Brand change (#27)

* rebrand as noggin

* remove pycache files

Author: rsokl

.gitattributes

@@ -1 +1 @@
-src/liveplot/_version.py export-subst
+src/noggin/_version.py export-subst

MANIFEST.in

@@ -1,2 +1,2 @@
 include versioneer.py
-include src/liveplot/_version.py
+include src/noggin/_version.py

README.md

@@ -1,18 +1,18 @@
 [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/rsokl/liveplot/master?filepath=LivePlot_Demo.ipynb)
 
-# liveplot
-Log and plot metrics during train/test time for a neural network (or whatever, really). `liveplot`
-provides convenient i/o functions for saving, loading, and recreating liveplot sessions. It also provides
+# nogging
+Log and plot metrics during train/test time for a neural network (or whatever, really). `nogging`
+provides convenient i/o functions for saving, loading, and recreating nogging sessions. It also provides
 an interface for accessing logged metrics as [xarray data sets](http://xarray.pydata.org/en/stable/index.html). This
-functionality, availabile via `liveplot.xarray`, permits users to seamlessly access their logged metrics as N-dimensional arrays with named axes.
+functionality, availabile via `nogging.xarray`, permits users to seamlessly access their logged metrics as N-dimensional arrays with named axes.
 
-Please consult the [demo notebook](https://github.com/rsokl/LivePlot/blob/master/LivePlot_Demo.ipynb) for a summary of `liveplot`'s functionality.
+Please consult the [demo notebook](https://github.com/rsokl/LivePlot/blob/master/LivePlot_Demo.ipynb) for a summary of `nogging`'s functionality.
 
-![liveplot](https://user-images.githubusercontent.com/29104956/52166468-bf425700-26db-11e9-9324-1fc83d4bc71d.gif)
+![nogging](https://user-images.githubusercontent.com/29104956/52166468-bf425700-26db-11e9-9324-1fc83d4bc71d.gif)
 
 
-## Installing liveplot
-Clone/download this repository, navigate to the `liveplot` directory, and run:
+## Installing nogging
+Clone/download this repository, navigate to the `nogging` directory, and run:
 ```shell
 python setup.py install
 ```
@@ -21,7 +21,7 @@ python setup.py install
 ### Creating a live plot
 ```python
 import numpy as np
-from liveplot import create_plot
+from nogging import create_plot
 
 %matplotlib notebook
 
@@ -50,191 +50,9 @@ for i, x in enumerate(np.linspace(0, 10, 100)):
 plotter.plot()  # ensures final data gets plotted
 
 
-from liveplot import save_metrics
+from nogging import save_metrics
 
-# save metrics from liveplot instance
+# save metrics from nogging instance
 save_metrics("tmp.npz", plotter)
 ```
 
-### Recreating plot from saved metrics
-```python
-from liveplot import load_metrics, recreate_plot
-
-train, test = load_metrics("tmp.npz")
-
-colors = {"accuracy":dict(train="C4", test="C6"),
-          "loss":"red"}
-
-new_plotter, fig, ax = recreate_plot(train_metrics=train,
-                                     test_metrics=test,
-                                     colors=colors)
-```
-
-## Documentation
-
-```
-def create_plot(metrics, refresh=0., plot_title=None, figsize=None, track_time=True):
-    """ Create matplotlib figure/axes, and a live-plotter, which publishes
-        "live" training/testing metric data, at a batch and epoch level, to
-        the figure.
-
-        Parameters
-        ----------
-        metrics : Union[str, Sequence[str], Dict[str, valid-color]
-            The name, or sequence of names, of the metric(s) that will be plotted.
-
-            `metrics` can also be a dictionary, specifying the colors used to plot
-            the metrics. Two mappings are valid:
-                - metric-name -> color-value  (specifies train-metric color only)
-                - metric-name -> {train/test : color-value}
-
-        refresh : float, optional (default=0.)
-            Sets the plot refresh rate in seconds.
-
-            A refresh rate of 0. updates the once every 1/1000 seconds. A
-            negative refresh rate will draw the plot only at the end of the session.
-
-        plot_title : Optional[str]
-            Specifies the title used on the plot.
-
-        figsize : Optional[Sequence[int, int]]
-            Specifies the width and height, respectively, of the figure.
-
-        track_time : bool, default=True
-            If `True`, the total time of plotting is annotated in within the first axes
-
-        Returns
-        -------
-        Tuple[liveplot.LivePlot, matplotlib.figure.Figure, numpy.ndarray(matplotlib.axes.Axes)]
-            (LivePlot-instance, figure, array-of-axes)
-
-
-def recreate_plot(liveplot=None, *, train_metrics=None, test_metrics=None, colors=None):
-    """ Recreate a plot from a LivePlot instance or from train/test metric dictionaries.
-
-        Parameters
-        ----------
-        liveplot : Optional[liveplot.LivePlot]
-            An existing liveplot object.
-
-        Keyword-Only Arguments
-        ----------------------
-        train_metrics : Optional[OrderedDict[str, Dict[str, numpy.ndarray]]]
-           metric_name -> {"batch_data":   array,
-                           "epoch_data":   array,
-                           "epoch_domain": array}
-
-        test_metrics : Optional[OrderedDict[str, Dict[str, numpy.ndarray]]]
-           metric_name -> {"batch_data":   array,
-                           "epoch_data":   array,
-                           "epoch_domain": array}
-
-        colors : Optional[Dict[str, color-value], Dict[str, Dict[str, color-value]]
-            Specifying train-time metric colors only:
-                  metric-name -> color-value
-
-            Specifying train or test-time metric colors:
-                 metric-name -> {train/test -> color-value}
-
-        Returns
-        -------
-        Tuple[liveplot.LivePlot, matplotlib.figure.Figure, numpy.ndarray(matplotlib.axes.Axes)]
-            (LivePlot-instance, figure, array-of-axes)"""
-
-
-
-def save_metrics(path, liveplot=None, *, train_metrics=None, test_metrics=None):
-    """ Save live-plot metrics to a numpy zipped-archive (.npz). A LivePlot-instance
-        can be supplied, or train/test metrics can be passed explicitly to the function.
-
-        Parameters
-        ----------
-        path: PathLike
-           The file-path used to save the archive. E.g. 'path/to/saved_metrics.npz'
-
-        liveplot : Optional[liveplot.LivePlot]
-           The LivePlot instance whose metrics will be saves.
-
-        train_metrics : Optional[OrderedDict[str, Dict[str, numpy.ndarray]]]]
-
-            metric-name -> {batch_data -> array, epoch_domain -> array, epoch_data -> array}
-
-        test_metrics : Optional[OrderedDict[str, Dict[str, numpy.ndarray]]]]
-            metric-name -> {batch_data -> array, epoch_domain -> array, epoch_data -> array}"""
-
-
-def load_metrics(path):
-    """ Load liveplot metrics from a numpy archive.
-
-        Parameters
-        ----------
-        path : PathLike
-            Path to numpy archive.
-
-        Returns
-        -------
-        Tuple[OrderedDict[str, Dict[str, numpy.ndarray]], OrderedDict[str, Dict[str, numpy.ndarray]]]]
-           (train-metrics, test-metrics)"""
-
-class LivePlot:
-    """ Plots batch-level and epoch-level summary statistics of the training and
-        testing metrics of a model during a session.
-
-
-        Attributes
-        ----------
-        train_metrics : OrderedDict[str, Dict[str, numpy.ndarray]]
-            Stores training metric results data and plot-objects.
-
-        test_metrics : OrderedDict[str, Dict[str, numpy.ndarray]]
-            Stores testing metric results data and plot-objects.
-
-        metric_colors : Dict[str, Dict[str, color-value]]
-            {metric-name -> {'train'/'test' -> color-value}}
-
-       Methods
-       -------
-       plot(self)
-           Plot data, irrespective of the refresh rate. This should only
-           be called if you are generating a static plot.
-
-       plot_objects(self)
-           The figure-instance of the plot, and the axis-instance for each metric.
-
-           Returns
-           -------
-           Tuple[matplotlib.figure.Figure, numpy.ndarray(matplotlib.axes.Axes)]
-
-       plot_test_epoch(self)
-
-       plot_train_epoch(self)
-
-       set_test_batch(self, metrics, batch_size)
-
-           Parameters
-           ----------
-           metrics : Dict[str, Real]
-               Mapping of metric-name to value. Only those metrics that were
-               registered when initializing LivePlot will be recorded.
-
-           batch_size : Integral
-               The number of samples in the batch used to produce the metrics.
-               Used to weight the metrics to produce epoch-level statistics.
-
-       set_train_batch(self, metrics, batch_size, plot=True)
-
-           Parameters
-           ----------
-           metrics : Dict[str, Real]
-               Mapping of metric-name to value. Only those metrics that were
-               registered when initializing LivePlot will be recorded.
-
-           batch_size : Integral
-               The number of samples in the batch used to produce the metrics.
-               Used to weight the metrics to produce epoch-level statistics.
-
-           plot : bool
-               If True, plot the batch-metrics (adhering to the refresh rate)
-
-       show(self)
-           Calls `matplotlib.pyplot.show()`. For visualizing a static-plot. """```

requirements/coverage-requirements.txt

@@ -1,3 +1,4 @@
 numpy == 1.15.4
 matplotlib == 3.0.2
-xarray == 0.12.1
+xarray == 0.12.1
+custom_inherit == 2.2

setup.cfg

@@ -7,12 +7,12 @@ filterwarnings =
 [versioneer]
 VCS = git
 style = pep440
-versionfile_source = src/liveplot/_version.py
-versionfile_build= liveplot/_version.py
+versionfile_source = src/noggin/_version.py
+versionfile_build= noggin/_version.py
 tag_prefix = v
 
 [coverage:run]
-omit = src/liveplot/_version.py
+omit = src/noggin/_version.py
 
 # all of the following is the configuration for tox
 [tox:tox]
@@ -29,4 +29,4 @@ deps = {[testenv]deps}
        -r requirements/coverage-requirements.txt
        coverage
        pytest-cov
-commands = pytest --cov-report term-missing --cov-config=setup.cfg --cov=liveplot tests
+commands = pytest --cov-report term-missing --cov-config=setup.cfg --cov=noggin tests

setup.py

@@ -7,7 +7,7 @@ def do_setup():
     setup(
         version=versioneer.get_version(),
         cmdclass=versioneer.get_cmdclass(),
-        name="liveplot",
+        name="noggin",
         author="Ryan Soklaski",
         license="MIT",
         platforms=["Windows", "Linux", "Mac OS-X", "Unix"],

src/liveplot/__init__.py

@@ -0,0 +1,9 @@
+from noggin._version import get_versions
+from noggin.plotter import LivePlot
+from noggin.logger import LiveLogger
+from noggin.utils import create_plot, save_metrics, load_metrics
+
+__version__ = get_versions()["version"]
+del get_versions
+
+__all__ = ["create_plot", "save_metrics", "load_metrics", "LiveLogger", "LivePlot"]

src/noggin/__init__.py

@@ -42,7 +42,7 @@ def get_config():
     cfg.style = "pep440"
     cfg.tag_prefix = "v"
     cfg.parentdir_prefix = "None"
-    cfg.versionfile_source = "src/liveplot/_version.py"
+    cfg.versionfile_source = "src/noggin/_version.py"
     cfg.verbose = False
     return cfg
 

src/liveplot/_version.py src/noggin/_version.pysrc/liveplot/_version.py renamed to src/noggin/_version.py

@@ -266,7 +266,7 @@ def test_metrics(self) -> Dict[str, Dict[str, ndarray]]:
 
     def to_xarray(self, train_or_test: str) -> Tuple[Dataset, Dataset]:
         """
-        Given liveplot metrics, returns xarray datasets for the batch-level and epoch-level
+        Given noggin metrics, returns xarray datasets for the batch-level and epoch-level
         metrics, respectively.
 
         Parameters