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import streamlit as st

import networkx as nx

import minitorch

from minitorch import MathTest, MathTestVariable

import plotly.graph_objects as go

import inspect

import graph_builder

MyModule = None

minitorch

def render_function(fn):

st.markdown(

"""

```python

%s

```"""

% inspect.getsource(fn)

)

def render_math_sandbox(use_scalar=False):

st.write("# Sandbox for the Math Functions")

if use_scalar:

one, two, red = MathTestVariable._tests()

else:

one, two, red = MathTest._tests()

f_type = st.selectbox("Function Type", ["One Arg", "Two Arg", "Reduce"])

select = {"One Arg": one, "Two Arg": two, "Reduce": red}

fn = st.selectbox("Function", select[f_type], format_func=lambda a: a[0])

name, _, scalar = fn

if f_type == "One Arg":

st.write("### " + name)

render_function(scalar)

st.write("Function f(x)")

xs = [((x / 1.0) - 50.0 + 1e-5) for x in range(1, 100)]

if use_scalar:

ys = [scalar(minitorch.Scalar(p)).data for p in xs]

else:

ys = [scalar(p) for p in xs]

scatter = go.Scatter(mode="lines", x=xs, y=ys)

fig = go.Figure(scatter)

st.write(fig)

if use_scalar:

st.write("Derivative f'(x)")

x_var = [minitorch.Scalar(x) for x in xs]

for x in x_var:

out = scalar(x)

out.backward()

scatter = go.Scatter(mode="lines", x=xs, y=[x.derivative for x in x_var])

fig = go.Figure(scatter)

st.write(fig)

G = graph_builder.GraphBuilder().run(out)

G.graph["graph"] = {"rankdir": "LR"}

st.graphviz_chart(nx.nx_pydot.to_pydot(G).to_string())

if f_type == "Two Arg":

st.write("### " + name)

render_function(scalar)

st.write("Function f(x, y)")

xs = [((x / 1.0) - 50.0 + 1e-5) for x in range(1, 100)]

ys = [((x / 1.0) - 50.0 + 1e-5) for x in range(1, 100)]

if use_scalar:

zs = [

[scalar(minitorch.Scalar(x), minitorch.Scalar(y)).data for x in xs]

for y in ys

]

else:

zs = [[scalar(x, y) for x in xs] for y in ys]

scatter = go.Surface(x=xs, y=ys, z=[zs for y in ys])

fig = go.Figure(scatter)

st.write(fig)

if use_scalar:

a, b = [], []

for x in xs:

oa, ob = [], []

for y in ys:

x1 = minitorch.Scalar(x)

y1 = minitorch.Scalar(y)

out = scalar(x1, y1)

out.backward()

oa.append((x, y, x1.derivative))

ob.append((x, y, y1.derivative))

a.append(oa)

b.append(ob)

st.write("Derivative f'_x(x, y)")

scatter = go.Surface(

x=[[c[0] for c in a2] for a2 in a],

y=[[c[1] for c in a2] for a2 in a],

z=[[c[2] for c in a2] for a2 in a],

)

fig = go.Figure(scatter)

st.write(fig)

st.write("Derivative f'_y(x, y)")

scatter = go.Surface(

x=[[c[0] for c in a2] for a2 in b],

y=[[c[1] for c in a2] for a2 in b],

z=[[c[2] for c in a2] for a2 in b],

)

fig = go.Figure(scatter)

st.write(fig)

if f_type == "Reduce":

st.write("### " + name)

render_function(scalar)

xs = [((x / 1.0) - 50.0 + 1e-5) for x in range(1, 100)]

ys = [((x / 1.0) - 50.0 + 1e-5) for x in range(1, 100)]

scatter = go.Surface(x=xs, y=ys, z=[[scalar([x, y]) for x in xs] for y in ys])

fig = go.Figure(scatter)

st.write(fig)