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* __2D selectors__ Select slices of data along two dimensions (x and y) using rectangular brushing
* __Lasso__ Select slices of data along two dimensions (x and y) using a lasso
* __Jupyter Widget Integration__ Seamless integration and linking with Jupyter widgets
* __Extensibility__ `Object Model` can be extended to build re-usable compound plotting widgets and widget libraries
* __Extensibility__ `Object Model` can be extended to build reusable compound plotting widgets and widget libraries
* __Dashboards/Apps__ Build rich interactive dashboards and apps by integrating `bqplot` with other Jupyter widget libraries and `voila` dashboarding tool
While `bqplot` provides support for static plotting for most of the chart types, it really shines in interactive plotting where data attributes are updated in an event-driven fashion (using ipywidgets, click handlers etc.)
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To render labels professionally it's better to offset the label to avoid obscuring the true graph below. To do so, set the `x_offset` and `y_offset` attrbiutes (which accepts values in pixels), like so:
To render labels professionally it's better to offset the label to avoid obscuring the true graph below. To do so, set the `x_offset` and `y_offset` attributes (which accepts values in pixels), like so:
```py
label.x_offset = 10
label.y_offset = 10
Expand All
@@ -59,9 +59,9 @@ fig = plt.figure()
y = np.cumsum(np.random.randn(100))
line = plt.plot(np.arange(y.size), y_data)
label = plt.label(
["Halfway Point"],
default_size=26,
font_weight="bolder",
["Halfway Point"],
default_size=26,
font_weight="bolder",
colors=["orange"]
)
Expand All
@@ -70,7 +70,7 @@ label.y = [0.5]
```

Further, we can use values expressed in the units of the data. For example, if we consider a time series, we might want to label a specific date. Rather than calculating the position of the data with respect to the figure, we can simply pass in the date to the label function.
Further, we can use values expressed in the units of the data. For example, if we consider a time series, we might want to label a specific date. Rather than calculating the position of the data with respect to the figure, we can simply pass in the date to the label function.
```py
fig = plt.figure()
y = pd.Series(
Expand All
@@ -80,8 +80,8 @@ y = pd.Series(
)
lines = plt.plot(y.index, y)
label = plt.label(["Special Day"],
x=[np.datetime64("2007-02-14")],
label = plt.label(["Special Day"],
x=[np.datetime64("2007-02-14")],
colors=["orange"])
labels.y = [0.5]
Expand All
@@ -100,4 +100,4 @@ labels.enable_move = True
For detailed examples of bar plots, refer to the following example notebooks
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__Object Model__ is a verbose but fully customizable object-oriented API for plotting. Lower level constructs like scales, axes etc. have to explicitly constructed.
For detailed usage refer to the [example notebooks](https://github.com/bqplot/bqplot/tree/master/examples/Marks/Object%20Model) using `Object Model`.
__Object Model__ can be used to build re-usable plotting widgets and widget libraries. More details can be found in [here]
__Object Model__ can be used to build reusable plotting widgets and widget libraries. More details can be found in [here]
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"2. Inside the class constructor call the super class constructor passing in the widgets as children\n",
"3. Register any widget callbacks inside the constructor and define callbacks as class methods\n",
"\n",
"Once the compound widget is defined in a class, it can be put in a library and used along with other interactive widgets! Compound widgets are an excellent way of building re-usable components (**using just python code**) which can be seamlessly integrated with `ipywidgets`, `bqplot` and other interactive widgets."
"Once the compound widget is defined in a class, it can be put in a library and used along with other interactive widgets! Compound widgets are an excellent way of building reusable components (**using just python code**) which can be seamlessly integrated with `ipywidgets`, `bqplot` and other interactive widgets."
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Fix CI #1642
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Fix CI #1642
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