Merge pull request #412 from zfit/precommit_hooks2

.pre-commit-config.yaml

@@ -3,12 +3,12 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.2.0
+    rev: v4.3.0
     hooks:
       - id: check-added-large-files
         args: [ '--maxkb=1000' ]
       - id: check-case-conflict
-      #      - id: check-merge-conflict
+      - id: check-merge-conflict
       - id: check-symlinks
       - id: check-yaml
       - id: check-toml
@@ -18,7 +18,7 @@ repos:
       - id: requirements-txt-fixer
       - id: trailing-whitespace
       - id: detect-private-key
-      #          - id: name-tests-test
+#      - id: name-tests-test  # TODO: rename tests correctly?
       - id: fix-byte-order-marker
       - id: check-ast
   - repo: local
@@ -46,11 +46,11 @@ repos:
       - id: python-use-type-annotations
       - id: python-check-mock-methods
       - id: python-no-eval
-      #          - id: rst-backticks
+      - id: rst-backticks
       - id: rst-directive-colons
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v2.32.0
+    rev: v2.34.0
     hooks:
       - id: pyupgrade
         args: [ --py37-plus ]
@@ -90,8 +90,15 @@ repos:
       - id: upgrade-type-hints
         args: [ '--futures=true' ]
 
+  - repo: https://github.com/shssoichiro/oxipng
+    rev: acdd66b4c5a5fda8b08281ad9b3216327b6847b4
+    hooks:
+      - id: oxipng
+
+
+
   - repo: https://github.com/ambv/black
-    rev: 22.3.0
+    rev: 22.6.0
     hooks:
       - id: black
         language_version: python3

CHANGELOG.rst

@@ -9,10 +9,10 @@ Develop
 
 Major Features and Improvements
 -------------------------------
-- improved data handling in constructors `from_pandas` (which allows now to
+- improved data handling in constructors ``from_pandas`` (which allows now to
   have weights as columns, dataframes that are a superset of the obs) and
-  `from_root` (obs can now be spaces and therefore cuts can be direcly applied)
-- add hashing of unbinned datasets with a `hashint` attribute. None if no hash was possible.
+  ``from_root`` (obs can now be spaces and therefore cuts can be direcly applied)
+- add hashing of unbinned datasets with a ``hashint`` attribute. None if no hash was possible.
 
 Breaking changes
 ------------------
@@ -47,14 +47,14 @@ Thanks
 
 Major Features and Improvements
 -------------------------------
-- Save results by pickling, unpickling a frozen (`FitResult.freeze()`) result and using
-  `zfit.param.set_values(params, result)` to set the values of `params`.
+- Save results by pickling, unpickling a frozen (``FitResult.freeze()``) result and using
+  ``zfit.param.set_values(params, result)`` to set the values of ``params``.
 
 
 
 Deprecations
 -------------
-- the default name of the uncertainty methods `hesse` and `errors` depended on
+- the default name of the uncertainty methods ``hesse`` and ``errors`` depended on
   the method used (such as 'minuit_hesse', 'zfit_errors' etc.) and would be the exact method name.
   New names are now 'hesse' and 'errors', independent of the method used. This reflects better that the
   methods, while internally different, produce the same result.
@@ -86,22 +86,22 @@ Major Features and Improvements
   binned losses. TODO: extend to include changes/point to binned introduction.
 - new Poisson PDF
 - added Poisson constraint, LogNormal Constraint
-- Save results by pickling, unpickling a frozen (`FitResult.freeze()`) result and using
-  `zfit.param.set_values(params, result)` to set the values of `params`.
+- Save results by pickling, unpickling a frozen (``FitResult.freeze()``) result and using
+  ``zfit.param.set_values(params, result)`` to set the values of ``params``.
 
 Breaking changes
 ------------------
 
 - params given in ComposedParameters are not sorted anymore. Rely on their name instead.
-- `norm_range` is now called `norm` and should be replaced everywhere if possible. This will break in
+- ``norm_range`` is now called ``norm`` and should be replaced everywhere if possible. This will break in
   the future.
 
 Deprecation
 -------------
 
 Bug fixes and small changes
 ---------------------------
-- remove warning when using `rect_limits` or similar.
+- remove warning when using ``rect_limits`` or similar.
 - gauss integral accepts now also tensor inputs in limits
 - parameters at limits is now shown correctly
 
@@ -134,7 +134,7 @@ Bug fixes and small changes
 Major Features and Improvements
 -------------------------------
 
-- allow `FitResult` to `freeze()`, making it pickleable. The parameters
+- allow ``FitResult`` to ``freeze()``, making it pickleable. The parameters
   are replaced by their name, the objects such as loss and minimizer as well.
 - improve the numerical integration by adding a one dimensional efficient integrator, testing for the accuracy of
   multidimensional integrals. If there is a sharp peak, this maybe fails to integrate and the number of points
@@ -156,7 +156,7 @@ Breaking changes
 ------------------
 - the numerical integration improved with more sensible values for tolerance. This means however that some fits will
   greatly increase the runtime. To restore the old behavior globally, do
-  for each instance `pdf.update_integration_options(draws_per_dim=40_000, max_draws=40_000, tol=1)`
+  for each instance ``pdf.update_integration_options(draws_per_dim=40_000, max_draws=40_000, tol=1)``
   This will integrate regardless of the chosen precision and it may be non-optimal.
   However, the precision estimate in the integrator is also not perfect and maybe overestimates the error, so that
   the integration by default takes longer than necessary. Feel free to play around with the parameters and report back.
@@ -187,7 +187,7 @@ Thanks
 
 Bug fixes and small changes
 ---------------------------
-- fix wrong arguments to `minimize`
+- fix wrong arguments to ``minimize``
 - make BaseMinimizer arguments optional
 
 0.7.1 (6. July 2021)
@@ -210,7 +210,7 @@ Major Features and Improvements
 
 Bug fixes and small changes
 ---------------------------
-- Scipy minimizers with hessian arguments use now `BFGS` as default
+- Scipy minimizers with hessian arguments use now ``BFGS`` as default
 
 
 Requirement changes
@@ -230,8 +230,8 @@ Update ipyopt requirement < 0.12 to allow numpy compatible with TensorFlow
 ==================
 
 - hotfix for wrong argument in exponential PDF
-- removed requirement ipyopt, can be installed with `pip install zfit[ipyopt]`
-  or by manually installing `pip install ipyopt`
+- removed requirement ipyopt, can be installed with ``pip install zfit[ipyopt]``
+  or by manually installing ``pip install ipyopt``
 
 
 
@@ -306,7 +306,7 @@ Major Features and Improvements
 - Completely new and overhauled minimizers design, including:
 
   - minimizers can now be used with arbitrary Python functions and an initial array independent of zfit
-  - a minimization can be 'continued' by passing `init` to `minimize`
+  - a minimization can be 'continued' by passing ``init`` to ``minimize``
   - more streamlined arguments for minimizers, harmonized names and behavior.
   - Adding a flexible criterion (currently EDM) that will terminate the minimization.
   - Making the minimizer fully stateless.
@@ -315,10 +315,10 @@ Major Features and Improvements
 
 - Major overhaul of the ``FitResult``, including:
 
-  - improved `zfit_error` (equivalent of `MINOS`)
-  - `minuit_hesse` and `minuit_minos` are now available with all minimizers as well thanks to an great
+  - improved ``zfit_error`` (equivalent of ``MINOS``)
+  - ``minuit_hesse`` and ``minuit_minos`` are now available with all minimizers as well thanks to an great
     improvement in iminuit.
-  - Added an `approx` hesse that returns the approximate hessian (if available, otherwise empty)
+  - Added an ``approx`` hesse that returns the approximate hessian (if available, otherwise empty)
 
 - upgrade to iminuit v2 changes the way it works and also the Minuit minimizer in zfit,
   including a new step size heuristic.
@@ -330,11 +330,11 @@ Major Features and Improvements
 
 Breaking changes
 ------------------
-- NLL (and extended) subtracts now by default a constant value. This can be changed with a new `options` argument.
+- NLL (and extended) subtracts now by default a constant value. This can be changed with a new ``options`` argument.
   COMPARISON OF DIFFEREN NLLs (their absolute values) fails now! (flag can be deactivated)
 - BFGS (from TensorFlow Probability) has been removed as it is not working properly. There are many alternatives
   such as ScipyLBFGSV1 or NLoptLBFGSV1
-- Scipy (the minimizer) has been removed. Use specialized `Scipy*` minimizers instead.
+- Scipy (the minimizer) has been removed. Use specialized ``Scipy*`` minimizers instead.
 - Creating a ``zfit.Parameter``, usign ``set_value`` or ``set_values`` now raises a ``ValueError``
   if the value is outside the limits. Use ``assign`` to suppress it.
 
@@ -348,7 +348,7 @@ Bug fixes and small changes
 - FFTconv was shifted if the kernel limits were not symetrical, now properly taken into account.
 - circumvent overflow error in sampling
 - shuffle samples from sum pdfs to ensure uniformity and remove conv sampling bias
-- `create_sampler` now samples immediately to allow for precompile, a new hook that will allow objects to optimize
+- ``create_sampler`` now samples immediately to allow for precompile, a new hook that will allow objects to optimize
   themselves.
 
 

docs/conf.py

@@ -58,9 +58,9 @@
 # releases_github_path = "zfit/zfit"  # TODO: use releases or similar?
 # releases_document_name = "../CHANGELOG.rst"
 
-jupyter_execute_notebooks = "force"  # use if needed and cache should be ignored
-# jupyter_execute_notebooks = "cache"
-if jupyter_execute_notebooks == "cache":
+nb_execution_mode = "force"  # use if needed and cache should be ignored
+# nb_execution_mode = "cache"
+if nb_execution_mode == "cache":
     jupyter_cache_path = project_dir.joinpath("docs", ".cache", "myst-nb")
     jupyter_cache_path.mkdir(parents=True, exist_ok=True)
     jupyter_cache = str(jupyter_cache_path)
@@ -83,7 +83,7 @@
 atexit.register(lambda path=zfit_tutorials_path: shutil.rmtree(path))
 pygit2.clone_repository("https://github.com/zfit/zfit-tutorials", zfit_tutorials_path)
 
-execution_in_temp = True
+nb_execution_in_temp = True
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]

docs/tutorials/components/intro/space.rst

@@ -42,7 +42,7 @@ with or without limits.
 
 **Limit** The range on a certain axis. Typically defines an interval. In fact, there are two times of limits:
  * **rectangular**: This type is the usual limit as e.g. ``(-2, 5)`` for a simple, 1 dimensional interval. It is
-   rectangular. This can either be given as ``limits`` of a :py:class:`~zfit.Space` or as `rect_limits``.
+   rectangular. This can either be given as ``limits`` of a :py:class:`~zfit.Space` or as ``rect_limits``.
  * **functional**: In order to define arbitrary limits, a function can be used that receives a tensor-like
    object ``x`` and returns ``True`` on every position that is inside the limits, ``False`` for every value outside.
    When a functional limit is given, rectangular limits that contain the functional limit as a subset **must** be
@@ -80,7 +80,7 @@ to be combined with ``product`` has to be specified in the same space.
 
     data = zfit.Data.from_numpy(obs=combined_obs, ...)
 
-Now we have a :py:class:`~zfit.Data` object that is defined in the same domain as `product``
+Now we have a :py:class:`~zfit.Data` object that is defined in the same domain as ``product``
 and can be used to build a loss function.
 
 Limits
@@ -99,9 +99,9 @@ two intervals) or combination (increase the dimensionality) are also possible.
 
     added_limits = simple_limit1 + simple_limit2
 
-In this case, ``added_limits`` is now a :py:class:`zfit.Space` with observable `'obs1'`` defined in the intervals
+In this case, ``added_limits`` is now a :py:class:`zfit.Space` with observable ``obs1'`` defined in the intervals
 (-5, 1) and (3, 7.5). This can be useful, *e.g.*, when fitting in two regions.
-An example of the product of different :py:class:`zfit.Space` instances has been shown before as `combined_obs``.
+An example of the product of different :py:class:`zfit.Space` instances has been shown before as ``combined_obs``.
 
 Functional limits
 '''''''''''''''''

requirements_dev.txt

@@ -11,10 +11,10 @@ mplhep  # docs
 myst-nb
 pip>=9.0.1
 pre-commit
+progressbar2
 pydata-sphinx-theme>=0.6.2  # fix for Jinja
 pygit2
 pyhf  # for comparison of correctness
-pyhf
 pytest-benchmark
 pyyaml
 seed_intersphinx_mapping
@@ -28,7 +28,7 @@ sphinx-copybutton
 sphinx-panels
 sphinxcontrib-bibtex
 sphinxcontrib-images
-sphinxcontrib-youtube>=1.0.0
+sphinxcontrib-youtube>=1.0.0,<1.2.0
 tox>=2.9.1
 twine>=1.10.0
 watchdog>=0.8.3

utils/api/argdocs.yaml

@@ -37,19 +37,19 @@ pdf.init.norm: |1+
 
 pdf.pdf.norm: |1+
   Normalization of the function.
-                By default, this is the `norm` of the PDF (which by default is the same as
+                By default, this is the ``norm`` of the PDF (which by default is the same as
                 the space of the PDF).
 
 pdf.init.extended: |1+
   The overall yield of the PDF.
                 If this is parameter-like, it will be used as the yield,
                 the expected number of events, and the PDF will be extended.
                 An extended PDF has additional functionality, such as the
-                `ext_*` methods and the `counts` (for binned PDFs).
+                ``ext_*`` methods and the ``counts`` (for binned PDFs).
 
 
 pdf.init.extended.auto: |1+
-                If `True`,
+                If ``True``,
                 the PDF will be extended automatically if the PDF is extended
                 using the total number of events in the histogram.
                 This is the default.
@@ -61,11 +61,11 @@ pdf.integrate.limits: |1+
 pdf.integrate.norm: |1+
   Normalization of the integration.
                 By default, this is the same as the default space of the PDF.
-                `False` means no normalization and returns the unnormed integral.
+                ``False`` means no normalization and returns the unnormed integral.
 pdf.integrate.options: |1+
   Options for the integration.
                 Additional options for the integration. Currently supported options are:
-                - type: one of (`bins`)
+                - type: one of (``bins``)
                   This hints that bins are integrated. A method that is vectorizable, non-dynamic and
                   therefore less suitable for complicated functions is chosen.
 
@@ -80,7 +80,7 @@ pdf.sample.limits: |1+
 
 pdf.binned.counts.x: |1+
   Data for the binned PDF.
-                The returned counts correspond to the binned axis in `x`.
+                The returned counts correspond to the binned axis in ``x``.
 
 pdf.binned.counts.norm: |1+
   Normalization of the counts.
@@ -295,14 +295,14 @@ minimizer.criterion: |1+
                     stopps and it is assumed that the minimum
                     has been found.
 minimizer.strategy: |1+
-    A class of type `ZfitStrategy` that takes no
+    A class of type ``ZfitStrategy`` that takes no
                     input arguments in the init. Determines the behavior of the minimizer in
                     certain situations, most notably when encountering
                     NaNs. It can also implement a callback function.
 minimizer.maxiter: |1+
     Approximate number of iterations.
                     This corresponds to roughly the maximum number of
-                    evaluations of the `value`, 'gradient` or `hessian`.
+                    evaluations of the ``value``, 'gradient`` or ``hessian``.
 minimizer.name: |1+
     Human-readable name of the minimizer.
 minimizer.maxcor: |1+
@@ -411,7 +411,7 @@ minimizer.nlopt.info: |1+
 
 loss.binned.init.model: |1+
    Binned PDF(s) that return the normalized probability
-                (`rel_counts` or `counts`) for
+                (``rel_counts`` or ``counts``) for
                 *data* under the given parameters.
                 If multiple model and data are given, they will be used
                 in the same order to do a simultaneous fit.
@@ -460,7 +460,7 @@ loss.init.explain.extendedterm: |1+
 
          and the extended likelihood is the product of both.
 loss.init.explain.simultaneous: |1+
-    A simultaneous fit can be performed by giving one or more `model`, `data`, to the loss. The
+    A simultaneous fit can be performed by giving one or more ``model``, ``data``, to the loss. The
          length of each has to match the length of the others
 
          .. math::
@@ -521,12 +521,12 @@ loss.init.options: |1+
                   value of the NLL is meaningless. However,
                   with this switch on, one cannot directly compare
                   different likelihoods ablolute value as the constant
-                  may differ! Use `create_new` in order to have a comparable likelihood
+                  may differ! Use ``create_new`` in order to have a comparable likelihood
                   between different losses
 
 
                 These settings may extend over time. In order to make sure that a loss is the
-                same under the same data, make sure to use `create_new` instead of instantiating
+                same under the same data, make sure to use ``create_new`` instead of instantiating
                 a new loss as the former will automatically overtake any relevant constants
                 and behavior.
 
@@ -543,10 +543,10 @@ result.init.params: |1+
                 value from the minimum found by the minimizer.
 
 result.init.minimizer: |1+
-    Minimizer that was used to obtain this `FitResult` and will be used to
+    Minimizer that was used to obtain this ``FitResult`` and will be used to
                     calculate certain errors. If the minimizer
                     is state-based (like "iminuit"), then this is a copy
-                    and the state of other `FitResults` or of the *actual*
+                    and the state of other ``FitResults`` or of the *actual*
                     minimizer that performed the minimization
                     won't be altered.
 result.init.valid: |1+