added support for jupyter notebook

Author: Ioannis Giagkiozis

Cargo.toml

@@ -2,5 +2,5 @@
 members = [
     "plotly",
     "plotly_kaleido",
-    "plotly_ndarray"
+#    "plotly_ndarray"
 ]

README.md

@@ -50,7 +50,7 @@ The following feature flags are available:
     * Optional, compatible with Rust stable.
     * Adds plot save functionality to the following formats: png, jpeg, webp, svg, pdf and eps.
     * Requires some additional configuration, see [plotly_kaleido](https://github.com/igiagkiozis/plotly/tree/master/plotly_kaleido).
-* `ndarray`
+* `plotly_ndarray`
     * Optional, compatible with Rust stable.
     * Adds support for creating plots directly using [ndarray](https://github.com/rust-ndarray/ndarray) types.
 

docs/book/src/SUMMARY.md

@@ -3,7 +3,7 @@
 - [Plotly.rs](./plotly_rs.md)
     - [Getting Started](./getting_started.md)
 - [Fundamentals](./fundamentals.md)
-    - [Jupyter Lab Support](./fundamentals/jupyter_lab_support.md)
+    - [Jupyter Support](./fundamentals/jupyter_support.md)
     - [ndarray Support](./fundamentals/ndarray_support.md)
     - [Shapes](./fundamentals/shapes.md)
 - [Recipes](./recipes.md)

docs/book/src/fundamentals/jupyter_lab_support.md renamed to docs/book/src/fundamentals/jupyter_support.md

@@ -1,23 +1,29 @@
-# Jupyter Lab Support
+# Jupyter Support
 
 As of version `0.6.0`, [Plotly.rs](https://github.com/igiagkiozis/plotly) has native support for the [EvCxR Jupyter Kernel](https://github.com/google/evcxr/tree/master/evcxr_jupyter). 
 
 Once you've installed the required packages you'll be able to run all the examples shown here as well as all [the recipes](../recipes.md) in Jupyter Lab!
 
 
 ## Installation
+It is assumed that an installation of the [Anaconda](https://www.anaconda.com/products/individual) Python distribution is already present in the system. If that is not the case you can follow these [instructions](https://www.anaconda.com/products/individual) to get up and running with `Anaconda`. 
 
 ```shell script
-conda install -c plotly=4.9.0
+conda install -c plotly plotly=4.9.0
 conda install jupyterlab "ipywidgets=7.5"
 ```
 
+optionally (or instead of `jupyterlab`) you can also install Jupyter Notebook:
+```shell script
+conda install notebook
+```
+
 Although there are alternative methods to enable support for the [EvCxR Jupyter Kernel](https://github.com/google/evcxr/tree/master/evcxr_jupyter), we have elected to keep the requirements consistent with what those of other languages, e.g. Julia, Python and R. This way users know what to expect; and also the folks at [Plotly](https://plotly.com/python/getting-started/#jupyter-notebook-support) have done already most of the heavy lifting to create an extension for Jupyter Lab that works very well.
 
 Run the following to install the Plotly Jupyter Lab extension: 
 ```shell script
 jupyter labextension install [email protected]
-``` 
+```
 
 Once this step is complete to make sure the installation so far was successful, run the following command: 
 ```shell script
@@ -97,7 +103,8 @@ let mut plot = Plot::new();
 plot.add_trace(trace);
 let layout = Layout::new().height(800);
 plot.set_layout(layout);
-// Alternatively you can directly invoke the display method: plot.evcxr_display();
-plot
+plot.lab_display();
 ```
-Notice that at the last line there is no semicolon. There are two ways to display a plot in the `EvCxR` kernel, either have the plot object as shown above in the last line or directly invoke the `Plot::evcxr_display()` method on it; both have the same result. You can also find an example notebook [here](https://github.com/igiagkiozis/plotly/blob/master/plotly/examples/jupyterlab_examples.ipynb) that will periodically be updated with examples.
+For Jupyter Lab there are two ways to display a plot in the `EvCxR` kernel, either have the plot object be in the last line without a semicolon or directly invoke the `Plot::lab_display` method on it; both have the same result. You can also find an example notebook [here](https://github.com/igiagkiozis/plotly/blob/master/plotly/examples/jupyter_lab_examples.ipynb) that will periodically be updated with examples.
+
+The process for Jupyter Notebook is very much the same with one exception; the `Plot::noteboo_display` method must be used to display the plot. You can find an example notebook [here](https://github.com/igiagkiozis/plotly/blob/master/plotly/examples/jupyter_notebook_examples.ipynb) 

docs/book/src/fundamentals/ndarray_support.md

@@ -1 +1,133 @@
-# `ndarray` Support 
+# `ndarray` Support 
+
+To enable [ndarray](https://github.com/rust-ndarray/ndarray) support in [Plotly.rs](https://github.com/igiagkiozis/plotly) add the following feature to your `Cargo.toml` file:
+```toml
+[dependencies]
+plotly = { version = ">=0.6.0", features = ["plotly_ndarray"] }
+```
+
+This extends the [Plotly.rs](https://github.com/igiagkiozis/plotly) API in two ways: 
+* `Scatter` traces can now be created using the `Scatter::from_ndarray` constructor,
+* and also multiple traces can be created with the `Scatter::to_traces` method.
+
+The full source code for the examples below can be found [here](https://github.com/igiagkiozis/plotly/blob/master/plotly/examples/ndarray_support.rs).
+
+## `ndarray` Traces
+
+The following imports have been used to produce the plots below:
+
+```rust
+use plotly::common::{Mode};
+use plotly::{Plot, Scatter};
+use ndarray::{Array, Ix1, Ix2};
+use plotly::ndarray::ArrayTraces;
+```
+
+
+### Single Trace
+```rust
+fn single_ndarray_trace(show: bool) {
+    let n: usize = 11;
+    let t: Array<f64, Ix1> = Array::range(0., 10., 10. / n as f64);
+    let ys: Array<f64, Ix1> = t.iter().map(|v| (*v).powf(2.)).collect();
+
+    let trace = Scatter::from_array(t, ys).mode(Mode::LinesMarkers);
+
+    let mut plot = Plot::new();
+    plot.add_trace(trace);
+    if show {
+        plot.show();
+    }
+    println!("{}", plot.to_inline_html(Some("single_ndarray_trace")));
+}
+```
+<div id="single_ndarray_trace" class="plotly-graph-div" style="height:100%; width:100%;"></div>
+<script type="text/javascript">
+    window.PLOTLYENV=window.PLOTLYENV || {};
+    if (document.getElementById("single_ndarray_trace")) {
+        var d3 = Plotly.d3;
+        var image_element= d3.select('#image-export');
+        var trace_0 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[0.0,0.8264462809917354,3.305785123966942,7.438016528925619,13.223140495867767,20.661157024793383,29.752066115702476,40.49586776859504,52.89256198347107,66.94214876033058,82.64462809917353]};
+var data = [trace_0];
+var layout = {};
+        Plotly.newPlot('single_ndarray_trace', data, layout, {"responsive": true});
+    };
+</script>
+
+### Multiple Traces
+To display a `2D` array (`Array<_, Ix2>`) you can use the `Scatter::to_traces` method. The first argument of the method represents the common axis for the traces (`x` axis) whilst the second argument contains a collection of traces. At this point it should be noted that there is some ambiguity when passing a `2D` array; namely are the traces arranged along the columns or the rows of the matrix? This ambiguity is resolved by the third argument of the `Scatter::to_traces` method. If that argument is set to `ArrayTraces::OverColumns` then the library assumes that every column represents an individual trace, alternatively if this is set to `ArrayTraces::OverRows` the assumption is that every row represents a trace.   
+
+To illustrate this distinction consider the following examples:
+```rust
+fn multiple_ndarray_traces_over_columns(show: bool) {
+    let n: usize = 11;
+    let t: Array<f64, Ix1> = Array::range(0., 10., 10. / n as f64);
+    let mut ys: Array<f64, Ix2> = Array::zeros((11, 11));
+    let mut count = 0.;
+    for mut row in ys.gencolumns_mut() {
+        for index in 0..row.len() {
+            row[index] = count + (index as f64).powf(2.);
+        }
+        count += 1.;
+    }
+
+    let traces =
+        Scatter::default()
+            .mode(Mode::LinesMarkers)
+            .to_traces(t, ys, ArrayTraces::OverColumns);
+
+    let mut plot = Plot::new();
+    plot.add_traces(traces);
+    if show {
+        plot.show();
+    }
+    println!("{}", plot.to_inline_html(Some("multiple_ndarray_traces_over_columns")));
+}
+```
+<div id="multiple_ndarray_traces_over_columns" class="plotly-graph-div" style="height:100%; width:100%;"></div>
+<script type="text/javascript">
+    window.PLOTLYENV=window.PLOTLYENV || {};
+    if (document.getElementById("multiple_ndarray_traces_over_columns")) {
+        var d3 = Plotly.d3;
+        var image_element= d3.select('#image-export');
+        var trace_0 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[0.0,1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0,100.0]};
+var trace_1 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[1.0,2.0,5.0,10.0,17.0,26.0,37.0,50.0,65.0,82.0,101.0]};
+var trace_2 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[2.0,3.0,6.0,11.0,18.0,27.0,38.0,51.0,66.0,83.0,102.0]};
+var trace_3 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[3.0,4.0,7.0,12.0,19.0,28.0,39.0,52.0,67.0,84.0,103.0]};
+var trace_4 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[4.0,5.0,8.0,13.0,20.0,29.0,40.0,53.0,68.0,85.0,104.0]};
+var trace_5 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[5.0,6.0,9.0,14.0,21.0,30.0,41.0,54.0,69.0,86.0,105.0]};
+var trace_6 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[6.0,7.0,10.0,15.0,22.0,31.0,42.0,55.0,70.0,87.0,106.0]};
+var trace_7 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[7.0,8.0,11.0,16.0,23.0,32.0,43.0,56.0,71.0,88.0,107.0]};
+var trace_8 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[8.0,9.0,12.0,17.0,24.0,33.0,44.0,57.0,72.0,89.0,108.0]};
+var trace_9 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[9.0,10.0,13.0,18.0,25.0,34.0,45.0,58.0,73.0,90.0,109.0]};
+var trace_10 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[10.0,11.0,14.0,19.0,26.0,35.0,46.0,59.0,74.0,91.0,110.0]};
+var data = [trace_0,trace_1,trace_2,trace_3,trace_4,trace_5,trace_6,trace_7,trace_8,trace_9,trace_10];
+var layout = {};
+        Plotly.newPlot('multiple_ndarray_traces_over_columns', data, layout, {"responsive": true});
+    };
+</script>
+
+Replacing `ArrayTraces::OverColumns` with `ArrayTraces::OverRows` results in the following:
+
+<div id="multiple_ndarray_traces_over_rows" class="plotly-graph-div" style="height:100%; width:100%;"></div>
+<script type="text/javascript">
+    window.PLOTLYENV=window.PLOTLYENV || {};
+    if (document.getElementById("multiple_ndarray_traces_over_rows")) {
+        var d3 = Plotly.d3;
+        var image_element= d3.select('#image-export');
+        var trace_0 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0]};
+var trace_1 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0]};
+var trace_2 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0]};
+var trace_3 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0]};
+var trace_4 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[16.0,17.0,18.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,26.0]};
+var trace_5 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[25.0,26.0,27.0,28.0,29.0,30.0,31.0,32.0,33.0,34.0,35.0]};
+var trace_6 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[36.0,37.0,38.0,39.0,40.0,41.0,42.0,43.0,44.0,45.0,46.0]};
+var trace_7 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[49.0,50.0,51.0,52.0,53.0,54.0,55.0,56.0,57.0,58.0,59.0]};
+var trace_8 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[64.0,65.0,66.0,67.0,68.0,69.0,70.0,71.0,72.0,73.0,74.0]};
+var trace_9 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[81.0,82.0,83.0,84.0,85.0,86.0,87.0,88.0,89.0,90.0,91.0]};
+var trace_10 = {"type":"scatter","mode":"lines+markers","x":[0.0,0.9090909090909091,1.8181818181818181,2.727272727272727,3.6363636363636362,4.545454545454545,5.454545454545454,6.363636363636363,7.2727272727272725,8.181818181818182,9.09090909090909],"y":[100.0,101.0,102.0,103.0,104.0,105.0,106.0,107.0,108.0,109.0,110.0]};
+var data = [trace_0,trace_1,trace_2,trace_3,trace_4,trace_5,trace_6,trace_7,trace_8,trace_9,trace_10];
+var layout = {};
+        Plotly.newPlot('multiple_ndarray_traces_over_rows', data, layout, {"responsive": true});
+    };
+</script>

plotly/Cargo.toml

@@ -19,23 +19,21 @@ exclude = [
 [features]
 # Adds plot save functionality to the following formats: png, jpeg, webp, svg, pdf and eps.
 kaleido = ["plotly_kaleido"]
-ndarray = ["plotly_ndarray"]
+plotly_ndarray = ["ndarray"]
 
 [dependencies]
 plotly_kaleido = { version = "0.2.0", path = "../plotly_kaleido", optional = true }
-plotly_ndarray = { version = "0.1.0", path = "../plotly_ndarray", optional = true }
+ndarray = { version = ">=0.13.1", optional = true }
 serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
 askama = "0.9.0"
 rand = "0.7.3"
 rand_distr = "0.2.2"
 
-chrono = { version = "0.4.13", features = ["serde"] }
-time = "0.2.16"
 
 [dev-dependencies]
 plotly_kaleido = { version = "0.2.0", path = "../plotly_kaleido" }
-plotly_ndarray = { version = "0.1.0", path = "../plotly_ndarray" }
 itertools = "0.9.0"
 itertools-num = "0.1.3"
-csv = "1.1.3"
+csv = "1.1.3"
+ndarray = "0.13.1"

plotly/README.md

@@ -50,7 +50,7 @@ The following feature flags are available:
     * Optional, compatible with Rust stable.
     * Adds plot save functionality to the following formats: png, jpeg, webp, svg, pdf and eps.
     * Requires some additional configuration, see [plotly_kaleido](https://github.com/igiagkiozis/plotly/tree/master/plotly_kaleido).
-* `ndarray`
+* `plotly_ndarray`
     * Optional, compatible with Rust stable.
     * Adds support for creating plots directly using [ndarray](https://github.com/rust-ndarray/ndarray) types.