Fix warnings

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doc/notebooks/demo_thermalized_lbm.ipynb

 %% Cell type:code id: tags:
 ``` python
 import pystencils as ps
 from lbmpy.session import *
 from lbmpy.moments import is_shear_moment, get_order
 ```
 %% Cell type:markdown id: tags:
 # Demo: Thermalized (Randomized) LBM
 This demo notebook shows how to modify the LB collision operator to account for microscopic fluctuations. This technique is also called thermalized or randomized LBM. In these methods a random fluctuation is added to the equilibrium moments. In this simple example we implement a thermalized model by writing our own simple linear congruent random number generator, the draws indepedent random numbers on each cell. A seed is stored for each cell since all cells are processed in parallel.
 %% Cell type:markdown id: tags:
 ## 1) Creating the method definition
 We begin with a standard SRT method...
 %% Cell type:code id: tags:
 ``` python
 random_number_symbols = sp.symbols("rand_:3")
 method = create_lb_method(method='srt', relaxation_rate=1.8)
 method
 ```
 %% Output
-    <lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7fee5d180fd0>
+    <lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f53ba131fa0>
 %% Cell type:markdown id: tags:
 ...and modify its collision table. The `create_lb_method_from_existing` function provides a convenient way to do this.
 We pass a custom function that receives a row of the collision table and returns a modified version of it.
 Our modification rule adds symbols that stand for random numbers to some of the moments.
 %% Cell type:code id: tags:
 ``` python
 def modification_func(moment, equilibrium, relaxation_rate):
     if is_shear_moment(moment, dim=method.dim):
         equilibrium += random_number_symbols[0] * 0.001
     elif get_order(moment) > 2:
         equilibrium += random_number_symbols[1] * 0.001
     return moment, equilibrium, relaxation_rate
 thermalized_method = create_lb_method_from_existing(method, modification_func)
 thermalized_method.override_weights(method.weights)
 thermalized_method
 ```
 %% Output
-    <lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7fee54d8ee80>
+    <lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f53b1d12a90>
 %% Cell type:markdown id: tags:
 ## 2) Creating the kernel equations
 %% Cell type:markdown id: tags:
 Next we have to define rules how to compute the quantities $rand_0$ and $rand_1$.
 We do this using a linear congruent RNG on each cell. We pass in a seed array, and in each time step this seed array is updated with the new random numbers.
 %% Cell type:code id: tags:
 ``` python
 dh = ps.create_data_handling(domain_size=(80, 80))
 seed_type = np.uint32
 max_seed_type = np.iinfo(seed_type).max
 # Initialize the seed array
 seedField = dh.add_array('seed', dtype=seed_type, values_per_cell=len(random_number_symbols))
 for b in dh.iterate():
     random_field = np.random.randint(0, high=max_seed_type, dtype=seed_type, size=b['seed'].shape)
     np.copyto(b['seed'], random_field)
 debug_output = dh.add_array('dbg')
 linear_congruent_rng_eqs = [ps.Assignment(seedField(i), seedField(i) * 1664525 + 1013904223)
                             for i, _ in enumerate(random_number_symbols)]
-floatEqs = [ps.Assignment(ps.TypedSymbol(s.name, np.float), seedField(i) / max_seed_type)
+floatEqs = [ps.Assignment(ps.TypedSymbol(s.name, np.float64), seedField(i) / max_seed_type)
             for i, s in enumerate(random_number_symbols)]
 rng_eqs = linear_congruent_rng_eqs + floatEqs + [ps.Assignment(debug_output.center, seedField(0) / max_seed_type)]
 rng_eqs
 ```
 %% Output
     $\displaystyle \left[ {{seed}_{(0,0)}^{0}} \leftarrow 1664525 {{seed}_{(0,0)}^{0}} + 1013904223, \  {{seed}_{(0,0)}^{1}} \leftarrow 1664525 {{seed}_{(0,0)}^{1}} + 1013904223, \  {{seed}_{(0,0)}^{2}} \leftarrow 1664525 {{seed}_{(0,0)}^{2}} + 1013904223, \  rand_{0} \leftarrow \frac{{{seed}_{(0,0)}^{0}}}{4294967295}, \  rand_{1} \leftarrow \frac{{{seed}_{(0,0)}^{1}}}{4294967295}, \  rand_{2} \leftarrow \frac{{{seed}_{(0,0)}^{2}}}{4294967295}, \  {{dbg}_{(0,0)}} \leftarrow \frac{{{seed}_{(0,0)}^{0}}}{4294967295}\right]$
     ⎡
     ⎢seed_C__0 := 1664525⋅seed_C__0 + 1013904223, seed_C__1 := 1664525⋅seed_C__1 +
     ⎣
     
                                                                        seed_C__0
      1013904223, seed_C__2 := 1664525⋅seed_C__2 + 1013904223, rand₀ := ──────────,
                                                                        4294967295
     
               seed_C__1            seed_C__2            seed_C__0 ⎤
      rand₁ := ──────────, rand₂ := ──────────, dbg_C := ──────────⎥
               4294967295           4294967295           4294967295⎦
 %% Cell type:markdown id: tags:
 These assignments are then added to the LB collision rule.
 %% Cell type:code id: tags:
 ``` python
 collision_rule = create_lb_collision_rule(lb_method=thermalized_method)
 collision_rule.subexpressions += rng_eqs
 ```
 %% Cell type:markdown id: tags:
 Finally, lets test our method by running a lid-driven-cavity with it.
 %% Cell type:code id: tags:
 ``` python
 ldc = create_lid_driven_cavity(data_handling=dh, collision_rule=collision_rule, lid_velocity=0.05,
                                kernel_params={'rand_0': 0, 'rand_1': 0})
 ```
 %% Cell type:code id: tags:
 ``` python
 #show_code(ldc.ast)
 ```
 %% Cell type:code id: tags:
 ``` python
 ldc.run(100)
 ```
 %% Cell type:code id: tags:
 ``` python
 plt.figure(dpi=200)
 plt.vector_field(ldc.velocity[:, :]);
 ```
 %% Output
 %% Cell type:code id: tags:
 ``` python
 assert np.isfinite(dh.max('ldc_velocity'))
 ```