enh: update to zfit 0.20

.github/workflows/ci.yml

@@ -9,7 +9,6 @@ on:
   push:
 
 jobs:
-
   notebooks:
     runs-on: ubuntu-latest
     timeout-minutes: 150
@@ -18,7 +17,7 @@ jobs:
       fail-fast: False
       matrix:
         use-graph: [ 0, 1 ]
-        python-version: [ "3.8", "3.11" ]
+        python-version: [ "3.9", "3.12" ]
     name: Run notebooks, Python ${{ matrix.python-version }}, compiled = ${{ matrix.use-graph }}
     steps:
       - uses: SimenB/github-actions-cpu-cores@v2
@@ -35,8 +34,8 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install pytest-xdist nbval
+          pip install -U -r requirements.txt
           pip install git+https://github.com/zfit/zfit
-          pip install -r requirements.txt
       - name: Run notebooks
         run: |
           ZFIT_DO_JIT=${{ matrix.use-graph }}

TensorFlow/HPC_with_TensorFlow.ipynb

@@ -1527,6 +1527,7 @@
     "    x2 = np.mean(logx)\n",
     "    return x + x1 + x2\n",
     "\n",
+    "\n",
     "calc_np_numba = calc_np  # numba.jit(nopython=True, parallel=True)(calc_np)"
    ]
   },
@@ -1570,6 +1571,7 @@
     "\n",
     "    return x + x1 + x2\n",
     "\n",
+    "\n",
     "calc_tf_func = tf.function(calc_tf, autograph=False)"
    ]
   },
@@ -1698,9 +1700,12 @@
     "        sum_init += x\n",
     "    return sum_init\n",
     "\n",
+    "\n",
     "calc_tf_func2 = tf.function(calc_tf2, autograph=False)\n",
     "\n",
     "# @numba.njit(parallel=True)  # njit is equal to jit(nopython=True), meaning \"compile everything or raise error\"\n",
+    "\n",
+    "\n",
     "def calc_numba2(x, n):\n",
     "    sum_init = np.zeros_like(x)\n",
     "    for i in range(1, n + 1):\n",
@@ -1882,9 +1887,11 @@
     "def true_fn():\n",
     "    return 1.\n",
     "\n",
+    "\n",
     "def false_fn():\n",
     "    return 0.\n",
     "\n",
+    "\n",
     "value = tf.cond(tf.greater(111., 42.), true_fn=true_fn, false_fn=false_fn)"
    ]
   },
@@ -1927,9 +1934,11 @@
     "def cond(x, y):\n",
     "    return x > y\n",
     "\n",
+    "\n",
     "def body(x, y):\n",
     "    return x / 2, y + 1\n",
     "\n",
+    "\n",
     "x, y = tf.while_loop(cond=cond,\n",
     "                     body=body,\n",
     "                     loop_vars=[100., 1.])"
@@ -2013,6 +2022,7 @@
     "def do_map(func, tensor):\n",
     "    return tf.map_fn(func, tensor)\n",
     "\n",
+    "\n",
     "do_map(tf.math.sin, rnd1_big)"
    ]
   },
@@ -2030,6 +2040,7 @@
     "def do_map_vec(func, tensor):\n",
     "    return tf.vectorized_map(func, tensor)\n",
     "\n",
+    "\n",
     "do_map_vec(tf.math.sin, rnd1_big)"
    ]
   },

_unused/Summary.ipynb

@@ -252,9 +252,12 @@
    "source": [
     "# This shortcut function will be available in zfit, but here\n",
     "# we use the core one to highlight the use of tensorflow graphs\n",
+    "\n",
+    "\n",
     "def api_unbinned_nll(pdf, data, norm_range):\n",
     "    return zfit.core.loss.unbinned_nll(pdf.prob(data, norm_range=norm_range))\n",
     "\n",
+    "\n",
     "mu1 = zfit.Parameter(\"mu\", 5.0, 0., 10)\n",
     "sigma1 = zfit.Parameter(\"sigma\", 1, 0.1, 5.)\n",
     "gauss1 = zfit.pdf.Gauss(mu=mu1, sigma=sigma1)\n",

_unused/kstmumu_tutorial.py

@@ -256,9 +256,9 @@ def plot_pdf_data(data, model, title, n_bins=40):
         histtype="step",
     )
     # plot the pdfs
-    y = model.pdf(x).numpy()
-    y_sig = (model.pdfs[0].pdf(x) * model.fracs[0]).numpy()  # notice the frac!
-    y_bkg = (model.pdfs[1].pdf(x) * model.fracs[1]).numpy()  # notice the frac!
+    y = model.pdf(x)
+    y_sig = model.pdfs[0].pdf(x) * model.fracs[0]  # notice the frac!
+    y_bkg = model.pdfs[1].pdf(x) * model.fracs[1]  # notice the frac!
 
     plt.plot(x, y * plot_scaling, label="Sum - Model", linewidth=linewidth * 2)
     plt.plot(

_website/tutorials/TensorFlow/HPC_with_TensorFlow.ipynb

@@ -1527,6 +1527,7 @@
     "    x2 = np.mean(logx)\n",
     "    return x + x1 + x2\n",
     "\n",
+    "\n",
     "calc_np_numba = calc_np  # numba.jit(nopython=True, parallel=True)(calc_np)"
    ]
   },
@@ -1570,6 +1571,7 @@
     "\n",
     "    return x + x1 + x2\n",
     "\n",
+    "\n",
     "calc_tf_func = tf.function(calc_tf, autograph=False)"
    ]
   },
@@ -1698,9 +1700,12 @@
     "        sum_init += x\n",
     "    return sum_init\n",
     "\n",
+    "\n",
     "calc_tf_func2 = tf.function(calc_tf2, autograph=False)\n",
     "\n",
     "# @numba.njit(parallel=True)  # njit is equal to jit(nopython=True), meaning \"compile everything or raise error\"\n",
+    "\n",
+    "\n",
     "def calc_numba2(x, n):\n",
     "    sum_init = np.zeros_like(x)\n",
     "    for i in range(1, n + 1):\n",
@@ -1882,9 +1887,11 @@
     "def true_fn():\n",
     "    return 1.\n",
     "\n",
+    "\n",
     "def false_fn():\n",
     "    return 0.\n",
     "\n",
+    "\n",
     "value = tf.cond(tf.greater(111., 42.), true_fn=true_fn, false_fn=false_fn)"
    ]
   },
@@ -1927,9 +1934,11 @@
     "def cond(x, y):\n",
     "    return x > y\n",
     "\n",
+    "\n",
     "def body(x, y):\n",
     "    return x / 2, y + 1\n",
     "\n",
+    "\n",
     "x, y = tf.while_loop(cond=cond,\n",
     "                     body=body,\n",
     "                     loop_vars=[100., 1.])"
@@ -2013,6 +2022,7 @@
     "def do_map(func, tensor):\n",
     "    return tf.map_fn(func, tensor)\n",
     "\n",
+    "\n",
     "do_map(tf.math.sin, rnd1_big)"
    ]
   },
@@ -2030,6 +2040,7 @@
     "def do_map_vec(func, tensor):\n",
     "    return tf.vectorized_map(func, tensor)\n",
     "\n",
+    "\n",
     "do_map_vec(tf.math.sin, rnd1_big)"
    ]
   },

_website/tutorials/components/05 - Exploring the FitResult.ipynb

@@ -35,15 +35,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "obs = zfit.Space('x', limits=(0, 10))\n",
+    "obs = zfit.Space('x', 0, 10)\n",
     "mu = zfit.Parameter('mu', 5, 0, 10)\n",
     "sigma = zfit.Parameter('sigma', 1, 0, 10)\n",
     "nsig = zfit.Parameter('nsig', 1000, 0, 10000)\n",
-    "gauss_nonext = zfit.pdf.Gauss(obs=obs, mu=mu, sigma=sigma,\n",
-    "                       # extended=nsig  # requires zfit>=0.13\n",
-    "                       )\n",
-    "gauss = gauss_nonext.create_extended(nsig)\n",
-    "\n",
+    "gauss = zfit.pdf.Gauss(obs=obs, mu=mu, sigma=sigma,extended=nsig)\n",
     "data = gauss.sample()\n",
     "print(f\"The sampled data (poisson fluctuated) has {data.nevents} events.\")"
    ]
@@ -319,7 +315,7 @@
    "outputs": [],
    "source": [
     "weighted_data = zfit.Data.from_tensor(obs=obs, tensor=data.value(), weights=znp.random.uniform(0.1, 5, size=(data.nevents,)))\n",
-    "weighted_nll = zfit.loss.UnbinnedNLL(model=gauss_nonext, data=weighted_data)\n",
+    "weighted_nll = zfit.loss.UnbinnedNLL(model=gauss, data=weighted_data)\n",
     "weighted_result = minimizer.minimize(weighted_nll)"
    ]
   },

_website/tutorials/components/20 - Composite Models.ipynb

@@ -33,7 +33,7 @@
    "source": [
     "frac = zfit.Parameter(\"frac_gauss\", 0.5, 0, 1)\n",
     "\n",
-    "obs1 = zfit.Space('obs1', limits=(-5, 5))\n",
+    "obs1 = zfit.Space('obs1',-5, 5)\n",
     "\n",
     "mu1 = zfit.Parameter(\"mu1\", 1.)\n",
     "sigma1 = zfit.Parameter(\"sigma1\", 1.)\n",
@@ -96,7 +96,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "obs2 = zfit.Space('obs2', limits=(-3, 7))\n",
+    "obs2 = zfit.Space('obs2', -3, 7)\n",
     "mu3 = zfit.Parameter(\"mu3\", 1.)\n",
     "sigma3 = zfit.Parameter(\"sigma3\", 1.)\n",
     "gauss3 = zfit.pdf.Gauss(obs=obs2, mu=mu3, sigma=sigma3)  # different obs than above."
@@ -143,8 +143,9 @@
    "source": [
     "## creating an extended PDF\n",
     "\n",
-    "An extended PDF can either be created with the `create_extended(yield_param)` method or with the simple Python\n",
-    "syntax of multiplying a PDF with the parameter."
+    "An extended PDF can be created using the `extended` argument in the initialization.\n",
+    " \n",
+    "Alternatively, an extended PDF from a non-extended PDF can be created with the `create_extended(yield_param)` method."
    ]
   },
   {
@@ -153,7 +154,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "yield1 = zfit.Parameter(\"yield_gauss1\", 100, 0, 1000)"
+    "yield1 = zfit.Parameter(\"yield_gauss1\", 100, 0, 1000)\n",
+    "gauss3_ext = zfit.pdf.Gauss(obs=obs2, mu=mu3, sigma=sigma3, extended=yield1)"
    ]
   },
   {
@@ -170,9 +172,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "gauss3_ext = zfit.pdf.Gauss(obs=obs2, mu=mu3, sigma=sigma3, extended=yield1)"
-   ]
+   "source": []
   }
  ],
  "metadata": {

_website/tutorials/components/30 - Binned models.ipynb

@@ -52,7 +52,7 @@
     "\n",
     "normal_np = np.random.normal(loc=2., scale=3., size=10000)\n",
     "\n",
-    "obs = zfit.Space(\"x\", limits=(-10, 10))\n",
+    "obs = zfit.Space(\"x\", -10, 10)\n",
     "\n",
     "mu = zfit.Parameter(\"mu\", 1., -4, 6)\n",
     "sigma = zfit.Parameter(\"sigma\", 1., 0.1, 10)\n",
@@ -81,7 +81,7 @@
     "obs_bin = zfit.Space(\"x\", binning=binning)\n",
     "\n",
     "data = data_nobin.to_binned(obs_bin)\n",
-    "model = zfit.pdf.BinnedFromUnbinnedPDF(model_nobin, obs_bin)\n",
+    "model = model_nobin.to_binned(obs_bin)\n",
     "loss = zfit.loss.BinnedNLL(model, data)"
    ]
   },
@@ -160,7 +160,7 @@
     "\n",
     "plt.figure()\n",
     "mplhep.histplot(model_hist, density=1, label=\"model\")\n",
-    "mplhep.histplot(data.to_hist(), density=1, label=\"data\")\n",
+    "mplhep.histplot(data, density=1, label=\"data\")\n",
     "plt.legend()\n",
     "plt.title(\"After fit\")"
    ]

_website/tutorials/components/33 - Binned fits.ipynb

@@ -64,7 +64,7 @@
    "source": [
     "normal_np = np.random.normal(loc=2., scale=1.3, size=10000)\n",
     "\n",
-    "obs = zfit.Space(\"x\", limits=(-10, 10))\n",
+    "obs = zfit.Space(\"x\", -10, 10)\n",
     "\n",
     "mu = zfit.Parameter(\"mu\", 1., -4, 6)\n",
     "sigma = zfit.Parameter(\"sigma\", 1., 0.1, 10)\n",
@@ -487,8 +487,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "bkg_hist = zfit.Data.from_numpy(obs=obs, array=np.random.exponential(scale=20, size=100_000) - 10).to_binned(\n",
-    "    obs_binned)\n",
+    "bkg_hist = zfit.Data(np.random.exponential(scale=20, size=100_000) - 10, obs=obs_binned)\n",
     "bkg_hist_m1 = zfit.Data.from_numpy(obs=obs,\n",
     "                                   array=np.random.exponential(scale=35, size=100_000) - 10).to_binned(\n",
     "    obs_binned)\n",
@@ -571,8 +570,8 @@
    "outputs": [],
    "source": [
     "modifier_constraints = zfit.constraint.GaussianConstraint(params=list(modifiers.values()), observation=np.ones(len(modifiers)),\n",
-    "                                   uncertainty=np.ones(len(modifiers)))\n",
-    "# alpha_constraint = zfit.constraint.GaussianConstraint(alpha, 0, 1)"
+    "                                   uncertainty=0.1 * np.ones(len(modifiers)))\n",
+    "alpha_constraint = zfit.constraint.GaussianConstraint(alpha, 0, sigma=1)"
    ]
   },
   {
@@ -581,7 +580,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loss_binned = zfit.loss.ExtendedBinnedNLL(model, data, constraints=modifier_constraints)"
+    "loss_binned = zfit.loss.ExtendedBinnedNLL(model, data, constraints=[modifier_constraints, alpha_constraint])"
    ]
   },
   {