Traceback (most recent call last):


 "C:\Python27\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code

exec(code_obj, self.user_global_ns, self.user_ns)


 "<ipython-input-2-a21ce0484562>", line 14, in <module>

sns.stripplot(x='Datetime', y='Event / Element', hue='Element', data=df, orient='h')


 "C:\Python27\lib\site-packages\seaborn\categorical.py", line 2603, in stripplot

plotter.plot(ax, kwargs)


 "C:\Python27\lib\site-packages\seaborn\categorical.py", line 1191, in plot

self.draw_stripplot(ax, kws)


 "C:\Python27\lib\site-packages\seaborn\categorical.py", line 1171, in draw_stripplot

ax.scatter(strip_data, cat_pos, **kws)


 "C:\Python27\lib\site-packages\matplotlib\__init__.py", line 1710, in inner

return func(ax, *args, **kwargs)


 "C:\Python27\lib\site-packages\matplotlib\axes\_axes.py", line 4087, in scatter

offsets = np.column_stack([x, y])


 "C:\Python27\lib\site-packages\numpy\lib\shape_base.py", line 353, in column_stack

return _nx.concatenate(arrays, 1)
TypeError: invalid type promotion

Traceback (most recent call last):


 "spatial_unmixing.py", line 111, in <module>

main()


 "spatial_unmixing.py", line 87, in main

x_res = ms.optimII(x0, lambda x: jax_gradient_spatial_functional(x, Y, K_GT, m, n, lambda_, tau, h), ms.projectVectorsCanonicalSimplex, alpha0, mu, L, restart_nesterov, maxIter, threshold)


 "/Users/xxx/Desktop/xxx/Projects/unmixing/unmixing_example/utilities/msiplib.py", line 675, in optimII

xNew = projC(yOld - 1 / L * fGrad(yOld))


 "spatial_unmixing.py", line 87, in <lambda>

x_res = ms.optimII(x0, lambda x: jax_gradient_spatial_functional(x, Y, K_GT, m, n, lambda_, tau, h), ms.projectVectorsCanonicalSimplex, alpha0, mu, L, restart_nesterov, maxIter, threshold)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/api.py", line 148, in f_jitted

out = xla.xla_call(flat_fun, *args_flat, device=device, backend=backend)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/core.py", line 591, in call_bind

outs = primitive.impl(f, *args, **params)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/interpreters/xla.py", line 418, in _xla_call_impl

compiled_fun = _xla_callable(fun, device, backend, *map(abstractify, args))


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/linear_util.py", line 217, in cached_fun

ans, f_prev = cached_fun_body(f, args)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/linear_util.py", line 214, in cached_fun_body

return call(f, *args), f


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/interpreters/xla.py", line 434, in _xla_callable

jaxpr, (pvals, consts, env) = pe.trace_to_subjaxpr(fun, master, False).call_wrapped(pvals)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/linear_util.py", line 165, in call_wrapped

ans = self.f(*args, **dict(self.params, **kwargs))


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/api.py", line 343, in grad_f

_, g = value_and_grad_f(*args, **kwargs)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/api.py", line 391, in value_and_grad_f

ans, vjp_py = vjp(f_partial, *dyn_args)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/api.py", line 1149, in vjp

out_primal, out_vjp = ad.vjp(flat_fun, primals_flat)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/interpreters/ad.py", line 107, in vjp

out_primals, pvals, jaxpr, consts = linearize(traceable, *primals)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/interpreters/ad.py", line 96, in linearize

jaxpr, out_pvals, consts = pe.trace_to_jaxpr(jvpfun_flat, in_pvals)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/interpreters/partial_eval.py", line 343, in trace_to_jaxpr

jaxpr, (out_pvals, consts, env) = fun.call_wrapped(pvals)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/linear_util.py", line 165, in call_wrapped

ans = self.f(*args, **dict(self.params, **kwargs))


 "/Users/xxx/Desktop/xxx/Projects/unmixing/unmixing_example/jax_unmixing.py", line 24, in spatial_functional_jax

X_imshape = np.reshape(X, (r, m, n), order = 'F')


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/numpy/lax_numpy.py", line 778, in reshape

return a.reshape(newshape, order=order)
# forward to method for ndarrays


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/numpy/lax_numpy.py", line 808, in _reshape_method

return _reshape(a, newshape, order=order)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/jax/numpy/lax_numpy.py", line 784, in _reshape

computed_newshape = onp.reshape(dummy_val, newshape).shape


 "<__array_function__ internals>", line 6, in reshape


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 301, in reshape

return _wrapfunc(a, 'reshape', newshape, order=order)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 70, in _wrapfunc

return _wrapit(obj, method, *args, **kwds)


 "/Users/xxx/anaconda/envs/xxx/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 47, in _wrapit

result = getattr(asarray(obj), method)(*args, **kwds)
TypeError: 'JaxprTracer' object cannot be interpreted as an integer
