Prepare to publish on Pypi

.gitignore

@@ -3,4 +3,5 @@
 __pycache__/
 /*.csv
 build/
-*.egg-info/
+*.egg-info/
+dist/

README.md

@@ -2,8 +2,7 @@
 
 The Visual Pandas Selector is a tool to visually select portions of numeric time-series data from a pandas dataframe. The tool is intended to provide an fast interactive way for manual data selection, as can be very useful in for example machine learning, regression or system identification.
 
-The tool is configurable in order to plot a range of dataframe columns in vertically stacked subplots.
-Hereby the user can specify which columns are plotted in which subplot. Furthermore, a histogram is included to get a rough idea on the distribution of the data.
+Easily configure the tool to plot dataframe columns in vertically stacked subplots and view data distributions with the included histogram feature. With a simple click and drag, you can then select horizontal data windows, and let the tool automatically combine them into a new dataframe.
 
 The user can subsequentially select different horizontal data windows via click and drag and he tool then automatically combines the visually selected sections into a new dataframe.
 
@@ -12,19 +11,19 @@ The user can subsequentially select different horizontal data windows via click
 
 ## Install
 
-I am still working on publishing the project on PyPi (any help is apprechiated here). For now you can install the package locally from the top-level project folder using:
+Install the package using:
 
 ```bash
-pip install .
+pip install vpselector
 ```
 
 ## Use in your project
 
 Then simply import it using `import vpselector`. Then simply use:
 
-- If your project does not contain a pyqt application: `select_visual_data(data : pd.DataFrame, plot_config : dict)` 
+- If your project does not contain a pyqt application: `vpselector.select_visual_data(data : pd.DataFrame, plot_config : dict)` 
 
-- To add the vpselector to an existing pyqt application: `select_visual_data_in_pyqt_app(data : pd.DataFrame, plot_config : dict, pyqt_app)` 
+- To add the vpselector to an existing pyqt application: `vpselector.select_visual_data_in_pyqt_app(data : pd.DataFrame, plot_config : dict, pyqt_app)` 
 
 
 ## Run the Example 

pyproject.toml

@@ -1,21 +1,33 @@
 [project]
 name = "vpselector"
-version = "0.1.0"
+version = "1.0.0"
 description = "Visualize and interactively select time-series data from a pandas DataFrame."
 readme = "README.md"
 requires-python = ">=3.8"
-license = {file = "LICENSE.txt"}
-keywords = ["python", "dataframe", "pandas", "visualization", "data selection", "time-series data", "data tools", "data science", "data-driven engineering", "machine learning", "system identification"]
+license = { file = "LICENSE.txt" }
+keywords = [
+  "python",
+  "dataframe",
+  "pandas",
+  "visualization",
+  "data selection",
+  "time-series data",
+  "data tools",
+  "data science",
+  "data-driven engineering",
+  "machine learning",
+  "system identification",
+]
 authors = [
-  {email = "manuel.galliker@gmx.ch"},
-  {name = "Manuel Yves Galliker"}
+  { email = "manuel@galliker.tech" },
+  { name = "Manuel Yves Galliker" },
 ]
 maintainers = [
-  {name = "Manuel Yves Galliker", email = "manuel.galliker@gmx.ch"}
+  { name = "Manuel Yves Galliker", email = "manuel@galliker.tech" },
 ]
 classifiers = [
   "Development Status :: 4 - Beta",
-  "Programming Language :: Python"
+  "Programming Language :: Python",
 ]
 
 dependencies = [
@@ -24,12 +36,11 @@ dependencies = [
   "matplotlib>=3.7.1",
   "seaborn>=0.11.1",
   "overrides>=7.3.1",
+  "twine>=4.0.2",
 ]
 
 [project.optional-dependencies]
-format = [
-  "black >= 23.3.0"
-]
+format = ["black >= 23.3.0"]
 
 [project.urls]
-repository = "https://github.com/manumerous/vpselector"
+repository = "https://github.com/manumerous/vpselector"

src/vpselector/windows/main_window.py

@@ -21,7 +21,7 @@
 class MainWindow(QtWidgets.QMainWindow):
     def __init__(self, data : pd.DataFrame , plot_config : dict, *args, **kwargs):
         super(MainWindow, self).__init__(*args, **kwargs)
-        self.setWindowTitle("Visual Dataframe Selector")
+        self.setWindowTitle("Visual Pandas Selector")
 
         self.data = data
         self.cropped_data = pd.DataFrame()