v. 0.1.5. Added jsplots.py for JavaScript-based plots in notebooks in…

… the book

biocircuits/__init__.py

@@ -10,8 +10,9 @@
 from .dynsys import *
 from .gillespie import *
 from .rd import *
+from . import jsplots
 
 
 __author__ = """Justin Bois"""
 __email__ = "[email protected]"
-__version__ = "0.1.4"
+__version__ = "0.1.5"

biocircuits/jsplots.py

@@ -0,0 +1,142 @@
+import warnings
+
+import numpy as np
+
+import matplotlib.streamplot
+
+import bokeh.application
+import bokeh.application.handlers
+import bokeh.layouts
+import bokeh.models
+import bokeh.palettes
+import bokeh.plotting
+
+import colorcet
+
+from . import utils
+
+
+def turing_dispersion_relation():
+    """Plot of dispersion relation for Turing patterns.
+
+    Replaces Python code:
+
+    def dispersion_relation(k_vals, d, mu):
+        lam = np.empty_like(k_vals)
+        for i, k in enumerate(k_vals):
+            A = np.array([[1-d*k**2,          1],
+                          [-2*mu,    -mu - k**2]])
+            lam[i] = np.linalg.eigvals(A).real.max()
+
+        return lam
+
+    d_slider = pn.widgets.FloatSlider(
+        name="d", start=0.01, end=1, value=0.05, step=0.01, width=150
+    )
+
+    mu_slider = pn.widgets.FloatSlider(
+        name="μ", start=0.01, end=2, value=1.5, step=0.005, width=150
+    )
+
+
+    @pn.depends(d_slider.param.value, mu_slider.param.value)
+    def plot_dispersion_relation(d, mu):
+        k = np.linspace(0, 10, 200)
+        lam_max_real_part = dispersion_relation(k, d, mu)
+
+        p = bokeh.plotting.figure(
+            frame_width=350,
+            frame_height=200,
+            x_axis_label="k",
+            y_axis_label="Re[λ-max]",
+            x_range=[0, 10],
+        )
+
+        p.line(k, lam_max_real_part, color="black", line_width=2)
+
+        return p
+
+
+    pn.Column(
+        pn.Row(pn.Spacer(width=50), d_slider, mu_slider),
+        pn.Spacer(height=20),
+        plot_dispersion_relation,
+    )
+
+    """
+    d_slider = bokeh.models.Slider(
+        title="d", start=0.01, end=1, value=0.05, step=0.01, width=150
+    )
+
+    mu_slider = bokeh.models.Slider(
+        title="μ", start=0.01, end=2, value=1.5, step=0.005, width=150
+    )
+
+    k = np.linspace(0, 10, 500)
+    k2 = k ** 2
+    mu = mu_slider.value
+    d = d_slider.value
+    b = mu + (1.0 + d) * k2 - 1.0
+    c = (mu + k ** 2) * (d * k ** 2 - 1.0) + 2.0 * mu
+    discriminant = b ** 2 - 4.0 * c
+
+    lam = np.empty_like(k)
+    inds = discriminant <= 0
+    lam[inds] = -b[inds] / 2.0
+
+    inds = discriminant > 0
+    lam[inds] = (-b[inds] + np.sqrt(discriminant[inds])) / 2.0
+
+    cds = bokeh.models.ColumnDataSource(dict(k=k, lam=lam))
+
+    p = bokeh.plotting.figure(
+        frame_width=350,
+        frame_height=200,
+        x_axis_label="k",
+        y_axis_label="Re[λ-max]",
+        x_range=[0, 10],
+    )
+
+    p.line(source=cds, x="k", y="lam", color="black", line_width=2)
+
+    js_code = """
+    function dispersion_relation(mu, d, k) {
+        let k2 = k**2;
+        let b = mu + (1.0 + d) * k2 - 1.0;
+        let c = (mu + k**2) * (d * k**2 - 1.0) + 2.0 * mu
+        let discriminant = b**2 - 4.0 * c;
+
+        if (discriminant < 0) {
+            return -b / 2.0;
+        }
+        else {
+            return (-b + Math.sqrt(discriminant)) / 2.0;
+        }
+    }
+
+    let mu = mu_slider.value;
+    let d = d_slider.value;
+    let k = cds.data['k'];
+    let lam = cds.data['lam'];
+
+    for (let i = 0; i < k.length; i++) {
+        lam[i] = dispersion_relation(mu, d, k[i]);
+    }
+
+    cds.change.emit();
+    """
+    callback = bokeh.models.CustomJS(
+        args=dict(cds=cds, d_slider=d_slider, mu_slider=mu_slider), code=js_code
+    )
+    mu_slider.js_on_change("value", callback)
+    d_slider.js_on_change("value", callback)
+
+    layout = bokeh.layouts.column(
+        bokeh.layouts.row(
+            bokeh.models.Spacer(width=60), d_slider, mu_slider, width=400
+        ),
+        bokeh.models.Spacer(height=20),
+        p,
+    )
+
+    return layout

biocircuits/viz.py

@@ -462,30 +462,28 @@ def xyt_plot(
     # Callback
     js_code = """
 function sortedIndex(array, value) {
-    var low = 0,
+    let low = 0,
         high = array.length;
 
     while (low < high) {
-        var mid = (low + high) >>> 1;
+        let mid = (low + high) >>> 1;
         if (array[mid] < value) low = mid + 1;
         else high = mid;
     }
     return low;
 }
 
-var x = source_plot.data['x'];
-var x_len = x.length;
-var t = source_t.data['t'];
+let x = source_plot.data['x'];
+let x_len = x.length;
+let t = source_t.data['t'];
 
-var i = sortedIndex(t, cb_obj.value);
-
-var k;
+let i = sortedIndex(t, cb_obj.value);
 """
 
     for var_name in ["y_" + str(j) for j in range(len(y))]:
-        js_code += f"""var {var_name} = source_plot.data['{var_name}'];
-var {var_name}_source = source.data['{var_name}'];
-for (k = 0; k < x_len; k++) {var_name}[k] = {var_name}_source[x_len * i + k];
+        js_code += f"""let {var_name} = source_plot.data['{var_name}'];
+let {var_name}_source = source.data['{var_name}'];
+for (let k = 0; k < x_len; k++) {var_name}[k] = {var_name}_source[x_len * i + k];
 
 """
 
@@ -498,7 +496,9 @@ def xyt_plot(
 
     t_slider.js_on_change("value", callback)
 
-    return bokeh.layouts.column(t_slider, p)
+    return bokeh.layouts.column(
+        bokeh.layouts.row(bokeh.models.Spacer(width=10), t_slider), p
+    )
 
 
 def phase_portrait(

setup.py

@@ -2,7 +2,7 @@
 from codecs import open
 from os import path
 
-__version__ = '0.1.4'
+__version__ = '0.1.5'
 
 here = path.abspath(path.dirname(__file__))