🎨 formatting + linting in CI-CD

.github/workflows/lint.yml

@@ -0,0 +1,34 @@
+name: Lint
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        lfs: true
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.10'
+    - name: Install Poetry
+      uses: snok/install-poetry@v1
+    - name: Cache poetry
+      id: cached-poetry-dependencies
+      uses: actions/cache@v2
+      with:
+        path: ~/.cache/pypoetry/virtualenvs
+        key: ${{ runner.os }}-poetry-${{ hashFiles('poetry.lock') }}-python-${{ matrix.python-version }}
+    - run: poetry --version
+    - name: Install dependencies
+      run: poetry install
+      # Do not use caching (anymore)
+#      if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+    - name: Lint
+      run: make lint

.gitignore

@@ -1,5 +1,6 @@
 venv*
 .cache_datasets/
+*.ruff*
 *.DS_Store
 file_system_store/
 

CONTRIBUTING.md

@@ -79,12 +79,30 @@ poetry build  # build the underlying C code
 
 </details>
 
+### Formatting the code
+
+We use [black](https://github.com/psf/black) and [isort](https://github.com/PyCQA/isort) to format the code.
+
+To format the code, run the following command (more details in the [Makefile](Makefile)):
+```sh
+make format
+```
+
+### Checking the linting
+
+We use [ruff](https://github.com/charliermarsh/ruff) to check the linting.
+
+To check the linting, run the following command (more details in the [Makefile](Makefile)):
+```sh
+make lint
+```
+
 ### Running the tests (& code coverage)
 
-You can run the test with the following code:
+You can run the tests with the following code (more details in the [Makefile](Makefile)):
 
 ```sh
-poetry run pytest --cov-report term-missing --cov=plotly_resampler tests
+make test
 ```
 
 To get the selenium tests working you should have Google Chrome installed.

Makefile

@@ -0,0 +1,31 @@
+black = black plotly_resampler examples tests
+isort = isort plotly_resampler examples tests
+
+.PHONY: format
+format:
+	$(isort)
+	$(black)
+
+.PHONY: lint
+lint:
+	poetry run ruff plotly_resampler tests
+	poetry run $(isort) --check-only --df
+	poetry run $(black) --check --diff
+
+.PHONY: test
+test:
+	poetry run pytest --cov-report term-missing --cov=plotly_resampler tests
+
+.PHONY: clean
+clean:
+	rm -rf `find . -name __pycache__`
+	rm -rf .cache
+	rm -rf .pytest_cache
+	rm -rf *.egg-info
+	rm -f .coverage
+	rm -rf build
+
+	rm -f `find . -type f -name '*.py[co]' `
+	rm -f `find . -type f -name '*~' `
+	rm -f `find . -type f -name '.*~' `
+	rm -f `find . -type f -name '*.cpython-*' `

examples/dash_apps/01_minimal_global.py

@@ -15,10 +15,10 @@
 
 import numpy as np
 import plotly.graph_objects as go
-from dash import Input, Output, dcc, html, Dash, no_update, callback_context
+from dash import Dash, Input, Output, callback_context, dcc, html, no_update
+from trace_updater import TraceUpdater
 
 from plotly_resampler import FigureResampler
-from trace_updater import TraceUpdater
 
 # Data that will be used for the plotly-resampler figures
 x = np.arange(2_000_000)
@@ -38,7 +38,6 @@
         html.H1("plotly-resampler global variable", style={"textAlign": "center"}),
         html.Button("plot chart", id="plot-button", n_clicks=0),
         html.Hr(),
-
         # The graph and it's needed components to update efficiently
         dcc.Graph(id="graph-id"),
         TraceUpdater(id="trace-updater", gdID="graph-id"),
@@ -59,8 +58,8 @@ def plot_graph(n_clicks):
         # Note how the replace method is used here on the global figure object
         global fig
         fig.replace(go.Figure())
-        fig.add_trace(go.Scattergl(name="log"), hf_x=x, hf_y=noisy_sin * .9999995 ** x)
-        fig.add_trace(go.Scattergl(name="exp"), hf_x=x, hf_y=noisy_sin * 1.000002 ** x)
+        fig.add_trace(go.Scattergl(name="log"), hf_x=x, hf_y=noisy_sin * 0.9999995**x)
+        fig.add_trace(go.Scattergl(name="exp"), hf_x=x, hf_y=noisy_sin * 1.000002**x)
         return fig
     else:
         return no_update

examples/dash_apps/02_minimal_cache.py

@@ -11,15 +11,16 @@
 
 import numpy as np
 import plotly.graph_objects as go
-from dash import Input, Output, State, dcc, html, no_update, callback_context
+from dash import Input, Output, State, callback_context, dcc, html, no_update
 from dash_extensions.enrich import (
     DashProxy,
     ServersideOutput,
     ServersideOutputTransform,
 )
-from plotly_resampler import FigureResampler
 from trace_updater import TraceUpdater
 
+from plotly_resampler import FigureResampler
+
 # Data that will be used for the plotly-resampler figures
 x = np.arange(2_000_000)
 noisy_sin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1_000

examples/dash_apps/03_minimal_cache_dynamic.py

@@ -13,8 +13,8 @@
 
 """
 
-from uuid import uuid4
 from typing import List
+from uuid import uuid4
 
 import numpy as np
 import plotly.graph_objects as go
@@ -26,9 +26,10 @@
     Trigger,
     TriggerTransform,
 )
-from plotly_resampler import FigureResampler
 from trace_updater import TraceUpdater
 
+from plotly_resampler import FigureResampler
+
 # Data that will be used for the plotly-resampler figures
 x = np.arange(2_000_000)
 noisy_sin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1_000

examples/dash_apps/11_sine_generator.py

@@ -14,20 +14,21 @@
 
 from uuid import uuid4
 
+import dash_bootstrap_components as dbc
 import numpy as np
 import plotly.graph_objects as go
-import dash_bootstrap_components as dbc
-from dash import MATCH, Input, Output, State, dcc, html, no_update, callback_context
+from dash import MATCH, Input, Output, State, callback_context, dcc, html, no_update
 from dash_extensions.enrich import (
     DashProxy,
     ServersideOutput,
     ServersideOutputTransform,
     Trigger,
     TriggerTransform,
 )
-from plotly_resampler import FigureResampler
 from trace_updater import TraceUpdater
 
+from plotly_resampler import FigureResampler
+
 # --------------------------------------Globals ---------------------------------------
 app = DashProxy(
     __name__,

examples/dash_apps/12_file_selector.py

@@ -12,19 +12,18 @@
 
 import dash_bootstrap_components as dbc
 import plotly.graph_objects as go
-from dash import Input, Output, State, dcc, html, no_update, callback_context
-
+from dash import Input, Output, State, callback_context, dcc, html, no_update
 from dash_extensions.enrich import (
     DashProxy,
     ServersideOutput,
     ServersideOutputTransform,
 )
-from plotly_resampler import FigureResampler
 from trace_updater import TraceUpdater
-
-from utils.callback_helpers import multiple_folder_file_selector, get_selector_states
+from utils.callback_helpers import get_selector_states, multiple_folder_file_selector
 from utils.graph_construction import visualize_multiple_files
 
+from plotly_resampler import FigureResampler
+
 # --------------------------------------Globals ---------------------------------------
 app = DashProxy(
     __name__,

examples/dash_apps/13_coarse_fine.py

@@ -13,24 +13,23 @@
 
 __author__ = "Jonas Van Der Donckt"
 
-import dash_bootstrap_components as dbc
-import plotly.graph_objects as go
 from pathlib import Path
 from typing import List
-from dash import Input, Output, State, dcc, html, no_update, callback_context
 
+import dash_bootstrap_components as dbc
+import plotly.graph_objects as go
+from dash import Input, Output, State, callback_context, dcc, html, no_update
 from dash_extensions.enrich import (
     DashProxy,
     ServersideOutput,
     ServersideOutputTransform,
 )
-
-from plotly_resampler import FigureResampler
 from trace_updater import TraceUpdater
-
-from utils.callback_helpers import multiple_folder_file_selector, get_selector_states
+from utils.callback_helpers import get_selector_states, multiple_folder_file_selector
 from utils.graph_construction import visualize_multiple_files
 
+from plotly_resampler import FigureResampler
+
 # --------------------------------------Globals ---------------------------------------
 app = DashProxy(
     __name__,

examples/dash_apps/utils/callback_helpers.py

@@ -3,10 +3,10 @@
 
 __author__ = "Jonas Van Der Donckt"
 
+import itertools
 from pathlib import Path
 from typing import Dict, List
 
-import itertools
 import dash_bootstrap_components as dbc
 from dash import Input, Output, State, dcc, html
 from functional import seq
@@ -21,8 +21,8 @@ def _update_file_widget(folder):
             set(
                 list(
                     seq(Path(folder).iterdir())
-                        .filter(lambda x: x.is_file() and x.name.endswith("parquet"))
-                        .map(lambda x: x.name)
+                    .filter(lambda x: x.is_file() and x.name.endswith("parquet"))
+                    .map(lambda x: x.name)
                 )
             )
         )
@@ -41,7 +41,7 @@ def _register_selection_callbacks(app, ids=None):
 
 
 def multiple_folder_file_selector(
-        app, name_folders_list: List[Dict[str, dict]], multi=True
+    app, name_folders_list: List[Dict[str, dict]], multi=True
 ) -> dbc.Card:
     """Constructs a folder user date selector
 

examples/other_apps/streamlit_app.py

@@ -13,30 +13,31 @@
 
 __author__ = "Jeroen Van Der Donckt"
 
+# 0. Create a noisy sine wave
+import numpy as np
 import plotly.graph_objects as go
+
 from plotly_resampler import FigureResampler
 
-# 0. Create a noisy sine wave
-import numpy as np
 x = np.arange(1_000_000)
 noisy_sin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1_000
 
 ### 1. Use FigureResampler
 fig = FigureResampler(default_n_shown_samples=2_000)
-fig.add_trace(go.Scattergl(name='noisy sine', showlegend=True), hf_x=x, hf_y=noisy_sin)
+fig.add_trace(go.Scattergl(name="noisy sine", showlegend=True), hf_x=x, hf_y=noisy_sin)
 fig.update_layout(height=700)
 
 ### 2. Run the visualization (which is a dash app) in a (sub)process on a certain port
 # Note: starting a process allows executing code after `.show_dash` is called
 from multiprocessing import Process
+
 port = 9022
-proc = Process(
-    target=fig.show_dash, kwargs=dict(mode="external", port=port)
-).start()
+proc = Process(target=fig.show_dash, kwargs=dict(mode="external", port=port)).start()
 
 # Deleting the lines below this and running this file will result in a classic running dash app
 # Note: for just a dash app it is not even necessary to execute .show_dash in a (sub)process
 
 ### 3. Add as iframe component to streamlit
 import streamlit.components.v1 as components
-components.iframe(f'http://localhost:{port}', height=700)
+
+components.iframe(f"http://localhost:{port}", height=700)

plotly_resampler/aggregation/aggregation_interface.py

@@ -29,14 +29,14 @@ def __init__(
             irregularly sampled data. By default, True.
         nan_position: str, optional
             Indicates where nans must be placed when gaps are detected. \n
-            If ``'end'``, the first point after a gap will be replaced with a 
+            If ``'end'``, the first point after a gap will be replaced with a
             nan-value \n
-            If ``'begin'``, the last point before a gap will be replaced with a 
+            If ``'begin'``, the last point before a gap will be replaced with a
             nan-value \n
-            If ``'both'``, both the encompassing gap datapoints are replaced with 
+            If ``'both'``, both the encompassing gap datapoints are replaced with
             nan-values \n
             .. note::
-                This parameter only has an effect when ``interleave_gaps`` is set 
+                This parameter only has an effect when ``interleave_gaps`` is set
                 to *True*.
         dtype_regex_list: List[str], optional
             List containing the regex matching the supported datatypes, by default None.

plotly_resampler/aggregation/aggregators.py

@@ -21,6 +21,7 @@
     from .algorithms.lttb_c import LTTB_core_c as LTTB_core
 except (ImportError, ModuleNotFoundError):
     import warnings
+
     warnings.warn("Could not import lttbc; will use a (slower) python alternative.")
     from .algorithms.lttb_py import LTTB_core_py as LTTB_core
 
@@ -286,7 +287,7 @@ def _aggregate(self, s: pd.Series, n_out: int) -> pd.Series:
         ratio_threshold = 100
 
         # TODO -> test this with a move of the .so file
-        if LTTB_core.__name__ == 'LTTB_core_py':
+        if LTTB_core.__name__ == "LTTB_core_py":
             size_threshold = 1_000_000
 
         if s.shape[0] > size_threshold and s.shape[0] / n_out > ratio_threshold:

plotly_resampler/aggregation/algorithms/lttb_c.py

@@ -2,6 +2,7 @@
 
 
 import numpy as np
+
 from .lttbc import (
     downsample_double_double,
     downsample_int_double,

plotly_resampler/figure_resampler/__init__.py

@@ -13,7 +13,6 @@
 from .figure_resampler import FigureResampler
 from .figurewidget_resampler import FigureWidgetResampler
 
-
 __all__ = [
     "FigureResampler",
     "FigureWidgetResampler",

plotly_resampler/figure_resampler/figure_resampler.py

@@ -10,11 +10,11 @@
 
 __author__ = "Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost"
 
+import base64
+import uuid
 import warnings
-from typing import Tuple, List
+from typing import List, Tuple
 
-import uuid
-import base64
 import dash
 import plotly.graph_objects as go
 from flask_cors import cross_origin