diff --git a/Vagrantfile b/Vagrantfile
deleted file mode 100644
index 06d185fd4c1..00000000000
--- a/Vagrantfile
+++ /dev/null
@@ -1,124 +0,0 @@
-# -*- mode: ruby -*-
-# vi: set ft=ruby :
-
-# Vagrantfile API/syntax version. Don't touch unless you know what you're doing!
-VAGRANTFILE_API_VERSION = "2"
-
-Vagrant.configure(VAGRANTFILE_API_VERSION) do |config|
- # All Vagrant configuration is done here. The most common configuration
- # options are documented and commented below. For a complete reference,
- # please see the online documentation at vagrantup.com.
-
- # Every Vagrant virtual environment requires a box to build off of.
- config.vm.box = "ubuntu/trusty64"
-
- config.vm.provision :shell, path: "bootstrap_trusty64.sh", privileged:false
-
- # Disable automatic box update checking. If you disable this, then
- # boxes will only be checked for updates when the user runs
- # `vagrant box outdated`. This is not recommended.
- # config.vm.box_check_update = false
-
- # Create a forwarded port mapping which allows access to a specific port
- # within the machine from a port on the host machine. In the example below,
- # accessing "localhost:8080" will access port 80 on the guest machine.
- # config.vm.network "forwarded_port", guest: 80, host: 8080
-
- # Create a private network, which allows host-only access to the machine
- # using a specific IP.
- # config.vm.network "private_network", ip: "192.168.33.10"
-
- # Create a public network, which generally matched to bridged network.
- # Bridged networks make the machine appear as another physical device on
- # your network.
- # config.vm.network "public_network"
-
- # If true, then any SSH connections made will enable agent forwarding.
- # Default value: false
- # config.ssh.forward_agent = true
-
- # Share an additional folder to the guest VM. The first argument is
- # the path on the host to the actual folder. The second argument is
- # the path on the guest to mount the folder. And the optional third
- # argument is a set of non-required options.
- # config.vm.synced_folder "../data", "/vagrant_data"
-
- # Provider-specific configuration so you can fine-tune various
- # backing providers for Vagrant. These expose provider-specific options.
- # Example for VirtualBox:
- #
- # config.vm.provider "virtualbox" do |vb|
- # # Don't boot with headless mode
- # vb.gui = true
- #
- # # Use VBoxManage to customize the VM. For example to change memory:
- # vb.customize ["modifyvm", :id, "--memory", "1024"]
- # end
- #
- # View the documentation for the provider you're using for more
- # information on available options.
-
- # Enable provisioning with CFEngine. CFEngine Community packages are
- # automatically installed. For example, configure the host as a
- # policy server and optionally a policy file to run:
- #
- # config.vm.provision "cfengine" do |cf|
- # cf.am_policy_hub = true
- # # cf.run_file = "motd.cf"
- # end
- #
- # You can also configure and bootstrap a client to an existing
- # policy server:
- #
- # config.vm.provision "cfengine" do |cf|
- # cf.policy_server_address = "10.0.2.15"
- # end
-
- # Enable provisioning with Puppet stand alone. Puppet manifests
- # are contained in a directory path relative to this Vagrantfile.
- # You will need to create the manifests directory and a manifest in
- # the file default.pp in the manifests_path directory.
- #
- # config.vm.provision "puppet" do |puppet|
- # puppet.manifests_path = "manifests"
- # puppet.manifest_file = "default.pp"
- # end
-
- # Enable provisioning with chef solo, specifying a cookbooks path, roles
- # path, and data_bags path (all relative to this Vagrantfile), and adding
- # some recipes and/or roles.
- #
- # config.vm.provision "chef_solo" do |chef|
- # chef.cookbooks_path = "../my-recipes/cookbooks"
- # chef.roles_path = "../my-recipes/roles"
- # chef.data_bags_path = "../my-recipes/data_bags"
- # chef.add_recipe "mysql"
- # chef.add_role "web"
- #
- # # You may also specify custom JSON attributes:
- # chef.json = { mysql_password: "foo" }
- # end
-
- # Enable provisioning with chef server, specifying the chef server URL,
- # and the path to the validation key (relative to this Vagrantfile).
- #
- # The Opscode Platform uses HTTPS. Substitute your organization for
- # ORGNAME in the URL and validation key.
- #
- # If you have your own Chef Server, use the appropriate URL, which may be
- # HTTP instead of HTTPS depending on your configuration. Also change the
- # validation key to validation.pem.
- #
- # config.vm.provision "chef_client" do |chef|
- # chef.chef_server_url = "https://api.opscode.com/organizations/ORGNAME"
- # chef.validation_key_path = "ORGNAME-validator.pem"
- # end
- #
- # If you're using the Opscode platform, your validator client is
- # ORGNAME-validator, replacing ORGNAME with your organization name.
- #
- # If you have your own Chef Server, the default validation client name is
- # chef-validator, unless you changed the configuration.
- #
- # chef.validation_client_name = "ORGNAME-validator"
-end
diff --git a/bootstrap_trusty64.sh b/bootstrap_trusty64.sh
deleted file mode 100644
index b998a63517a..00000000000
--- a/bootstrap_trusty64.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/usr/bin/env bash
-
-sudo apt-get install git --yes
-
-wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
-bash miniconda.sh -b
-
-export PATH=/home/vagrant/miniconda/bin:$PATH
-conda update --yes conda
-
-conda create --yes -n tardis --file /vagrant/pip-requirements pip
-
-echo "export PATH=/home/vagrant/miniconda/bin:$PATH" >> .bashrc
-
-wget http://opensupernova.org/~ftsamis/tardis_example.tar.gz
-tar zxvf tardis_example.tar.gz
\ No newline at end of file
diff --git a/docs/examples/run_simple_example.ipynb b/docs/examples/run_simple_example.ipynb
new file mode 100644
index 00000000000..7cadb3d9a57
--- /dev/null
+++ b/docs/examples/run_simple_example.ipynb
@@ -0,0 +1,1222 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Simple Jupyter TARDIS Example ###"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+ " \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from tardis import run_tardis\n",
+ "from tardis.io.atom_data.util import download_atom_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\u001b[1mtardis.io.atom_data.atom_web_download\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Downloading atomic data from https://media.githubusercontent.com/media/tardis-sn/tardis-refdata/master/atom_data/kurucz_cd23_chianti_H_He.h5 to /Users/wkerzend/projects/tardis/tardis-data/kurucz_cd23_chianti_H_He.h5 (\u001b[1matom_web_download.py\u001b[0m:37)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "33dd6796be49474b81854df7f5cf0778",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(IntProgress(value=0, description='kurucz_cd23_chianti_H_He.h5', max=73704476, style=ProgressSty…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# the data is automatically downloaded\n",
+ "download_atom_data('kurucz_cd23_chianti_H_He')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\u001b[1mtardis.plasma.standard_plasmas\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Reading Atomic Data from kurucz_cd23_chianti_H_He.h5 (\u001b[1mstandard_plasmas.py\u001b[0m:76)\n",
+ "[\u001b[1mtardis.io.atom_data.util\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Atom Data kurucz_cd23_chianti_H_He.h5 not found in local path. Exists in TARDIS Data repo /Users/wkerzend/projects/tardis/tardis-data/kurucz_cd23_chianti_H_He.h5 (\u001b[1mutil.py\u001b[0m:29)\n",
+ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3267: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
+ " exec(code_obj, self.user_global_ns, self.user_ns)\n",
+ " (\u001b[1mwarnings.py\u001b[0m:99)\n",
+ "[\u001b[1mtardis.io.atom_data.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Read Atom Data with UUID=6f7b09e887a311e7a06b246e96350010 and MD5=864f1753714343c41f99cb065710cace. (\u001b[1mbase.py\u001b[0m:175)\n",
+ "[\u001b[1mtardis.io.atom_data.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Non provided atomic data: synpp_refs, ion_cx_th_data, ion_cx_sp_data (\u001b[1mbase.py\u001b[0m:178)\n",
+ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/astropy/units/quantity.py:1067: AstropyDeprecationWarning: The truth value of a Quantity is ambiguous. In the future this will raise a ValueError.\n",
+ " AstropyDeprecationWarning)\n",
+ " (\u001b[1mwarnings.py\u001b[0m:99)\n",
+ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] /Users/wkerzend/python/tardis/tardis/plasma/properties/ion_population.py:59: FutureWarning: \n",
+ "Passing list-likes to .loc or [] with any missing label will raise\n",
+ "KeyError in the future, you can use .reindex() as an alternative.\n",
+ "\n",
+ "See the documentation here:\n",
+ "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
+ " partition_function.index].dropna())\n",
+ " (\u001b[1mwarnings.py\u001b[0m:99)\n",
+ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/astropy/units/equivalencies.py:90: RuntimeWarning: divide by zero encountered in double_scalars\n",
+ " (si.m, si.Hz, lambda x: _si.c.value / x),\n",
+ " (\u001b[1mwarnings.py\u001b[0m:99)\n",
+ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/astropy/units/quantity.py:1067: AstropyDeprecationWarning: The truth value of a Quantity is ambiguous. In the future this will raise a ValueError.\n",
+ " AstropyDeprecationWarning)\n",
+ " (\u001b[1mwarnings.py\u001b[0m:99)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 1/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 7.96915e+42 erg / s Luminosity absorbed = 2.63370e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 9926.501965 10171.209103 0.400392 0.500372\n",
+ "\t5 9852.611678 10306.111379 0.211205 0.191331\n",
+ "\t10 9779.813302 10174.379204 0.142695 0.116864\n",
+ "\t15 9708.082813 9910.442275 0.104556 0.085962\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 9933.952 K -- next t_inner 11453.040 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] /Users/wkerzend/python/tardis/tardis/plasma/properties/ion_population.py:59: FutureWarning: \n",
+ "Passing list-likes to .loc or [] with any missing label will raise\n",
+ "KeyError in the future, you can use .reindex() as an alternative.\n",
+ "\n",
+ "See the documentation here:\n",
+ "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
+ " partition_function.index].dropna())\n",
+ " (\u001b[1mwarnings.py\u001b[0m:99)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 2/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 1.40398e+43 erg / s Luminosity absorbed = 4.68639e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 10171.209103 11518.516702 0.500372 0.538298\n",
+ "\t5 10306.111379 11554.412119 0.191331 0.217946\n",
+ "\t10 10174.379204 11373.574056 0.116864 0.132935\n",
+ "\t15 9910.442275 11040.788763 0.085962 0.099700\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 11453.040 K -- next t_inner 9948.201 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 3/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 8.16814e+42 erg / s Luminosity absorbed = 2.50161e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 11518.516702 10501.129902 0.538298 0.438591\n",
+ "\t5 11554.412119 10869.940791 0.217946 0.160809\n",
+ "\t10 11373.574056 10558.269547 0.132935 0.103807\n",
+ "\t15 11040.788763 10185.648701 0.099700 0.079165\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 9948.201 K -- next t_inner 11328.896 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 4/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 1.34487e+43 erg / s Luminosity absorbed = 4.47931e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 10501.129902 11527.008679 0.438591 0.512509\n",
+ "\t5 10869.940791 11706.138980 0.160809 0.196458\n",
+ "\t10 10558.269547 11444.208532 0.103807 0.124092\n",
+ "\t15 10185.648701 11085.857161 0.079165 0.093694\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 11328.896 K -- next t_inner 10054.300 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 5/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 8.44348e+42 erg / s Luminosity absorbed = 2.68000e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 11527.008679 10659.382763 0.512509 0.433573\n",
+ "\t5 11706.138980 11038.244897 0.196458 0.158494\n",
+ "\t10 11444.208532 10790.696726 0.124092 0.099660\n",
+ "\t15 11085.857161 10450.666394 0.093694 0.074550\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 10054.300 K -- next t_inner 11261.489 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 6/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 1.32492e+43 erg / s Luminosity absorbed = 4.26229e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 10659.382763 11507.437689 0.433573 0.499441\n",
+ "\t5 11038.244897 11634.568776 0.158494 0.197953\n",
+ "\t10 10790.696726 11418.594559 0.099660 0.124085\n",
+ "\t15 10450.666394 10999.619473 0.074550 0.094676\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 11261.489 K -- next t_inner 10069.444 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 7/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 8.49858e+42 erg / s Luminosity absorbed = 2.69247e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 11507.437689 10679.650246 0.499441 0.431088\n",
+ "\t5 11634.568776 11048.266431 0.197953 0.157179\n",
+ "\t10 11418.594559 10825.714870 0.124085 0.098608\n",
+ "\t15 10999.619473 10506.321595 0.094676 0.073794\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 10069.444 K -- next t_inner 11241.826 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 8/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 1.30632e+43 erg / s Luminosity absorbed = 4.31719e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 10679.650246 11459.828312 0.431088 0.506176\n",
+ "\t5 11048.266431 11688.139012 0.157179 0.193468\n",
+ "\t10 10825.714870 11441.768967 0.098608 0.122412\n",
+ "\t15 10506.321595 11099.449886 0.073794 0.090915\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 11241.826 K -- next t_inner 10123.177 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 9/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 8.68357e+42 erg / s Luminosity absorbed = 2.75015e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 11459.828312 10666.223660 0.506176 0.445097\n",
+ "\t5 11688.139012 11000.288585 0.193468 0.163424\n",
+ "\t10 11441.768967 10822.884326 0.122412 0.101641\n",
+ "\t15 11099.449886 10384.181273 0.090915 0.078432\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 10123.177 K -- next t_inner 11180.783 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 10/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 1.28170e+43 erg / s Luminosity absorbed = 4.19511e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 10666.223660 11427.410445 0.445097 0.500788\n",
+ "\t5 11000.288585 11612.349822 0.163424 0.193129\n",
+ "\t10 10822.884326 11373.245169 0.101641 0.121622\n",
+ "\t15 10384.181273 11047.955461 0.078432 0.090937\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 11180.783 K -- next t_inner 10164.432 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 11/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 8.79074e+42 erg / s Luminosity absorbed = 2.82804e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 11427.410445 10741.337822 0.500788 0.439938\n",
+ "\t5 11612.349822 11230.515356 0.193129 0.153637\n",
+ "\t10 11373.245169 10870.696315 0.121622 0.101050\n",
+ "\t15 11047.955461 10538.170159 0.090937 0.074680\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 10164.432 K -- next t_inner 11157.711 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 12/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 1.27128e+43 erg / s Luminosity absorbed = 4.15415e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 10741.337822 11507.877578 0.439938 0.483114\n",
+ "\t5 11230.515356 11808.852053 0.153637 0.181826\n",
+ "\t10 10870.696315 11557.412757 0.101050 0.113563\n",
+ "\t15 10538.170159 11117.762050 0.074680 0.087418\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 11157.711 K -- next t_inner 10184.966 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 13/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 8.90399e+42 erg / s Luminosity absorbed = 2.80864e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 11507.877578 10727.217531 0.483114 0.443706\n",
+ "\t5 11808.852053 11171.758889 0.181826 0.160824\n",
+ "\t10 11557.412757 10971.722637 0.113563 0.098588\n",
+ "\t15 11117.762050 10574.916600 0.087418 0.075266\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 10184.966 K -- next t_inner 11108.923 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 14/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 1.25052e+43 erg / s Luminosity absorbed = 4.07335e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 10727.217531 11413.626870 0.443706 0.489526\n",
+ "\t5 11171.758889 11697.006992 0.160824 0.184795\n",
+ "\t10 10971.722637 11424.445844 0.098588 0.116722\n",
+ "\t15 10574.916600 11047.434163 0.075266 0.088067\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 11108.923 K -- next t_inner 10224.266 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 15/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 9.00727e+42 erg / s Luminosity absorbed = 2.88724e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 11413.626870 10712.622749 0.489526 0.455660\n",
+ "\t5 11697.006992 11125.894032 0.184795 0.164091\n",
+ "\t10 11424.445844 10816.390451 0.116722 0.104037\n",
+ "\t15 11047.434163 10421.687097 0.088067 0.080401\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 10224.266 K -- next t_inner 11087.666 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 16/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 1.23781e+43 erg / s Luminosity absorbed = 4.07435e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 10712.622749 11352.158138 0.455660 0.500754\n",
+ "\t5 11125.894032 11644.968350 0.164091 0.185289\n",
+ "\t10 10816.390451 11320.695930 0.104037 0.119195\n",
+ "\t15 10421.687097 10965.228363 0.080401 0.089940\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 11087.666 K -- next t_inner 10256.946 K (\u001b[1mbase.py\u001b[0m:350)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 17/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 9.18956e+42 erg / s Luminosity absorbed = 2.86249e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 11352.158138 10738.799194 0.500754 0.452965\n",
+ "\t5 11644.968350 11210.269586 0.185289 0.160945\n",
+ "\t10 11320.695930 10911.463205 0.119195 0.103910\n",
+ "\t15 10965.228363 10545.065525 0.089940 0.078433\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 10256.946 K -- next t_inner 11012.230 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 18/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 1.20741e+43 erg / s Luminosity absorbed = 3.93375e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 10738.799194 11316.665617 0.452965 0.492135\n",
+ "\t5 11210.269586 11599.094127 0.160945 0.183929\n",
+ "\t10 10911.463205 11403.508986 0.103910 0.114400\n",
+ "\t15 10545.065525 10958.974248 0.078433 0.087415\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 11012.230 K -- next t_inner 10314.623 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 19/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 9.29848e+42 erg / s Luminosity absorbed = 3.02011e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Plasma stratification:\n",
+ "\t t_rad next_t_rad w next_w\n",
+ "\tShell \n",
+ "\t0 11316.665617 10862.505720 0.492135 0.444495\n",
+ "\t5 11599.094127 11194.462074 0.183929 0.162842\n",
+ "\t10 11403.508986 11065.620645 0.114400 0.099372\n",
+ "\t15 10958.974248 10599.851144 0.087415 0.077690\n",
+ "\n",
+ " (\u001b[1mbase.py\u001b[0m:348)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] t_inner 10314.623 K -- next t_inner 11009.102 K (\u001b[1mbase.py\u001b[0m:350)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Starting iteration 20/20 (\u001b[1mbase.py\u001b[0m:266)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Luminosity emitted = 1.21062e+43 erg / s Luminosity absorbed = 3.88405e+42 erg / s Luminosity requested = 1.05928e+43 erg / s (\u001b[1mbase.py\u001b[0m:357)\n",
+ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] Simulation finished in 20 iterations and took 23.02 s (\u001b[1mbase.py\u001b[0m:306)\n"
+ ]
+ }
+ ],
+ "source": [
+ "#TARDIS now uses the data in the data repo\n",
+ "sim = run_tardis('tardis_example.yml')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/matplotlib/style/core.py:167: UserWarning: In /Users/wkerzend/.matplotlib/stylelib/talk_default.mplstyle: \n",
+ "The text.latex.unicode rcparam was deprecated in Matplotlib 2.2 and will be removed in 3.1.\n",
+ " styles = read_style_directory(stylelib_path)\n",
+ " (\u001b[1mwarnings.py\u001b[0m:99)\n",
+ "Populating the interactive namespace from numpy and matplotlib\n",
+ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] /Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/astropy/units/quantity.py:1067: AstropyDeprecationWarning: The truth value of a Quantity is ambiguous. In the future this will raise a ValueError.\n",
+ " AstropyDeprecationWarning)\n",
+ " (\u001b[1mwarnings.py\u001b[0m:99)\n",
+ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] /Users/wkerzend/python/tardis/tardis/montecarlo/formal_integral.py:167: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
+ " result = pd.DataFrame(att_S_ul.as_matrix(), index=transitions.transition_line_id.values)\n",
+ " (\u001b[1mwarnings.py\u001b[0m:99)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
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+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
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+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
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+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
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+ "\n",
+ " var fig = this;\n",
+ "\n",
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+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
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+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
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+ " }\n",
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+ " this.ws.onmessage = this._make_on_message_function(this);\n",
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+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
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+ " '');\n",
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+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('');\n",
+ "\n",
+ " var fmt_picker = $('');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('![](\"')
');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('');\n",
+ " var button = $('');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%pylab notebook\n",
+ "\n",
+ "spectrum = sim.runner.spectrum_integrated\n",
+ "\n",
+ "plot(spectrum.wavelength, spectrum.luminosity_density_lambda)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/physics/plasma/plasma_plots/lte_ionization_balance.py b/docs/physics/plasma/plasma_plots/lte_ionization_balance.py
index 7a8e1951bce..219b9d8245b 100644
--- a/docs/physics/plasma/plasma_plots/lte_ionization_balance.py
+++ b/docs/physics/plasma/plasma_plots/lte_ionization_balance.py
@@ -3,11 +3,9 @@
from matplotlib import colors
import tardis.util.base
-from tardis import util
-from tardis.io import atomic
+from tardis.io.atom_data import AtomData
import numpy as np
import pandas as pd
-from astropy import units as u
#Making 2 Figures for ionization balance and level populations
@@ -18,10 +16,10 @@
ax2 = plt.figure(2).add_subplot(111)
# expanding the tilde to the users directory
-atom_fname = os.path.join(os.path.dirname(atomic.__file__), 'data', 'atom_data.h5')
+#atom_fname = os.path.join(os.path.dirname(base.__file__), 'data', 'atom_data.h5')
# reading in the HDF5 File
-atom_data = atomic.AtomData.from_hdf(atom_fname)
+atom_data = AtomData.from_hdf(atom_fname)
#The atom_data needs to be prepared to create indices. The Class needs to know which atomic numbers are needed for the
#calculation and what line interaction is needed (for "downbranch" and "macroatom" the code creates special tables)
diff --git a/docs/physics/plasma/plasma_plots/nebular_ionization_balance.py b/docs/physics/plasma/plasma_plots/nebular_ionization_balance.py
index 428f9125e24..7da63d7f580 100644
--- a/docs/physics/plasma/plasma_plots/nebular_ionization_balance.py
+++ b/docs/physics/plasma/plasma_plots/nebular_ionization_balance.py
@@ -3,8 +3,7 @@
from matplotlib import colors
import tardis.util.base
-from tardis import util
-from tardis.io import atomic
+from tardis.io.atom_data import base
from matplotlib import pyplot as plt
import numpy as np
@@ -22,7 +21,7 @@
atom_fname = os.path.expanduser('~/.tardis/si_kurucz.h5')
# reading in the HDF5 File
-atom_data = atomic.AtomData.from_hdf(atom_fname)
+atom_data = base.AtomData.from_hdf(atom_fname)
#The atom_data needs to be prepared to create indices. The Class needs to know which atomic numbers are needed for the
#calculation and what line interaction is needed (for "downbranch" and "macroatom" the code creates special tables)
diff --git a/docs/quickstart.rst b/docs/quickstart.rst
index b0aab0bcdf0..fb9e0177b0b 100644
--- a/docs/quickstart.rst
+++ b/docs/quickstart.rst
@@ -11,6 +11,19 @@ To run TARDIS requires two files. The atomic database (for more info refer to
(``atom_data`` subfolder). We recommended to use the
``kurucz_cd23_chianti_H_He.h5`` dataset.
+
+Running TARDIS interactively in a Jupyter notebook (recommended)
+================================================================
+
+To get more information from each run of TARDIS one can run it interactively and
+have full access to the model properties (as described in :ref:`physical_quantities`)
+
+.. toctree::
+ :maxdepth: 1
+
+ examples/run_simple_example.ipynb
+
+
Running TARDIS in the commandline
=================================
@@ -42,21 +55,6 @@ Then plot the output_spectrum.dat with your favourite plotting program. Here's a
More atomic datasets can be downloaded from :ref:`atomic-data-download`.
-Running TARDIS interactively (recommended)
-==========================================
-
-To get more information from each run of TARDIS one can run it interactively and
-have full access to the model properties
-
-.. code-block:: python
-
- >>> from tardis import run_tardis
- >>> model = run_tardis('myconfig.yml')
-
-This model can then be used for inspecting the run as described
-:ref:`physical_quantities`
-
-
Graphical User Interface
========================
diff --git a/tardis/base.py b/tardis/base.py
index 8e771362926..66856c0fdd2 100644
--- a/tardis/base.py
+++ b/tardis/base.py
@@ -20,7 +20,7 @@ def run_tardis(config, atom_data=None, simulation_callbacks=[]):
[default=None]
"""
from tardis.io.config_reader import Configuration
- from tardis.io.atomic import AtomData
+ from tardis.io.atom_data.base import AtomData
from tardis.simulation import Simulation
if atom_data is not None:
diff --git a/tardis/data/atom_data.h5 b/tardis/data/atom_data.h5
deleted file mode 100644
index 7acf42d8b46..00000000000
Binary files a/tardis/data/atom_data.h5 and /dev/null differ
diff --git a/tardis/data/atomic_data_repo.yml b/tardis/data/atomic_data_repo.yml
new file mode 100644
index 00000000000..3a824a4c47f
--- /dev/null
+++ b/tardis/data/atomic_data_repo.yml
@@ -0,0 +1,5 @@
+default: kurucz_cd23_chianti_H_He
+
+kurucz_cd23_chianti_H_He:
+ url: https://media.githubusercontent.com/media/tardis-sn/tardis-refdata/master/atom_data/kurucz_cd23_chianti_H_He.h5
+ uuid: NA
diff --git a/tardis/data/default_tardis_internal_config.yml b/tardis/data/default_tardis_internal_config.yml
new file mode 100644
index 00000000000..0bcdbc53b37
--- /dev/null
+++ b/tardis/data/default_tardis_internal_config.yml
@@ -0,0 +1 @@
+data_dir:
\ No newline at end of file
diff --git a/tardis/io/__init__.py b/tardis/io/__init__.py
index 583d361a6b4..8034d6d0cc0 100644
--- a/tardis/io/__init__.py
+++ b/tardis/io/__init__.py
@@ -1,4 +1,4 @@
#readin model_data
from tardis.io.model_reader import read_simple_ascii_density, read_simple_ascii_abundances, read_density_file
-from tardis.io.util import parse_abundance_dict_to_dataframe
+from tardis.io.config_internal import get_internal_configuration, get_data_dir
diff --git a/tardis/io/atom_data/__init__.py b/tardis/io/atom_data/__init__.py
new file mode 100644
index 00000000000..5c3a8c24595
--- /dev/null
+++ b/tardis/io/atom_data/__init__.py
@@ -0,0 +1 @@
+from tardis.io.atom_data.base import AtomData
\ No newline at end of file
diff --git a/tardis/io/atom_data/atom_web_download.py b/tardis/io/atom_data/atom_web_download.py
new file mode 100644
index 00000000000..fc6d6d3702a
--- /dev/null
+++ b/tardis/io/atom_data/atom_web_download.py
@@ -0,0 +1,38 @@
+import os
+import logging
+
+from tardis.io.util import get_internal_data_path, download_from_url
+from tardis.io.config_internal import get_data_dir
+import yaml
+
+logger = logging.getLogger(__name__)
+
+def get_atomic_repo_config():
+ atomic_repo_fname = get_internal_data_path('atomic_data_repo.yml')
+ return yaml.load(open(atomic_repo_fname))
+
+
+def download_atom_data(atomic_data_name=None):
+ """
+ Download the atomic data from the repository
+ Parameters
+ ----------
+ atomic_data_name: str
+ if None
+
+ Returns
+ -------
+ : None
+
+ """
+ atomic_repo = get_atomic_repo_config()
+
+ if atomic_data_name is None:
+ atomic_data_name = atomic_repo['default']
+
+ if atomic_data_name not in atomic_repo:
+ raise ValueError('Atomic Data name {0} not known'.format(atomic_data_name))
+ dst_dir = os.path.join(get_data_dir(), '{0}.h5'.format(atomic_data_name))
+ src_url = atomic_repo[atomic_data_name]['url']
+ logger.info('Downloading atomic data from {0} to {1}'.format(src_url, dst_dir))
+ download_from_url(src_url, dst_dir)
\ No newline at end of file
diff --git a/tardis/io/atomic.py b/tardis/io/atom_data/base.py
similarity index 98%
rename from tardis/io/atomic.py
rename to tardis/io/atom_data/base.py
index 3ab59be373a..cf6c10a3394 100644
--- a/tardis/io/atomic.py
+++ b/tardis/io/atom_data/base.py
@@ -1,6 +1,6 @@
# atomic model
-import os
+
import logging
import numpy as np
import pandas as pd
@@ -10,7 +10,7 @@
from astropy import units as u
from tardis import constants as const
from astropy.units import Quantity
-
+from tardis.io.atom_data.util import resolve_atom_data_fname
class AtomDataNotPreparedError(Exception):
pass
@@ -127,21 +127,23 @@ class AtomData(object):
("collision_data", "collision_data_temperatures")]
@classmethod
- def from_hdf(cls, fname):
+ def from_hdf(cls, fname=None):
"""
- Function to read all the atom data from the special Carsus HDFStore file.
+ Function to read the atom data from a TARDIS atom HDF Store
Parameters
----------
fname: str, optional
- Path to the HDFStore file. Please contact the authors to get the up-to-date file.
+ Path to the HDFStore file or name of known atom data file
(default: None)
"""
dataframes = dict()
nonavailable = list()
+ fname = resolve_atom_data_fname(fname)
+
with pd.HDFStore(fname, 'r') as store:
for name in cls.hdf_names:
try:
diff --git a/tardis/io/atom_data/util.py b/tardis/io/atom_data/util.py
new file mode 100644
index 00000000000..d5f1fde1939
--- /dev/null
+++ b/tardis/io/atom_data/util.py
@@ -0,0 +1,42 @@
+import os
+import logging
+
+from tardis.io.config_internal import get_data_dir
+from tardis.io.atom_data.atom_web_download import get_atomic_repo_config, download_atom_data
+
+logger = logging.getLogger(__name__)
+
+def resolve_atom_data_fname(fname):
+ """
+ Check where if atom data HDF file is available on disk, can be downloaded or does not exist
+
+ Parameters
+ ----------
+ fname: str
+ name or path of atom data HDF file
+
+ Returns
+ -------
+ : str
+ resolved fpath
+ """
+
+ if os.path.exists(fname):
+ return fname
+
+ fpath = os.path.join(os.path.join(get_data_dir(), fname))
+ if os.path.exists(fpath):
+ logger.info('Atom Data {0} not found in local path. Exists in TARDIS Data repo {1}'.format(fname, fpath))
+ return fpath
+
+ atom_data_name = fname.replace('.h5', '')
+ atom_repo_config = get_atomic_repo_config()
+ if atom_data_name in atom_repo_config:
+ raise IOError('Atom Data {0} not found in path or in TARDIS data repo - it is available as download:\n'
+ 'from tardis.io.atom_data import download_atom_data\n'
+ 'download_atom_data(\'{1}\')'.format(fname, atom_data_name))
+
+ raise IOError('Atom Data {0} is not found in current path or in TARDIS data repo. {1} is also not a standard known'
+ 'TARDIS atom dataset.'.format(fname, atom_data_name))
+
+
diff --git a/tardis/io/config_internal.py b/tardis/io/config_internal.py
new file mode 100644
index 00000000000..6a8bc381677
--- /dev/null
+++ b/tardis/io/config_internal.py
@@ -0,0 +1,43 @@
+from tardis import __path__ as TARDIS_PATH
+import os, logging, shutil
+import yaml
+
+from astropy.config import get_config_dir
+
+TARDIS_PATH = TARDIS_PATH[0]
+DEFAULT_CONFIG_PATH = os.path.join(TARDIS_PATH, 'data', 'default_tardis_internal_config.yml')
+DEFAULT_DATA_DIR = os.path.join(os.path.expanduser('~'), 'Downloads', 'tardis-data')
+logger = logging.getLogger(__name__)
+
+def get_internal_configuration():
+
+ config_fpath = os.path.join(get_config_dir(), 'tardis_internal_config.yml')
+ if not os.path.exists(config_fpath):
+ logger.warning("Configuration File {0} does not exist - creating new one from default".format(config_fpath))
+ shutil.copy(DEFAULT_CONFIG_PATH, config_fpath)
+ return yaml.load(open(config_fpath))
+
+
+
+def get_data_dir():
+
+ config = get_internal_configuration()
+ data_dir = config.get('data_dir', None)
+ if data_dir is None:
+ config_fpath = os.path.join(get_config_dir(), 'tardis_internal_config.yml')
+ logging.critical('\n{line_stars}\n\nTARDIS will download different kinds of data (e.g. atomic) to its data directory {default_data_dir}\n\n'
+ 'TARDIS DATA DIRECTORY not specified in {config_file}:\n\n'
+ 'ASSUMING DEFAULT DATA DIRECTORY {default_data_dir}\n '
+ 'YOU CAN CHANGE THIS AT ANY TIME IN {config_file} \n\n'
+ '{line_stars} \n\n'.format(line_stars='*'*80, config_file=config_fpath,
+ default_data_dir=DEFAULT_DATA_DIR))
+ if not os.path.exists(DEFAULT_DATA_DIR):
+ os.makedirs(DEFAULT_DATA_DIR)
+ config['data_dir'] = DEFAULT_DATA_DIR
+ yaml.dump(config, open(config_fpath, 'w'), default_flow_style=False)
+ data_dir = DEFAULT_DATA_DIR
+
+ if not os.path.exists(data_dir):
+ raise IOError('Data directory specified in {0} does not exist'.format(data_dir))
+
+ return data_dir
diff --git a/tardis/io/util.py b/tardis/io/util.py
index 80df111a3ae..ce9821c4b1e 100644
--- a/tardis/io/util.py
+++ b/tardis/io/util.py
@@ -2,19 +2,38 @@
import os
import re
+import logging
+
import pandas as pd
import numpy as np
import collections
from collections import OrderedDict
+
+import requests
import yaml
+from tqdm.autonotebook import tqdm
+
from tardis import constants
from astropy import units as u
-from tardis.util.base import element_symbol2atomic_number
-import logging
+from tardis import __path__ as TARDIS_PATH
+
logger = logging.getLogger(__name__)
+
+def get_internal_data_path(fname):
+ """
+ Get internal data path of TARDIS
+
+ Returns
+ -------
+ data_path: str
+ internal data path of TARDIS
+
+ """
+ return os.path.join(TARDIS_PATH[0], 'data', fname)
+
def quantity_from_str(text):
"""
Convert a string to `astropy.units.Quantity`
@@ -114,21 +133,6 @@ def yaml_load_config_file(filename):
return yaml_load_file(filename, YAMLLoader)
-def parse_abundance_dict_to_dataframe(abundance_dict):
- atomic_number_dict = dict([(element_symbol2atomic_number(symbol), abundance_dict[symbol])
- for symbol in abundance_dict])
- atomic_numbers = sorted(atomic_number_dict.keys())
-
- abundances = pd.Series([atomic_number_dict[z] for z in atomic_numbers], index=atomic_numbers)
-
- abundance_norm = abundances.sum()
- if abs(abundance_norm - 1) > 1e-12:
- logger.warning('Given abundances don\'t add up to 1 (value = %g) - normalizing', abundance_norm)
- abundances /= abundance_norm
-
- return abundances
-
-
def traverse_configs(base, other, func, *args):
"""
Recursively traverse a base dict or list along with another one
@@ -347,5 +351,29 @@ def to_hdf(self, file_path, path='', name=None, collection=None):
super(PlasmaWriterMixin, self).to_hdf(file_path, path, name)
+def download_from_url(url, dst):
+ """
+ kindly used from https://gist.github.com/wy193777/0e2a4932e81afc6aa4c8f7a2984f34e2
+ @param: url to download file
+ @param: dst place to put the file
+ """
-
+ file_size = int(requests.head(url).headers["Content-Length"])
+ if os.path.exists(dst):
+ first_byte = os.path.getsize(dst)
+ else:
+ first_byte = 0
+ if first_byte >= file_size:
+ return file_size
+ header = {"Range": "bytes=%s-%s" % (first_byte, file_size)}
+ pbar = tqdm(
+ total=file_size, initial=first_byte,
+ unit='B', unit_scale=True, desc=url.split('/')[-1])
+ req = requests.get(url, headers=header, stream=True)
+ with(open(dst, 'ab')) as f:
+ for chunk in req.iter_content(chunk_size=1024):
+ if chunk:
+ f.write(chunk)
+ pbar.update(1024)
+ pbar.close()
+ return file_size
\ No newline at end of file
diff --git a/tardis/plasma/standard_plasmas.py b/tardis/plasma/standard_plasmas.py
index 7744bf5934e..5ac131f7045 100644
--- a/tardis/plasma/standard_plasmas.py
+++ b/tardis/plasma/standard_plasmas.py
@@ -2,9 +2,8 @@
import logging
import pandas as pd
-import numpy as np
-from tardis.io import atomic
+from tardis.io.atom_data import AtomData
from tardis.util.base import species_string_to_tuple
from tardis.plasma import BasePlasma
from tardis.plasma.properties.property_collections import (basic_inputs,
@@ -77,7 +76,7 @@ def assemble_plasma(config, model, atom_data=None):
logger.info('Reading Atomic Data from %s', atom_data_fname)
try:
- atom_data = atomic.AtomData.from_hdf(atom_data_fname)
+ atom_data = AtomData.from_hdf(atom_data_fname)
except TypeError as e:
print(e, 'Error might be from the use of an old-format of the atomic database, \n'
'please see https://github.com/tardis-sn/tardis-refdata/tree/master/atom_data'
diff --git a/tardis/tests/fixtures/atom_data.py b/tardis/tests/fixtures/atom_data.py
index 9cee0b62c83..5851300f504 100644
--- a/tardis/tests/fixtures/atom_data.py
+++ b/tardis/tests/fixtures/atom_data.py
@@ -3,7 +3,7 @@
import pytest
-from tardis.io.atomic import AtomData
+from tardis.io.atom_data.base import AtomData
DEFAULT_ATOM_DATA_UUID = '864f1753714343c41f99cb065710cace'
diff --git a/tardis/tests/integration_tests/test_integration.py b/tardis/tests/integration_tests/test_integration.py
index 3939d208517..ebab62f649f 100644
--- a/tardis/tests/integration_tests/test_integration.py
+++ b/tardis/tests/integration_tests/test_integration.py
@@ -6,9 +6,8 @@
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from numpy.testing import assert_allclose
-from astropy.tests.helper import assert_quantity_allclose
-from tardis.io.atomic import AtomData
+from tardis.io.atom_data.base import AtomData
from tardis.simulation import Simulation
from tardis.io.config_reader import Configuration
diff --git a/tardis/util/base.py b/tardis/util/base.py
index 45304340877..5a17dc88313 100644
--- a/tardis/util/base.py
+++ b/tardis/util/base.py
@@ -12,6 +12,7 @@
from pyne import nucname
import tardis
+from tardis.io.util import get_internal_data_path
k_B_cgs = constants.k_B.cgs.value
c_cgs = constants.c.cgs.value
@@ -55,21 +56,14 @@ def __str__(self):
tardis_dir = os.path.realpath(tardis.__path__[0])
-def get_data_path(fname):
- return os.path.join(tardis_dir, 'data', fname)
-
-def get_tests_data_path(fname):
- return os.path.join(tardis_dir, 'tests', 'data', fname)
-
-
-ATOMIC_SYMBOLS_DATA = pd.read_csv(get_data_path('atomic_symbols.dat'), delim_whitespace=True,
+ATOMIC_SYMBOLS_DATA = pd.read_csv(get_internal_data_path('atomic_symbols.dat'), delim_whitespace=True,
names=['atomic_number', 'symbol']).set_index('atomic_number').squeeze()
ATOMIC_NUMBER2SYMBOL = OrderedDict(ATOMIC_SYMBOLS_DATA.to_dict())
SYMBOL2ATOMIC_NUMBER = OrderedDict((y, x) for x, y in ATOMIC_NUMBER2SYMBOL.items())
-synpp_default_yaml_fname = get_data_path('synpp_default.yaml')
+synpp_default_yaml_fname = get_internal_data_path('synpp_default.yaml')
NUMERAL_MAP = tuple(zip(
diff --git a/tardis_env3.yml b/tardis_env3.yml
index 824e263c54f..27d94f7afa8 100644
--- a/tardis_env3.yml
+++ b/tardis_env3.yml
@@ -24,6 +24,8 @@ dependencies:
- pyne=0.5
- pytables
- h5py
+- requests
+- tqdm
# Analysis
- jupyter