starting on syntax refactor

Makefile

@@ -1,6 +1,6 @@
 # Makefile
 
-VERSION=1.0.1
+VERSION=1.1.0
 
 FORCE:
 	make install

README.md

@@ -19,7 +19,7 @@ import jax
 import jsmfsb
 
 lvmod = jsmfsb.models.lv()
-step = lvmod.stepGillespie()
+step = lvmod.step_gillespie()
 k0 = jax.random.key(42)
 out = jsmfsb.simTs(k0, lvmod.m, 0, 30, 0.1, step)
 assert(out.shape == (300, 2))
@@ -38,7 +38,7 @@ fig.savefig("lv.pdf")
 
 The API for this package is very similar to that of the `smfsb` package. The main difference is that non-deterministic (random) functions have an extra argument (typically the first argument) that corresponds to a JAX random number key. See the [relevant section](https://jax.readthedocs.io/en/latest/random-numbers.html) of the JAX documentation for further information regarding random numbers in JAX code.
 
-For further information, see the [demo directory](https://github.com/darrenjw/jax-smfsb/tree/main/demos) and the [API documentation](https://jax-smfsb.readthedocs.io/en/latest/index.html). Within the demos directory, see [shbuild.py](https://github.com/darrenjw/jax-smfsb/tree/main/demos/shbuild.py) for an example of how to specify a (SEIR epidemic) model using SBML-shorthand and [stepCLE2Df.py](https://github.com/darrenjw/jax-smfsb/tree/main/demos/stepCLE2Df.py) for a 2-d reaction-diffusion simulation. For parameter inference (from time course data), see [abc-cal.py](https://github.com/darrenjw/jax-smfsb/tree/main/demos/abc-cal.py) for ABC inference, [abcSmc.py](https://github.com/darrenjw/jax-smfsb/tree/main/demos/abcSmc.py) for ABC-SMC inference and [pmmh.py](https://github.com/darrenjw/jax-smfsb/tree/main/demos/pmmh.py) for particle marginal Metropolis-Hastings MCMC-based inference. There are many other demos besides these.
+For further information, see the [demo directory](https://github.com/darrenjw/jax-smfsb/tree/main/demos) and the [API documentation](https://jax-smfsb.readthedocs.io/en/latest/index.html). Within the demos directory, see [shbuild.py](https://github.com/darrenjw/jax-smfsb/tree/main/demos/shbuild.py) for an example of how to specify a (SEIR epidemic) model using SBML-shorthand and [step_cle2Df.py](https://github.com/darrenjw/jax-smfsb/tree/main/demos/step_cle2Df.py) for a 2-d reaction-diffusion simulation. For parameter inference (from time course data), see [abc-cal.py](https://github.com/darrenjw/jax-smfsb/tree/main/demos/abc-cal.py) for ABC inference, [abcSmc.py](https://github.com/darrenjw/jax-smfsb/tree/main/demos/abcSmc.py) for ABC-SMC inference and [pmmh.py](https://github.com/darrenjw/jax-smfsb/tree/main/demos/pmmh.py) for particle marginal Metropolis-Hastings MCMC-based inference. There are many other demos besides these.
 
 You can view this package on [GitHub](https://github.com/darrenjw/jax-smfsb) or [PyPI](https://pypi.org/project/jsmfsb/).
 

demos/abc-cal.py

@@ -20,7 +20,7 @@ def rpr(k):
 
 def rmod(k, th):
   return jsmfsb.simTs(k, jnp.array([50.0, 100.0]), 0, 30, 2,
-                     jsmfsb.models.lv(th).stepCLE(0.1))
+                     jsmfsb.models.lv(th).step_cle(0.1))
 
 def ss1d(vec):
   n = len(vec)

demos/abc.py

@@ -19,7 +19,7 @@ def rpr(k):
 
 def rmod(k, th):
   return jsmfsb.simTs(k, jnp.array([50.0, 100.0]), 0, 30, 2,
-                     jsmfsb.models.lv(th).stepCLE(0.1))
+                     jsmfsb.models.lv(th).step_cle(0.1))
 
 def sumStats(dat):
   return dat

demos/abcSmc.py

@@ -45,7 +45,7 @@ def dpr(th):
 
 def rmod(k, th):
   return jsmfsb.simTs(k, jnp.array([50.0, 100]), 0, 30, 2,
-                     jsmfsb.models.lv(jnp.exp(th)).stepCLE(0.1))
+                     jsmfsb.models.lv(jnp.exp(th)).step_cle(0.1))
 
 print("Pilot run...")
 

demos/lv.py

@@ -12,11 +12,11 @@
 import jsmfsb
 
 lvmod = jsmfsb.models.lv()
-step = lvmod.stepGillespie()
+step = lvmod.step_gillespie()
 k0 = jax.random.key(42)
 print(step(k0, lvmod.m, 0, 30))
 
-stepC = lvmod.stepCLE(0.01)
+stepC = lvmod.step_cle(0.01)
 print(stepC(k0, lvmod.m, 0, 30))
 
 out = jsmfsb.simSample(k0, 10000, lvmod.m, 0, 30, stepC)

demos/m-bd.py

@@ -11,7 +11,7 @@
 import jsmfsb
 
 bdmod = jsmfsb.models.bd()
-step = bdmod.stepGillespie()
+step = bdmod.step_gillespie()
 k0 = jax.random.key(42)
 print(step(k0, bdmod.m, 0, 30))
 

demos/m-dimer.py

@@ -11,7 +11,7 @@
 import jsmfsb
 
 dimermod = jsmfsb.models.dimer()
-step = dimermod.stepGillespie()
+step = dimermod.step_gillespie()
 k0 = jax.random.key(42)
 print(step(k0, dimermod.m, 0, 30))
 

demos/m-id.py

@@ -11,7 +11,7 @@
 import jsmfsb
 
 idmod = jsmfsb.models.id()
-step = idmod.stepGillespie()
+step = idmod.step_gillespie()
 k0 = jax.random.key(42)
 print(step(k0, idmod.m, 0, 30))
 

demos/m-lv-cle.py

@@ -11,7 +11,7 @@
 import jsmfsb
 
 lvmod = jsmfsb.models.lv()
-step = lvmod.stepCLE()
+step = lvmod.step_cle()
 k0 = jax.random.key(42)
 print(step(k0, lvmod.m, 0, 30))
 

demos/m-lv-euler.py

@@ -11,7 +11,7 @@
 import jsmfsb
 
 lvmod = jsmfsb.models.lv()
-step = lvmod.stepEuler(0.001)
+step = lvmod.step_euler(0.001)
 k0 = jax.random.key(42)
 print(step(k0, lvmod.m, 0, 30))
 

demos/m-lv-pts.py

@@ -11,7 +11,7 @@
 import jsmfsb
 
 lvmod = jsmfsb.models.lv()
-step = lvmod.stepPTS()
+step = lvmod.step_poisson()
 k0 = jax.random.key(42)
 print(step(k0, lvmod.m, 0, 30))
 

demos/m-lv.py

@@ -11,7 +11,7 @@
 import jsmfsb
 
 lvmod = jsmfsb.models.lv()
-step = lvmod.stepGillespie()
+step = lvmod.step_gillespie()
 k0 = jax.random.key(42)
 print(step(k0, lvmod.m, 0, 30))
 

demos/m-mm.py

@@ -11,7 +11,7 @@
 import jsmfsb
 
 mmmod = jsmfsb.models.mm()
-step = mmmod.stepGillespie()
+step = mmmod.step_gillespie()
 k0 = jax.random.key(42)
 print(step(k0, mmmod.m, 0, 30))
 

demos/m-sir.py

@@ -11,7 +11,7 @@
 import jsmfsb
 
 sirmod = jsmfsb.models.sir()
-step = sirmod.stepGillespie()
+step = sirmod.step_gillespie()
 k0 = jax.random.key(42)
 print(step(k0, sirmod.m, 0, 30))
 

demos/pfMLLik.py

@@ -14,8 +14,8 @@ def simX(k, t0, th):
     return jnp.array([jax.random.poisson(k1, 50),
                       jax.random.poisson(k2, 100)]).astype(jnp.float32)
 def step(k, x, t, dt, th):
-    sf = jsmfsb.models.lv(th).stepCLE(0.1)
-    #sf = jsmfsb.models.lv(th).stepGillespie()
+    sf = jsmfsb.models.lv(th).step_cle(0.1)
+    #sf = jsmfsb.models.lv(th).step_gillespie()
     return sf(k, x, t, dt)
 mll = jsmfsb.pfMLLik(100, simX, 0, step, obsll, jsmfsb.data.LVnoise10)
 

demos/pmmh.py

@@ -18,8 +18,8 @@ def simX(k, t0, th):
     return jnp.array([jax.random.poisson(k1, 50),
                       jax.random.poisson(k2, 100)]).astype(jnp.float32)
 def step(k, x, t, dt, th):
-    #sf = jsmfsb.models.lv(th).stepGillespie()
-    sf = jsmfsb.models.lv(th).stepCLE(0.1)
+    #sf = jsmfsb.models.lv(th).step_gillespie()
+    sf = jsmfsb.models.lv(th).step_cle(0.1)
     return sf(k, x, t, dt)
 mll = jsmfsb.pfMLLik(100, simX, 0, step, obsll, jsmfsb.data.LVnoise10)
 

demos/shbuild.py

@@ -32,7 +32,7 @@
 """
 
 seir = jsmfsb.sh2Spn(seirSH)
-stepSeir = seir.stepGillespie()
+stepSeir = seir.step_gillespie()
 k0 = jax.random.key(42)
 out = jsmfsb.simTs(k0, seir.m, 0, 40, 0.05, stepSeir)
 

demos/stepCLE1D.py

@@ -11,7 +11,7 @@
 x0 = jnp.zeros((2,N))
 lv = jsmfsb.models.lv()
 x0 = x0.at[:,int(N/2)].set(lv.m)
-stepLv1d = lv.stepCLE1D(jnp.array([0.6, 0.6]))
+stepLv1d = lv.step_cle1D(jnp.array([0.6, 0.6]))
 k0 = jax.random.key(42)
 x1 = stepLv1d(k0, x0, 0, 1)
 print(x1)
@@ -22,7 +22,7 @@
 for i in range(2):
     axis.imshow(out[i,:,:])
     axis.set_title(lv.n[i])
-    fig.savefig(f"stepCLE1D{i}.pdf")
+    fig.savefig(f"step_cle1D{i}.pdf")
 
 
 # eof

demos/stepCLE2D.py

@@ -12,15 +12,15 @@
 x0 = jnp.zeros((2,M,N))
 lv = jsmfsb.models.lv()
 x0 = x0.at[:,int(M/2),int(N/2)].set(lv.m)
-stepLv2d = lv.stepCLE2D(jnp.array([0.6, 0.6]))
+stepLv2d = lv.step_cle2D(jnp.array([0.6, 0.6]))
 k0 = jax.random.key(42)
 x1 = stepLv2d(k0, x0, 0, T)
 
 fig, axis = plt.subplots()
 for i in range(2):
     axis.imshow(x1[i,:,:])
     axis.set_title(lv.n[i])
-    fig.savefig(f"stepCLE2D{i}.pdf")
+    fig.savefig(f"step_cle2D{i}.pdf")
 
 
 

demos/stepCLE2Df.py

@@ -12,15 +12,15 @@
 x0 = jnp.zeros((2,M,N))
 lv = jsmfsb.models.lv()
 x0 = x0.at[:,int(M/2),int(N/2)].set(lv.m)
-stepLv2d = lv.stepCLE2D(jnp.array([0.6, 0.6]), 0.1)
+stepLv2d = lv.step_cle2D(jnp.array([0.6, 0.6]), 0.1)
 k0 = jax.random.key(42)
 x1 = stepLv2d(k0, x0, 0, T)
 
 fig, axis = plt.subplots()
 for i in range(2):
     axis.imshow(x1[i,:,:])
     axis.set_title(lv.n[i])
-    fig.savefig(f"stepCLE2Df{i}.pdf")
+    fig.savefig(f"step_cle2Df{i}.pdf")
 
 
 

demos/stepGillespie1D.py

@@ -12,7 +12,7 @@
 lv = jsmfsb.models.lv()
 x0 = x0.at[:,int(N/2)].set(lv.m)
 k0 = jax.random.key(42)
-stepLv1d = lv.stepGillespie1D(jnp.array([0.6, 0.6]))
+stepLv1d = lv.step_gillespie1D(jnp.array([0.6, 0.6]))
 x1 = stepLv1d(k0, x0, 0, 1)
 print(x1)
 out = jsmfsb.simTs1D(k0, x0, 0, T, 1, stepLv1d, True)
@@ -22,7 +22,7 @@
 for i in range(2):
     axis.imshow(out[i,:,:])
     axis.set_title(lv.n[i])
-    fig.savefig(f"stepGillespie1D{i}.pdf")
+    fig.savefig(f"step_gillespie1D{i}.pdf")
 
 
 # eof

demos/stepGillespie2D.py

@@ -12,15 +12,15 @@
 x0 = jnp.zeros((2,M,N))
 lv = jsmfsb.models.lv()
 x0 = x0.at[:,int(M/2),int(N/2)].set(lv.m)
-stepLv2d = lv.stepGillespie2D(jnp.array([0.6, 0.6]))
+stepLv2d = lv.step_gillespie2D(jnp.array([0.6, 0.6]))
 k0 = jax.random.key(42)
 x1 = stepLv2d(k0, x0, 0, T)
 
 fig, axis = plt.subplots()
 for i in range(2):
     axis.imshow(x1[i,:,:])
     axis.set_title(lv.n[i])
-    fig.savefig(f"stepGillespie2D{i}.pdf")
+    fig.savefig(f"step_gillespie2D{i}.pdf")
 
 
 # eof

demos/time-lv-cle.py

@@ -12,7 +12,7 @@
 import time
 
 lvmod = jsmfsb.models.lv()
-step = lvmod.stepCLE(0.01)
+step = lvmod.step_cle(0.01)
 k0 = jax.random.key(42)
 
 ## Start timer

demos/time-lv-gillespie.py

@@ -12,7 +12,7 @@
 import time
 
 lvmod = jsmfsb.models.lv()
-step = lvmod.stepGillespie()
+step = lvmod.step_gillespie()
 k0 = jax.random.key(42)
 
 ## Start timer

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "jsmfsb"
-version = "1.0.1"
+version = "1.1.0"
 authors = [
   { name="Darren Wilkinson", email="[email protected]" },
 ]

src/jsmfsb/inference.py

@@ -165,7 +165,7 @@ def pfMLLik(n, simX0, t0, stepFun, dataLLik, data, debug=False):
     >>>              jax.random.poisson(k2, 100)]).astype(jnp.float32)
     >>> 
     >>> def step(key, x, t, dt, th):
-    >>>     sf = jsmfsb.models.lv(th).stepGillespie()
+    >>>     sf = jsmfsb.models.lv(th).step_gillespie()
     >>>     return sf(key, x, t, dt)
     >>> 
     >>> mll = jsmfsb.pfMLLik(80, simX, 0, step, obsll, jsmfsb.data.LVnoise10)

src/jsmfsb/models.py

@@ -25,7 +25,7 @@ def bd(th=[1, 1.1]):
     >>> import jsmfsb
     >>> import jax
     >>> bd = jsmfsb.models.bd()
-    >>> step = bd.stepGillespie()
+    >>> step = bd.step_gillespie()
     >>> k = jax.random.key(42)
     >>> jsmfsb.simTs(k, bd.m, 0, 50, 0.1, step)
     """
@@ -53,7 +53,7 @@ def dimer(th=[0.00166, 0.2]):
     >>> import jsmfsb
     >>> import jax
     >>> dimer = jsmfsb.models.dimer()
-    >>> step = dimer.stepGillespie()
+    >>> step = dimer.step_gillespie()
     >>> k = jax.random.key(42)
     >>> jsmfsb.simTs(k, dimer.m, 0, 50, 0.1, step)
     """
@@ -82,7 +82,7 @@ def id(th=[1, 0.1]):
     >>> import smfsb
     >>> import jax
     >>> id = jsmfsb.models.id()
-    >>> step = id.stepGillespie()
+    >>> step = id.step_gillespie()
     >>> k = jax.random.key(42)
     >>> jsmfsb.simTs(k, id.m, 0, 50, 0.1, step)
     """
@@ -112,7 +112,7 @@ def lv(th=[1, 0.005, 0.6]):
     >>> import jsmfsb
     >>> import jax
     >>> lv = jsmfsb.models.lv()
-    >>> step = lv.stepGillespie()
+    >>> step = lv.step_gillespie()
     >>> k = jax.random.key(42)
     >>> jsmfsb.simTs(k, lv.m, 0, 50, 0.1, step)
     """
@@ -142,7 +142,7 @@ def mm(th=[0.00166, 1e-4, 0.1]):
     >>> import jsmfsb
     >>> import jax
     >>> mm = jsmfsb.models.mm()
-    >>> step = mm.stepGillespie()
+    >>> step = mm.step_gillespie()
     >>> k = jax.random.key(42)
     >>> jsmfsb.simTs(k, mm.m, 0, 50, 0.1, step)
     """
@@ -173,7 +173,7 @@ def sir(th=[0.0015, 0.1]):
     >>> import jsmfsb
     >>> import jax
     >>> sir = jsmfsb.models.sir()
-    >>> step = sir.stepGillespie()
+    >>> step = sir.step_gillespie()
     >>> k = jax.random.key(42)
     >>> jsmfsb.simTs(k, sir.m, 0, 50, 0.1, step)
     """