def test_run():
c = CRN()
c.r("P1 > m1 + P1", 0.01)
c.r("m1 > ", 0.01)
c.r("P1 + m2 <> PR1", [0.001, 0.001])
c.set_state({"P1": 1})
df = c.run(750)
gradient(df, 'time', 1)
gradient(df, 'time', 2)
plot(df, 'time', c.E)
def test_run():
c = CRN()
c.r("P1 > m1 + P1", 0.01)
c.r("m1 > ", 0.01)
c.r("P1 + m2 <> PR1", [0.001, 0.001])
c.set_state({"P1": 1})
df = c.run(750)
gradient(df, 'time', 1)
gradient(df, 'time', 2)
plot(df, 'time', c.E)
def test_N():
c = CRN()
c.r("A + 2B <> C", [1.0, 2.0])
print(c.E)
print(c.N)
print(c.R)
print(c.P)
c.set_state(dict(C=55))
print(c.x)
print("v(x)")
x = np.ones((3,1))*5
print(c.v(x))
d = c.run(0.001, 20)
print(d)
def test_N():
c = CRN()
c.r("A + 2B <> C", [1.0, 2.0])
print(c.E)
print(c.N)
print(c.R)
print(c.P)
c.set_state(dict(C=55))
print(c.x)
print("v(x)")
x = np.ones((3, 1)) * 5
print(c.v(x))
d = c.run(0.001, 20)
print(d)
