@ -49,4 +52,4 @@ def plot_cahnging_t_drain(ds):
     plt.show()
 
 
-plot_cahnging_t_drain(ds)
+plot_changing_t_drain(ds)

examples/compare_to_analytical_sol.py

@@ -8,11 +8,14 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
+# The time average of a one-dimensional model realization with constant horizontal velocities is compared
+# with analytically derived results. See O. E. Garcia, et al.; Phys. Plasmas 1 May 2016; 23 (5): 052308. https://doi.org/10.1063/1.4951016
+
 # use DefaultBlobFactory to define distribution functions fo random variables
 bf = DefaultBlobFactory(A_dist=DistributionEnum.deg, vy_dist=DistributionEnum.zeros)
 
 tmp = Model(
-    Nx=100,
+    Nx=20,
     Ny=1,
     Lx=10,
     Ly=0,
@@ -23,29 +26,24 @@
     periodic_y=False,
     num_blobs=10000,
     blob_factory=bf,
+    one_dimensional=True,
 )
 
-ds = tmp.make_realization(file_name="profile_comparison.nc", speed_up=False, error=1e-4)
-
-
-def plot_convergence_to_analytical_solution(ds):
-    x = np.linspace(0, 10, 100)
-    t_p = 1
-    t_w = 1 / 10
-    amp = 1
-    v_p = 1.0
-    t_loss = 2.0
-    t_d = t_loss * t_p / (t_loss + t_p)
-
-    analytical_profile = (
-        1 / np.sqrt(np.pi) * t_d / t_w * amp * np.exp(-x / (v_p * t_loss))
-    )
-
-    ds.n.isel(y=0).mean(dim=("t")).plot(label="blob_model")
-    plt.yscale("log")
-    plt.plot(x, analytical_profile, label="analytical solution")
-    plt.legend()
-    plt.show()
-
-
-plot_convergence_to_analytical_solution(ds)
+ds = tmp.make_realization(speed_up=True, error=1e-10)
+x = np.linspace(0, 10, 20)
+t_p = 1
+t_w = 1 / 10
+amp = 1
+v_p = 1.0
+t_loss = 2.0
+t_d = t_loss * t_p / (t_loss + t_p)
+
+analytical_profile = t_d / t_w * amp * np.exp(-x / (v_p * t_loss))
+
+ds.n.isel(y=0).mean(dim=("t")).plot(label="blob_model")
+plt.yscale("log")
+plt.xlabel("x")
+plt.ylabel("Time average")
+plt.plot(x, analytical_profile, label="analytical solution")
+plt.legend()
+plt.show()

examples/custom_blobfactory.py

@@ -4,11 +4,11 @@
     Blob,
     show_model,
     AbstractBlobShape,
-    BlobShapeImpl,
-    BlobShapeEnum,
 )
 import numpy as np
 
+# Example of implementing a BlobFactory, which allows to set blob parameters in any desirable way.
+
 
 # create custom class that inherits from BlobFactory
 # here you can define your custom parameter distributions