Bug fixes for plot methods

README.rst

@@ -3,18 +3,17 @@ waltlabtools
 
 .. image:: https://img.shields.io/conda/vn/tylerdougan/waltlabtools?label=Anaconda
    :target: https://anaconda.org/tylerdougan/waltlabtools
-   :alt: Conda
+   :alt: Anaconda
 .. image:: https://img.shields.io/pypi/v/waltlabtools?label=PyPI
    :target: https://pypi.org/project/waltlabtools
    :alt: PyPI
 .. image:: https://img.shields.io/readthedocs/waltlabtools?label=Documentation
    :target: https://waltlabtools.readthedocs.io/en/latest/
    :alt: Documentation
-.. image:: https://img.shields.io/github/repo-size/tylerdougan/waltlabtools?label=GitHub
+.. image:: https://img.shields.io/github/issues/tylerdougan/waltlabtools?label=GitHub
    :target: https://github.com/tylerdougan/waltlabtools
    :alt: GitHub
 
-
 A collection of tools for biomedical research assay analysis in Python.
 
 Key Features
@@ -51,46 +50,6 @@ first install Anaconda or
 with Python).
 
 
-Dependencies
-^^^^^^^^^^^^
-
-- waltlabtools requires
-
-  - `numpy <https://numpy.org/doc/stable/index.html>`__ ≥ 1.10
-
-  - `scipy <https://docs.scipy.org/doc/scipy/getting_started.html>`__ ≥ 1.3
-
-- If installing with pip or conda, numpy and scipy will be installed
-  automatically
-
-- Specific modules have additional requirements:
-
-  - `waltlabtools.read <https://waltlabtools.readthedocs.io/en/latest/waltlabtools.read.html>`__
-    (for importing data from Quanterix instruments) requires
-
-    - `pandas <https://pandas.pydata.org>`__ ≥ 0.25
-
-- waltlabtools also plays well with
-
-  - `JAX <https://jax.readthedocs.io/en/latest/index.html>`__
-    for accelerated numerical computing – waltlabtools will use jax.numpy
-    instead of numpy if jax is loaded
-
-  - `JupyterLab <https://jupyterlab.readthedocs.io/en/stable/>`__
-    for interactively writing code
-
-  - `pandas <https://pandas.pydata.org>`__
-    for data import/export and organization
-
-  - `matplotlib <https://matplotlib.org>`__ for plotting
-
-  - `scikit-learn <https://scikit-learn.org/stable/>`__ for data analysis
-
-All of these packages can all be installed using
-`conda <https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html>`__
-or `pip <https://pypi.org>`__.
-
-
 Usage
 ^^^^^
 

meta.yaml

@@ -1,13 +1,13 @@
 {% set name = "waltlabtools" %}
-{% set version = "1.0.0b25" %}
+{% set version = "1.0.0b26" %}
 
 package:
   name: {{ name|lower }}
   version: {{ version }}
 
 source:
   url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/waltlabtools-{{ version }}.tar.gz
-  sha256: 6804e087f3bc6b760e009355ca9c52e2bc161457915adc47c41620882432adbc
+  sha256: d38888eab34192924a264309c258ab0451a55b28f23bb08e35124e9ffd6ffa24
 
 build:
   noarch: python

pyproject.toml

@@ -14,7 +14,7 @@ dependencies = [
 ]
 requires-python = ">= 3.8"
 authors = [
-    {name = "Tyler Dougan", email = "tyler_dougan@hst.harvard.edu"}
+    {name = "Tyler Dougan", email = "tylerdougan@harvard.simplelogin.com"}
 ]
 description = "A collection of tools for biomedical research assay analysis in Python."
 readme = "README.rst"
@@ -29,8 +29,6 @@ classifiers = [
 ]
 
 [project.optional-dependencies]
-jax = ["jax"]
-numba = ["numba"]
 all = [
     "jax",
     "numba",
@@ -43,11 +41,11 @@ all = [
 ]
 
 [project.urls]
-Homepage = "https://github.com/tylerdougan/waltlabtools/"
+Anaconda = "https://anaconda.org/tylerdougan/waltlabtools"
 Documentation = "https://waltlabtools.readthedocs.io/en/latest/"
-PyPI = "https://pypi.org/project/waltlabtools/"
+Homepage = "https://github.com/tylerdougan/waltlabtools/"
 "Issue Tracker" = "https://github.com/tylerdougan/waltlabtools/issues"
-Anaconda = "https://anaconda.org/tylerdougan/waltlabtools"
+PyPI = "https://pypi.org/project/waltlabtools/"
 
 [tool.setuptools.dynamic]
 version = {attr = "waltlabtools.__version__"}

src/waltlabtools.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: waltlabtools
-Version: 1.0.0b25
+Version: 1.0.0b26
 Summary: A collection of tools for biomedical research assay analysis in Python.
 Author-email: Tyler Dougan <tyler_dougan@hst.harvard.edu>
 Project-URL: Homepage, https://github.com/tylerdougan/waltlabtools/
@@ -35,6 +35,7 @@ Requires-Dist: numexpr; extra == "all"
 Requires-Dist: bottleneck; extra == "all"
 Requires-Dist: jinja2; extra == "all"
 Requires-Dist: openpyxl; extra == "all"
+Requires-Dist: xlrd; extra == "all"
 
 waltlabtools
 ============

src/waltlabtools.egg-info/requires.txt

@@ -12,6 +12,7 @@ numexpr
 bottleneck
 jinja2
 openpyxl
+xlrd
 
 [jax]
 jax

src/waltlabtools/__init__.py

@@ -4,4 +4,4 @@
 from .model import *
 from .read import *
 
-__version__ = "v1.0.0b26"
+__version__ = "v1.0.0b27"

src/waltlabtools/_plot.py

@@ -49,6 +49,7 @@ def subplots(
         nrows=nrows,
         ncols=ncols,
         squeeze=False,
+        **match_kwargs(fig.subplots, kwargs),
     )
     return fig, axs
 

src/waltlabtools/cal_curve.py

@@ -444,11 +444,10 @@ def _make_curve_points(self, **kwargs):
             )
 
         trans = xscale.get_transform()
-        vmin = np.amin(self.X_)
-        vmax = np.amax(self.X_)
-        minpos = np.amin(self.X_[self.X_ > 0])
-        if np.isfinite(self.lod_):
-            vmin = min(vmin, self.lod_)
+        vmin = min(np.min(self.X_), self.lod_)
+        vmax = np.max(self.X_)
+        minpos = np.min(self.X_[self.X_ > 0])
+        if self.lod_ > 0:
             minpos = min(minpos, self.lod_)
         vmin, vmax = xscale.limit_range_for_scale(vmin=vmin, vmax=vmax, minpos=minpos)
         trvmin = trans.transform(vmin)
@@ -797,77 +796,22 @@ def plot(  # type: ignore
             cols = self.index.get_level_values(levels[0]).unique()
             ncols = self.index.get_level_values(levels[0]).nunique()
             nrows = self.index.get_level_values(0).value_counts().max()
-            fig, axs = subplots(nrows=nrows, ncols=ncols, fig=fig, **kwargs)
+            fig, axs = subplots(
+                nrows=nrows, ncols=ncols, fig=fig, squeeze=False, **kwargs
+            )
 
-            ax_s = pd.Series(index=self.index)
+            ax_s = pd.Series(index=self.index.sort_values(), dtype=object)
             for c, col in enumerate(cols):
-                # idx = sorted(self.index[self.index.get_level_values(levels[0]) == col])
-                # ax_s[col] = dict(zip(idx, axs[:, c]))
                 ax_s[col] = axs[: len(ax_s[col]), c]
         else:
             nrows = int(np.ceil(len(self) / max_cols))
             ncols = int(np.ceil(len(self) / nrows))
             fig, axs = subplots(nrows=nrows, ncols=ncols, fig=fig, **kwargs)
-            ax_s = pd.Series(dict(zip(self.index, np.ravel(axs))))
+            ax_s = pd.Series(dict(zip(self.index.sort_values(), np.ravel(axs))))
 
         for i, ax in ax_s.items():
-            title = "\n".join(str(a) for a in i) if isinstance(i, tuple) else i
-            self[i].plot(ax=ax, title=title, **match_kwargs(self[i].plot, kwargs))
-
-        return ax_s.item() if len(ax_s) == 1 else axs  # type: ignore
-        # self_notna = self[self.notna()]
-
-        # if len(self_notna) == 0:
-        #     # empty series
-        #     ax_map = {}
-
-        # elif len(self_notna) == 1:
-        #     # only one CalCurve in series
-        #     if ax is not None and isinstance(ax, Axes):
-        #         if fig is None:
-        #             fig = ax.figure
-        #     elif fig is not None:
-        #         ax = fig.subplots(**match_kwargs(plt.subplots, kwargs))
-        #     else:
-        #         fig, ax = self.subplots(**kwargs)
-        #     ax_map = {self.index[0]: ax}
-
-        # elif self.index.nlevels == 1:
-        #     # only one assay or only one plex
-        #     if len(self) <= max_cols:
-        #         fig, axs = self.subplots(ncols=len(self_notna), **kwargs)
-        #         ax_map = {self_notna.index[i]: axs[i] for i in range(len(self_notna))}
-        #     else:
-        #         nrows = int(np.ceil(len(self) / max_cols))
-        #         fig, axs = self.subplots(nrows=nrows, ncols=max_cols, **kwargs)
-        #         ax_map = {
-        #             self_notna.index[i]: axs[divmod(i, max_cols)]
-        #             for i in range(len(self_notna))
-        #         }
-
-        # else:
-        #     # try to lay out cal curves according to assay and plex
-        #     ncols = self.index.get_level_values(0).nunique()
-        #     nrows = self.index.get_level_values(0).value_counts().max()
-        #     fig, axs = self.subplots(nrows=nrows, ncols=ncols, **kwargs)
-        #     ax_map = {}
-        #     for a0, assay_level_0 in enumerate(
-        #         sorted(self_notna.index.get_level_values(0).unique())
-        #     ):
-        #         for a1, assay_level_1 in enumerate(
-        #             sorted(self_notna[assay_level_0].index)
-        #         ):
-        #             ax_map[(assay_level_0, assay_level_1)] = axs[a1, a0]
-
-        # for assay, ax in ax_map.items():
-        #     if hasattr(assay, "__iter__") and not isinstance(assay, str):
-        #         title = "\n".join([str(a) for a in assay])
-        #     else:
-        #         title = assay
-        #     self[assay].plot(
-        #         ax=ax, title=title, **match_kwargs(self[assay].plot, kwargs)
-        #     )  # type: ignore
-        # return
-
-        # # if tight_layout:
-        # #     plt.tight_layout()
+            if isinstance(self[i], CalCurve):
+                title = "\n".join(str(a) for a in i) if isinstance(i, tuple) else i
+                self[i].plot(ax=ax, title=title, **kwargs)
+
+        return ax_s.item() if len(ax_s) == 1 else axs