rename Python package, update python code and scripts, update jvm-mav…

…en scrips, update readme

.gitignore

@@ -14,7 +14,7 @@ node_modules/
 *.egg-info
 **/dist
 venv
-python-package/datalore_plot/package_data
+python-package/lets_plot/package_data
 __pycache__/
 /python-package/*.ipynb
 

CHANGELOG.md

@@ -1,4 +1,4 @@
-# Datalore-plot changelog
+# lets-plot changelog
 
 All notable changes to this project will be documented in this file.
 

README.md

@@ -1,13 +1,13 @@
-# Datalore Plot
+# Lets-Plot
 
 <a href="https://opensource.org/licenses/MIT">
 <img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="Couldn't load MIT license svg"/>
 </a>
 
 ###
-**Datalore Plot** is an open-source plotting library for statistical data. It is implemented using the [Kotlin programming language](https://kotlinlang.org/).
+**Lets-Plot** is an open-source plotting library for statistical data. It is implemented using the [Kotlin programming language](https://kotlinlang.org/).
 
-The design of Datalore Plot library is heavily influenced by Leland Wilkinson work [The Grammar of Graphics](https://www.goodreads.com/book/show/2549408.The_Grammar_of_Graphics) describing the deep features that underlie all statistical graphics.
+The design of Lets-Plot library is heavily influenced by Leland Wilkinson work [The Grammar of Graphics](https://www.goodreads.com/book/show/2549408.The_Grammar_of_Graphics) describing the deep features that underlie all statistical graphics.
 
 > This grammar [...] is made up of a set of independent components that can be composed in many different ways. This makes [it] very powerful because you are not limited to a set of pre-specified graphics, but you can create new graphics that are precisely tailored for your problem.
 > - Hadley Wickham, "ggplot2: [Elegant Graphics for Data Analysis](https://www.goodreads.com/book/show/6829192-ggplot2)"
@@ -16,6 +16,6 @@ Due to the unique multi-platform nature of Kotlin programming language, you can
 
 ### Python Extension 
 
-[Learn](README_PYTHON.md) how to create plots in Jupyter using the Datalore Plot python package.
+[Learn](README_PYTHON.md) how to create plots in Jupyter using the Lets-Plot python package.
 
 <img src="https://github.com/alshan/jupyter-examples/blob/master/images/quickstart.png" alt="Couldn't load quickstart.png" width="505" height="260"/>

README_PYTHON.md

@@ -1,4 +1,4 @@
-# Datalore Plot for Python
+# Lets-Plot for Python
 
 <table>
  <tr>
@@ -27,32 +27,32 @@
 
 ### Implementation Overview
 
-The Datalore Plot python extension includes native backend and a Python API, which was mostly based on the [`ggplot2`](https://ggplot2.tidyverse.org/) package well-known to data scientists who use R.
+The Lets-Plot python extension includes native backend and a Python API, which was mostly based on the [`ggplot2`](https://ggplot2.tidyverse.org/) package well-known to data scientists who use R.
 
 R `ggplot2` has extensive documentation and a multitude of examples and therefore is an excellent resource for those who want to learn the grammar of graphics. 
 
 Note that the Python API being very similar yet is different in detail from R. Although we have not implemented the entire ggplot2 API in our Python package, we have added a few [new functions](#unfamiliar_functions_used_in_the_examples) and [built-in sampling](#sampling) to our Python API.
 
-You can try the Datalore Plot library in [Datalore](https://blog.jetbrains.com/blog/2018/10/17/datalore-1-0-intelligent-web-application-for-data-analysis/). Datalore Plot is available in Datalore out-of-the-box and is almost identical to the one we ship as PyPI package. This is because Datalore Plot is an offshoot of the Datalore project from which it was extracted to a separate plotting library.
+You can try the Lets-Plot library in [Datalore](https://blog.jetbrains.com/blog/2018/10/17/datalore-1-0-intelligent-web-application-for-data-analysis/). Datalore Plot is available in Datalore out-of-the-box and is almost identical to the one we ship as PyPI package. This is because Datalore Plot is an offshoot of the Datalore project from which it was extracted to a separate plotting library.
 
-One important difference is that the python package in Datalore is named **datalore.plot** and the package you install from PyPI has name **datalore_plot**.
+One important difference is that the python package in Datalore is named **datalore.plot** and the package you install from PyPI has name **lets_plot**.
 
 The advantage of [Datalore](https://blog.jetbrains.com/blog/2018/10/17/datalore-1-0-intelligent-web-application-for-data-analysis/) as a learning tool in comparison to Jupyter is that it is equipped with very friendly Python editor which comes with auto-completion, intentions, and other useful coding assistance features.
 
 
 ### Installation
 
-To install the Datalore Plot library, run the following command:
+To install the Lets-Plot library, run the following command:
 ```shell script
 pip install lets-plot
 ```
 
 ### Quickstart in Jupyter
 
-To evaluate the plotting capabilities of Datalore Plot, add the following code to a Jupyter notebook:
+To evaluate the plotting capabilities of Lets-Plot, add the following code to a Jupyter notebook:
 ```python
 import numpy as np
-from datalore_plot import *
+from lets_plot import *
 
 np.random.seed(12)
 data = dict(
@@ -70,7 +70,7 @@ ggplot(data, aes(x='rating', fill='cond')) + ggsize(500, 250) \
 
 ### Examples
 
-Try the following examples to study more features of the Datalore Plot library.
+Try the following examples to study more features of the Lets-Plot library.
 
 TBD: replace temporary URLs ---> permanent 
 
@@ -100,18 +100,18 @@ Various presentation options:
 * `ggsize()` - sets size of the plot. Used in many examples starting from `quickstart`
 * `geom_density2df()` - fills space between equal density lines on 2D density plot. Similar to `geom_density2d` but supports `fill` aesthetic.
 
- Example: [density_2d.ipynb](https://nbviewer.jupyter.org/github/JetBrains/datalore-plot/blob/master/docs/examples/jupyter-notebooks/density_2d.ipynb) 
+ Example: [density_2d.ipynb](https://nbviewer.jupyter.org/github/JetBrains/lets-plot/blob/master/docs/examples/jupyter-notebooks/density_2d.ipynb) 
 
 * `geom_contourf()` - fills space between lines of equal level of bivariate function. Similar to `geom_contour` but supports `fill` aesthetic.
 
- Example: [contours.ipynb](https://nbviewer.jupyter.org/github/JetBrains/datalore-plot/blob/master/docs/examples/jupyter-notebooks/contours.ipynb) 
+ Example: [contours.ipynb](https://nbviewer.jupyter.org/github/JetBrains/lets-plot/blob/master/docs/examples/jupyter-notebooks/contours.ipynb) 
 
 
 ### Sampling 
 
 Sampling is a special technique of data transformation, which helps dealing with large datasets and overplotting.
 
-[Learn more](docs/sampling_python.md) about sampling in Datalore Plot. 
+[Learn more](docs/sampling_python.md) about sampling in Lets-Plot. 
 
 
 ### License

RELEASE.md

@@ -9,7 +9,7 @@
 ##### 2. Set production version in the properties (remove _"-SNAPSHOT"_ and _"dev"_): 
 
  - `version` and `js_artifact_version` in `build.gradle` 
- - `__version__` in `python-package/datalore_plot/_version.py`
+ - `__version__` in `python-package/lets_plot/_version.py`
 
 ##### 3. Push the version changes and git tag:
   
@@ -19,7 +19,7 @@
 ##### 4. Prepare to the next dev cycle: increment versions and add _"-SNAPSHOT"_ and _"dev"_:
 
  - `version` and `js_artifact_version` in `build.gradle` 
- - `__version__` in `python-package/datalore_plot/_version.py`
+ - `__version__` in `python-package/lets_plot/_version.py`
 
 ##### 5. Push new dev version to GitHub.
 

build.gradle

@@ -48,7 +48,7 @@ allprojects {
 // println "--- project ---: $project.name $project.rootDir"
 }
 
-def include_sources_datalorePlotJvmCommon = [
+def include_sources_letsPlotJvmCommon = [
  'base-portable',
  'base',
 // 'mapper-core',
@@ -68,7 +68,7 @@ def include_sources_datalorePlotJvmCommon = [
 // 'vis-demo-common-jfx',
 ]
 
-def include_sources_datalorePlotJvmJfx = [
+def include_sources_letsPlotJvmJfx = [
  'mapper-core',
  'vis-svg-mapper',
  'vis-svg-mapper-jfx',
@@ -77,8 +77,8 @@ def include_sources_datalorePlotJvmJfx = [
 ]
 
 configurations {
- datalorePlotJvmCommonSources
- datalorePlotJvmJfxSources
+ letsPlotJvmCommonSources
+ letsPlotJvmJfxSources
 }
 
 subprojects {
@@ -100,22 +100,22 @@ subprojects {
  }
 
  // jar jvm sources of this project
- if (name in include_sources_datalorePlotJvmCommon || name in include_sources_datalorePlotJvmJfx) {
+ if (name in include_sources_letsPlotJvmCommon || name in include_sources_letsPlotJvmJfx) {
  apply plugin: 'kotlin-multiplatform'
  kotlin.jvm {} // for `jvmSourcesJar` task
  build.dependsOn += jvmSourcesJar
  }
 
  // make build configuration depend on sources jars
  def sources_jar_path = "${buildDir}/libs/${name}-jvm-${version}-sources.jar"
- if (name in include_sources_datalorePlotJvmCommon) {
+ if (name in include_sources_letsPlotJvmCommon) {
  rootProject.dependencies {
- datalorePlotJvmCommonSources files(sources_jar_path)
+ letsPlotJvmCommonSources files(sources_jar_path)
  }
  }
- if (name in include_sources_datalorePlotJvmJfx) {
+ if (name in include_sources_letsPlotJvmJfx) {
  rootProject.dependencies {
- datalorePlotJvmJfxSources files(sources_jar_path)
+ letsPlotJvmJfxSources files(sources_jar_path)
  }
  }
 }

docs/examples/jupyter-notebooks/contours.ipynb

@@ -11,20 +11,20 @@
  "\n",
  " <div id=\"NGHV64\"></div>\n",
  " <script type=\"text/javascript\">\n",
- " if(!window.datalorePlotCallQueue) {\n",
- " window.datalorePlotCallQueue = [];\n",
+ " if(!window.letsPlotCallQueue) {\n",
+ " window.letsPlotCallQueue = [];\n",
  " }; \n",
- " window.datalorePlotCall = function(f) {\n",
- " window.datalorePlotCallQueue.push(f);\n",
+ " window.letsPlotCall = function(f) {\n",
+ " window.letsPlotCallQueue.push(f);\n",
  " };\n",
  " (function() {\n",
  " var script = document.createElement(\"script\");\n",
  " script.type = \"text/javascript\";\n",
  " script.src = \"https://dl.bintray.com/jetbrains/datalore-plot/datalore-plot-0.0.1rc4.min.js\";\n",
  " script.onload = function() {\n",
- " window.datalorePlotCall = function(f) {f();};\n",
- " window.datalorePlotCallQueue.forEach(function(f) {f();});\n",
- " window.datalorePlotCallQueue = [];\n",
+ " window.letsPlotCall = function(f) {f();};\n",
+ " window.letsPlotCallQueue.forEach(function(f) {f();});\n",
+ " window.letsPlotCallQueue = [];\n",
  " \n",
  " var div = document.createElement(\"div\");\n",
  " div.style.color = 'darkblue';\n",
@@ -33,8 +33,8 @@
  " \n",
  " };\n",
  " script.onerror = function(event) {\n",
- " window.datalorePlotCall = function(f) {}; // noop\n",
- " window.datalorePlotCallQueue = [];\n",
+ " window.letsPlotCall = function(f) {}; // noop\n",
+ " window.letsPlotCallQueue = [];\n",
  " var div = document.createElement(\"div\");\n",
  " div.style.color = 'darkred';\n",
  " div.textContent = 'Error loading Datalore Plot JS';\n",
@@ -53,9 +53,9 @@
  ],
  "source": [
  "import numpy as np\n",
- "from datalore_plot import *\n",
+ "from lets_plot import *\n",
  "\n",
- "load_datalore_plot_js()"
+ "load_lets_plot_js()"
  ]
  },
  {
@@ -122,15 +122,15 @@
  "}]\n",
  "};\n",
  " var plotContainer = document.getElementById(\"plot_output_chjHyJ\");\n",
- " window.datalorePlotCall(function() {{\n",
+ " window.letsPlotCall(function() {{\n",
  " DatalorePlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
  " }});\n",
  " })(); \n",
  " </script>\n",
  " "
  ],
  "text/plain": [
- "<datalore_plot.plot.core.PlotSpec at 0x1161cfed0>"
+ "<lets_plot.plot.core.PlotSpec at 0x1161cfed0>"
  ]
  },
  "execution_count": 4,
@@ -183,15 +183,15 @@
  "}]\n",
  "};\n",
  " var plotContainer = document.getElementById(\"plot_output_SK36LD\");\n",
- " window.datalorePlotCall(function() {{\n",
+ " window.letsPlotCall(function() {{\n",
  " DatalorePlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
  " }});\n",
  " })(); \n",
  " </script>\n",
  " "
  ],
  "text/plain": [
- "<datalore_plot.plot.core.PlotSpec at 0x105813b10>"
+ "<lets_plot.plot.core.PlotSpec at 0x105813b10>"
  ]
  },
  "execution_count": 5,
@@ -256,15 +256,15 @@
  "}]\n",
  "};\n",
  " var plotContainer = document.getElementById(\"plot_output_BQj8Gs\");\n",
- " window.datalorePlotCall(function() {{\n",
+ " window.letsPlotCall(function() {{\n",
  " DatalorePlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
  " }});\n",
  " })(); \n",
  " </script>\n",
  " "
  ],
  "text/plain": [
- "<datalore_plot.plot.core.PlotSpec at 0x1161d78d0>"
+ "<lets_plot.plot.core.PlotSpec at 0x1161d78d0>"
  ]
  },
  "execution_count": 6,
@@ -335,15 +335,15 @@
  "}]\n",
  "};\n",
  " var plotContainer = document.getElementById(\"plot_output_ZlY0P3\");\n",
- " window.datalorePlotCall(function() {{\n",
+ " window.letsPlotCall(function() {{\n",
  " DatalorePlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
  " }});\n",
  " })(); \n",
  " </script>\n",
  " "
  ],
  "text/plain": [
- "<datalore_plot.plot.core.PlotSpec at 0x11621de50>"
+ "<lets_plot.plot.core.PlotSpec at 0x11621de50>"
  ]
  },
  "execution_count": 7,
@@ -400,15 +400,15 @@
  "}]\n",
  "};\n",
  " var plotContainer = document.getElementById(\"plot_output_ZUXwIj\");\n",
- " window.datalorePlotCall(function() {{\n",
+ " window.letsPlotCall(function() {{\n",
  " DatalorePlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
  " }});\n",
  " })(); \n",
  " </script>\n",
  " "
  ],
  "text/plain": [
- "<datalore_plot.plot.core.PlotSpec at 0x105813490>"
+ "<lets_plot.plot.core.PlotSpec at 0x105813490>"
  ]
  },
  "execution_count": 8,
@@ -459,15 +459,15 @@
  "}]\n",
  "};\n",
  " var plotContainer = document.getElementById(\"plot_output_p2VGQa\");\n",
- " window.datalorePlotCall(function() {{\n",
+ " window.letsPlotCall(function() {{\n",
  " DatalorePlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
  " }});\n",
  " })(); \n",
  " </script>\n",
  " "
  ],
  "text/plain": [
- "<datalore_plot.plot.core.PlotSpec at 0x11621de10>"
+ "<lets_plot.plot.core.PlotSpec at 0x11621de10>"
  ]
  },
  "execution_count": 9,