add pycafe code

cafe_app.py

@@ -0,0 +1,138 @@
+from pathlib import Path
+
+import altair as alt
+import numpy as np
+import pandas as pd
+import solara
+import sympy as sp
+
+#
+P1, P2, PT, k_on, k_off, kD = sp.symbols("P_1 P_2 P_T k_on k_off k_D", positive=True)
+
+sol = sp.solve(
+    [
+        -2 * k_on * P1 * P1 + 2 * k_off * P2,
+        P1 + 2 * P2 - PT,
+        (k_off / k_on) - kD,
+    ],
+    [P1, P2, k_on, k_off],
+    dict=True,
+)
+
+solve_for = [P1, P2]
+inputs = [PT, kD]
+
+lambdas = {s: sp.lambdify(inputs, sol[0][s]) for s in solve_for}
+ld_total = sp.lambdify(inputs, sol[0][P1] + sol[0][P2])
+
+
+def make_chart(df: pd.DataFrame) -> alt.Chart:
+    source = df.melt("PT", var_name="species", value_name="y")
+
+    # Create a selection that chooses the nearest point & selects based on x-value
+    nearest = alt.selection_point(
+        nearest=True, on="pointerover", fields=["PT"], empty=False
+    )
+
+    # The basic line
+    line = (
+        alt.Chart(source)
+        .mark_line(interpolate="basis")
+        .encode(
+            x=alt.X("PT:Q", scale=alt.Scale(type="log"), title="Ratio PT/kD"),
+            y=alt.Y("y:Q", title="Fraction of total"),
+            color="species:N",
+        )
+        .properties(width="container")
+    )
+
+    # Draw points on the line, and highlight based on selection
+    points = (
+        line.mark_point()
+        .encode(opacity=alt.condition(nearest, alt.value(1), alt.value(0)))
+        .properties(width="container")
+    )
+
+    # Draw a rule at the location of the selection
+    rules = (
+        alt.Chart(source)
+        .transform_pivot("species", value="y", groupby=["PT"])
+        .mark_rule(color="black")
+        .encode(
+            x="PT:Q",
+            opacity=alt.condition(nearest, alt.value(0.3), alt.value(0)),
+            tooltip=[
+                alt.Tooltip(c, type="quantitative", format=".2f") for c in df.columns
+            ],
+        )
+        .add_params(nearest)
+        .properties(width="container")
+    )
+
+    # Put the five layers into a chart and bind the data
+    chart = (
+        alt.layer(line, points, rules)
+        .properties(height=300)
+        .configure(autosize="fit-x")
+    )
+
+    return chart
+
+
+md = """
+This app calculates monomer and dimer concentrations given a total amount of protomer PT and the 
+dissociation constant KD. More info on how and why can be found [HuggingFace](https://huggingface.co/spaces/Jhsmit/binding-kinetics) (right click, open new tab).
+"""
+
+
+@solara.component
+def Page():
+    solara.Style(Path("style.css"))
+
+    dark_effective = solara.lab.use_dark_effective()
+    if dark_effective is True:
+        alt.themes.enable("dark")
+
+    elif dark_effective is False:
+        alt.themes.enable("default")
+
+    PT = solara.use_reactive(10.0)
+    kD = solara.use_reactive(1.0)
+
+    vmin = solara.use_reactive(-1)
+    vmax = solara.use_reactive(3)
+
+    ans = {k: ld(PT.value, kD.value) for k, ld in lambdas.items()}
+
+    solara.Title("Dimerization Kinetics")
+    with solara.Card("Calculate concentrations from kD"):
+        solara.Markdown(md)
+        with solara.GridFixed(columns=2):
+            with solara.Tooltip("Total protomer concentration"):
+                solara.InputFloat("PT", value=PT)
+            with solara.Tooltip("Dissociation constant"):
+                solara.InputFloat("kD", value=kD)
+            solara.Markdown(f"### Concentration monomer: {ans[P1]:.2f}")
+            solara.Markdown(f"### Concentration dimer: {ans[P2]:.2f}")
+
+    # create a vector of PT values ranging from 0.1 times kD to 1000 times kD
+    def update():
+        PT_values = np.logspace(vmin.value, vmax.value, endpoint=True, num=100)
+        ans = {
+            k: ld(PT_values, 1) / ld_total(PT_values, 1) for k, ld in lambdas.items()
+        }
+
+        # put the results in a dataframe, together with input PT values
+        df = pd.DataFrame(dict(PT=PT_values) | {k.name: v for k, v in ans.items()})
+        return make_chart(df)
+
+    chart = solara.use_memo(update, [vmin.value, vmax.value])
+
+    with solara.Card("Fraction monomer/dimer vs ratio over kD"):
+        with solara.Row():
+            with solara.Tooltip("X axis lower limit (log10)"):
+                solara.InputFloat("xmin", value=vmin)
+            with solara.Tooltip("X axis upper limit (log10)"):
+                solara.InputFloat("xmax", value=vmax)
+        solara.HTML(tag="div", style="height: 10px")
+        solara.FigureAltair(chart)