diff --git a/demos/7_filepad/.gitignore b/demos/7_filepad/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..58461f25420fa09ee4a525651a0c54efa1b11af7
--- /dev/null
+++ b/demos/7_filepad/.gitignore
@@ -0,0 +1 @@
+.ipynb_checkpoints
\ No newline at end of file
diff --git a/demos/7_filepad/A_cluster.yaml b/demos/7_filepad/A_cluster.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1be85b05e8987b25e97f61639d9740bdde293687
--- /dev/null
+++ b/demos/7_filepad/A_cluster.yaml
@@ -0,0 +1,24 @@
+fw_id: -20
+name: "Minimize cluster"
+spec:
+ _category: "bwcloud_noqueue"
+ _files_out:
+ data_file: final.lammps
+ _tasks:
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/potential
+ new_file_names:
+ - Au-Grochola-JCP05.eam.alloy
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/small_cluster
+ new_file_names:
+ - initial.lammps
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/minimize_cluster
+ new_file_names:
+ - lammps.in
+ - _fw_name: ScriptTask
+ script: source /etc/profile; module load LAMMPS; mpirun -n 2 lmp -in lammps.in
diff --git a/demos/7_filepad/A_substrate.yaml b/demos/7_filepad/A_substrate.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a1b7f3de299c144dd1e63494d845ab9f7d0ebf65
--- /dev/null
+++ b/demos/7_filepad/A_substrate.yaml
@@ -0,0 +1,19 @@
+fw_id: -10
+name: "Minimize substrate"
+spec:
+ _category: "bwcloud_noqueue"
+ _files_out:
+ data_file: final.lammps
+ _tasks:
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/potential
+ new_file_names:
+ - Au-Grochola-JCP05.eam.alloy
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/minimize_substrate
+ new_file_names:
+ - lammps.in
+ - _fw_name: ScriptTask
+ script: source /etc/profile; module load LAMMPS; mpirun -n 2 lmp -in lammps.in
diff --git a/demos/7_filepad/B_cluster.yaml b/demos/7_filepad/B_cluster.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8ad04bce25e8b7f0f40839d648c35b1a76da4882
--- /dev/null
+++ b/demos/7_filepad/B_cluster.yaml
@@ -0,0 +1,21 @@
+fw_id: -40
+name: "Equilibrate cluster"
+spec:
+ _category: "bwcloud_noqueue"
+ _files_in:
+ data_file: initial.lammps
+ _files_out:
+ data_file: final.lammps
+ _tasks:
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/potential
+ new_file_names:
+ - Au-Grochola-JCP05.eam.alloy
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/equilibrate_cluster
+ new_file_names:
+ - lammps.in
+ - _fw_name: ScriptTask
+ script: source /etc/profile; module load LAMMPS; mpirun -n 2 lmp -in lammps.in
diff --git a/demos/7_filepad/B_substrate.yaml b/demos/7_filepad/B_substrate.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..569608638b01608488c2b9dc8f077ea37a85ce84
--- /dev/null
+++ b/demos/7_filepad/B_substrate.yaml
@@ -0,0 +1,21 @@
+fw_id: -30
+name: "Equilibrate substrate"
+spec:
+ _category: "bwcloud_noqueue"
+ _files_in:
+ data_file: initial.lammps
+ _files_out:
+ data_file_substrate: final.lammps
+ _tasks:
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/potential
+ new_file_names:
+ - Au-Grochola-JCP05.eam.alloy
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/equilibrate_substrate
+ new_file_names:
+ - lammps.in
+ - _fw_name: ScriptTask
+ script: source /etc/profile; module load LAMMPS; mpirun -n 2 lmp -in lammps.in
diff --git a/demos/7_filepad/C_cluster.yaml b/demos/7_filepad/C_cluster.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6366af468782da34fcebb2732d3788beaebed1f4
--- /dev/null
+++ b/demos/7_filepad/C_cluster.yaml
@@ -0,0 +1,21 @@
+fw_id: -50
+name: "Melt cluster"
+spec:
+ _category: "bwcloud_noqueue"
+ _files_in:
+ data_file: initial.lammps
+ _files_out:
+ data_file_cluster: final.lammps
+ _tasks:
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/potential
+ new_file_names:
+ - Au-Grochola-JCP05.eam.alloy
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/equilibrate_cluster
+ new_file_names:
+ - lammps.in
+ - _fw_name: ScriptTask
+ script: source /etc/profile; module load LAMMPS; mpirun -n 2 lmp -in lammps.in
diff --git a/demos/7_filepad/D_deposit.yaml b/demos/7_filepad/D_deposit.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a34625714ea8a45db9a6884972f733a3c28dc06b
--- /dev/null
+++ b/demos/7_filepad/D_deposit.yaml
@@ -0,0 +1,50 @@
+fw_id: -60
+name: "Deposit"
+spec:
+ _category: "bwcloud_noqueue"
+ _files_in:
+ data_file_cluster: cluster.lammps
+ data_file_substrate: substrate.lammps
+ _tasks:
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/potential
+ new_file_names:
+ - Au-Grochola-JCP05.eam.alloy
+ - _fw_name: GetFilesTask
+ identifiers:
+ - fireworks-hands-on-2019-12-09/deposit
+ new_file_names:
+ - lammps.in
+ - _fw_name: ScriptTask
+ script: source /etc/profile; module load LAMMPS; mpirun -n 2 lmp -in lammps.in
+ - _fw_name: AddFilesTask
+ paths:
+ - traj.dump
+ compress: True
+ metadata:
+ project: fireworks-hands-on-2019-12-09
+ type: trajectory
+ velocity: -25
+ steps: 4000
+ timestep: 0.001
+ - _fw_name: AddFilesTask
+ paths:
+ - final.lammps
+ compress: True
+ metadata:
+ project: fireworks-hands-on-2019-12-09
+ type: final configuration
+ velocity: -25
+ steps: 4000
+ timestep: 0.001
+ - _fw_name: AddFilesTask
+ paths:
+ - log.lammps
+ compress: True
+ metadata:
+ project: fireworks-hands-on-2019-12-09
+ type: log
+ velocity: -25
+ steps: 4000
+ timestep: 0.001
diff --git a/demos/7_filepad/PythonGoldCluster.ipynb b/demos/7_filepad/PythonGoldCluster.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f3bc4d7df8eddc59efafab044252a4520bb22941
--- /dev/null
+++ b/demos/7_filepad/PythonGoldCluster.ipynb
@@ -0,0 +1,2232 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Gold droplet hits substrate"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, we will shoot a gold cluster onto a gold substrate and systematically study the effect of cluster size and velocity. A workflow involves\n",
+ "\n",
+ "* Energy minimization of a substrate\n",
+ "* Equilibration of substrate (here in NVE ensemble)\n",
+ "* Energy minimization of a cluster\n",
+ "* Equilibration of cluster (here in NVE ensemble)\n",
+ "* Melting cluster at high temperature\n",
+ "* Shooting cluster onto substrate\n",
+ "\n",
+ "\n",
+ "\n",
+ "The subfolder `infiles` contains LAMMPS input scripts for each of these steps."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Initialization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:37:43.709106Z",
+ "start_time": "2019-12-08T16:37:43.374907Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# to display plots directly inline within this notebook\n",
+ "%matplotlib inline "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:37:44.100400Z",
+ "start_time": "2019-12-08T16:37:44.092058Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# we might need these for\n",
+ "# * enforced memory clean-up (gc)\n",
+ "# * bash-like globbing of files on the local file system (glob)\n",
+ "# * basic stream-from-string input (io)\n",
+ "# * extracting interseting information from log files by regular expressions (re)\n",
+ "# * os, sys ...\n",
+ "import gc, glob, io, os, re, sys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:37:46.812572Z",
+ "start_time": "2019-12-08T16:37:46.416071Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import ase, ase.io, ase.visualize # read and visualize LAMMPS trajectories\n",
+ "import matplotlib.pyplot as plt \n",
+ "import numpy as np \n",
+ "import pandas as pd\n",
+ "import scipy as scp # here for handling rotations as matrices and quaternions\n",
+ "import scipy.spatial \n",
+ "from pprint import pprint"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:37:47.136691Z",
+ "start_time": "2019-12-08T16:37:46.976330Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# FireWorks functionality \n",
+ "from fireworks import Firework, LaunchPad, ScriptTask, Workflow\n",
+ "\n",
+ "# new here: tasks using FireWorks FilePad\n",
+ "from fireworks.user_objects.firetasks.filepad_tasks import AddFilesTask, GetFilesTask\n",
+ "\n",
+ "# direct FilePad access, similar to the familiar LaunchPad:\n",
+ "from fireworks.utilities.filepad import FilePad "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:37:49.310291Z",
+ "start_time": "2019-12-08T16:37:49.302562Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "prefix = os.getcwd()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:37:50.964787Z",
+ "start_time": "2019-12-08T16:37:50.962395Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# to start with\n",
+ "infile_prefix = os.path.join(prefix,'infiles')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:37:51.489828Z",
+ "start_time": "2019-12-08T16:37:51.471492Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# the FireWorks LaunchPad\n",
+ "lp = LaunchPad.auto_load() #Define the server and database"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:37:52.684406Z",
+ "start_time": "2019-12-08T16:37:52.657957Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# FilePad behaves analogous to LaunchPad\n",
+ "fp = FilePad.auto_load()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:37:53.426855Z",
+ "start_time": "2019-12-08T16:37:53.419188Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# use this little snippet to identify large objects in memory\n",
+ "# delete them and, if necessary, call gc.collect() manually to free up memory\n",
+ "def memuse():\n",
+ " \"\"\"Quick overview on memory usage of objects in Jupyter notebook\"\"\"\n",
+ " # https://stackoverflow.com/questions/40993626/list-memory-usage-in-ipython-and-jupyter\n",
+ " # These are the usual ipython objects, including this one you are creating\n",
+ " ipython_vars = ['In', 'Out', 'exit', 'quit', 'get_ipython', 'ipython_vars']\n",
+ "\n",
+ " # Get a sorted list of the objects and their sizes\n",
+ " return sorted([(x, sys.getsizeof(globals().get(x))) for x in dir(sys.modules['__main__']) if not x.startswith('_') and x not in sys.modules and x not in ipython_vars], key=lambda x: x[1], reverse=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Static workflow"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Within this demo, we arrange our input and data files in a data base\n",
+ "and attach metadata for easy querying. FireWorks readily offers the FilePad \n",
+ "(based on an underlying MongoDB \"GridFS\") for such purposes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:37:55.647930Z",
+ "start_time": "2019-12-08T16:37:55.643345Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# some unique identifier for our study\n",
+ "project_id = 'fireworks-hands-on-2019-12-09'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:38:18.886682Z",
+ "start_time": "2019-12-08T16:38:18.876863Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# input and data files for the current parametric study\n",
+ "files = {\n",
+ " # one file describing the interatomic potential\n",
+ " 'potential': 'Au-Grochola-JCP05.eam.alloy',\n",
+ " # two files for different cluster sizes\n",
+ " 'large_cluster': 'cluster_3871.lammps',\n",
+ " 'small_cluster': 'cluster_923.lammps',\n",
+ " # several LAMMPS input files for the above steps\n",
+ " 'minimize_substrate': 'A_minimize_substrate.in',\n",
+ " 'equilibrate_substrate': 'B_equilibrate_substrate.in',\n",
+ " 'minimize_cluster': 'A_minimize_cluster.in',\n",
+ " 'equilibrate_cluster': 'B_equilibrate_cluster.in',\n",
+ " 'melt_cluster': 'C_melt_cluster.in',\n",
+ " 'deposit': 'D_deposit.in'\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:40:55.210701Z",
+ "start_time": "2019-12-08T16:40:55.204574Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# metadata common to all these files \n",
+ "metadata = {\n",
+ " 'project': project_id,\n",
+ " 'type': 'input'\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:40:59.183608Z",
+ "start_time": "2019-12-08T16:40:59.181212Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "fp_files = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:41:00.146179Z",
+ "start_time": "2019-12-08T16:41:00.006039Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# insert these input files into data base\n",
+ "for name, file in files.items():\n",
+ " file_path = os.path.join(infile_prefix,file)\n",
+ " identifier = '/'.join((project_id,name)) # identifier is like a path on a file system\n",
+ " metadata[\"name\"] = name\n",
+ " #fp.delete_file(identifier=identifier)\n",
+ " fp_files.append( fp.add_file(file_path,identifier=identifier,metadata = metadata) )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:41:19.091489Z",
+ "start_time": "2019-12-08T16:41:19.088857Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# queries to the data base are simple dictionaries\n",
+ "query = {\n",
+ " 'metadata.project': project_id,\n",
+ " 'metadata.type': 'input'\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:41:22.263674Z",
+ "start_time": "2019-12-08T16:41:22.250311Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "9"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# use underlying MongoDB functionality to check total number of documents matching query\n",
+ "fp.filepad.count_documents(query)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:41:24.146443Z",
+ "start_time": "2019-12-08T16:41:24.143487Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "fireworks-hands-on-2019-12-09/deposit\n"
+ ]
+ }
+ ],
+ "source": [
+ "# we fixed simple identifiers of this form\n",
+ "print(identifier)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:41:29.758827Z",
+ "start_time": "2019-12-08T16:41:29.751985Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[('5ded279c11940f1ec729018a', 'fireworks-hands-on-2019-12-09/potential'),\n",
+ " ('5ded279c11940f1ec729018d', 'fireworks-hands-on-2019-12-09/large_cluster'),\n",
+ " ('5ded279c11940f1ec7290190', 'fireworks-hands-on-2019-12-09/small_cluster'),\n",
+ " ('5ded279c11940f1ec7290193',\n",
+ " 'fireworks-hands-on-2019-12-09/minimize_substrate'),\n",
+ " ('5ded279c11940f1ec7290196',\n",
+ " 'fireworks-hands-on-2019-12-09/equilibrate_substrate'),\n",
+ " ('5ded279c11940f1ec7290199', 'fireworks-hands-on-2019-12-09/minimize_cluster'),\n",
+ " ('5ded279c11940f1ec729019c',\n",
+ " 'fireworks-hands-on-2019-12-09/equilibrate_cluster'),\n",
+ " ('5ded279c11940f1ec729019f', 'fireworks-hands-on-2019-12-09/melt_cluster'),\n",
+ " ('5ded279c11940f1ec72901a2', 'fireworks-hands-on-2019-12-09/deposit')]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# on a lower level, each object has a unique \"GridFS id\":\n",
+ "pprint(fp_files) # underlying GridFS id and readable identifiers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:41:35.813085Z",
+ "start_time": "2019-12-08T16:41:35.809608Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# if necessary, remove all files matching query\n",
+ "# fp.delete_file_by_query(query)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Build workflow"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T15:20:59.981060Z",
+ "start_time": "2019-12-08T15:20:59.970012Z"
+ }
+ },
+ "source": [
+ "We have each FireWork prepared in a static yaml file within this folder, \n",
+ "such as \n",
+ "\n",
+ "```yaml\n",
+ "fw_id: -10\n",
+ "name: \"Minimize substrate\"\n",
+ "spec:\n",
+ " _category: \"bwcloud_noqueue\"\n",
+ " _files_out: \n",
+ " data_file: final.lammps\n",
+ " _tasks:\n",
+ " - _fw_name: GetFilesTask\n",
+ " identifiers:\n",
+ " - fireworks-hands-on-2019-12-09/potential\n",
+ " new_file_names:\n",
+ " - Au-Grochola-JCP05.eam.alloy\n",
+ " - _fw_name: GetFilesTask\n",
+ " identifiers:\n",
+ " - fireworks-hands-on-2019-12-09/minimize_substrate\n",
+ " new_file_names:\n",
+ " - lammps.in\n",
+ " - _fw_name: ScriptTask\n",
+ " script: source /etc/profile; module load LAMMPS; mpirun -n 2 lmp -in lammps.in\n",
+ "```\n",
+ "\n",
+ "Notice the two `GetFilesTask`s. They query files by their identifier and put them in the task's working directory.\n",
+ "This is an alternative to `FileTransferTask`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:41:39.531011Z",
+ "start_time": "2019-12-08T16:41:39.407144Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# We have each FireWork prepared in a static yaml file within this folder.\n",
+ "# Now, we read these and fix the dependencies here:\n",
+ "\n",
+ "fw_A_substrate = Firework.from_file('A_substrate.yaml',)\n",
+ "fw_B_substrate = Firework.from_file('B_substrate.yaml')\n",
+ "\n",
+ "fw_A_cluster = Firework.from_file('A_cluster.yaml')\n",
+ "fw_B_cluster = Firework.from_file('B_cluster.yaml')\n",
+ "fw_C_cluster = Firework.from_file('C_cluster.yaml')\n",
+ "\n",
+ "fw_D_deposit = Firework.from_file('D_deposit.yaml')\n",
+ "\n",
+ "wf = Workflow(\n",
+ " [ fw_A_substrate, fw_B_substrate, \n",
+ " fw_A_cluster, fw_B_cluster, fw_C_cluster,\n",
+ " fw_D_deposit ],\n",
+ " \n",
+ " { fw_A_substrate: fw_B_substrate, \n",
+ " fw_B_substrate: fw_D_deposit,\n",
+ " \n",
+ " fw_A_cluster: fw_B_cluster, \n",
+ " fw_B_cluster: fw_C_cluster,\n",
+ " fw_C_cluster: fw_D_deposit },\n",
+ " \n",
+ " name = \"Gold cluster impact\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:41:41.638408Z",
+ "start_time": "2019-12-08T16:41:41.604046Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# we can write a static yaml file and have a look at, then submit it\n",
+ "# on the command line with \"lpad add wf.yaml\"...\n",
+ "wf.to_file('wf.yaml')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:41:43.076425Z",
+ "start_time": "2019-12-08T16:41:43.052356Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2019-12-08 17:41:43,074 INFO Added a workflow. id_map: {-60: 23568, -50: 23569, -40: 23570, -30: 23571, -20: 23572, -10: 23573}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# or directly submit from here\n",
+ "fw_ids = lp.add_wf(wf)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Query results"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Look at `D_deposit.yaml`. The final FireWork in this workflow uses\n",
+ "`AddFilesTask`s like this\n",
+ "```yaml\n",
+ " - _fw_name: AddFilesTask\n",
+ " paths: \n",
+ " - traj.dump\n",
+ " compress: True\n",
+ " metadata: \n",
+ " project: fireworks-hands-on-2019-12-09\n",
+ " type: trajectory\n",
+ " velocity: -25\n",
+ " steps: 4000\n",
+ " timestep: 0.001\n",
+ "```\n",
+ "to store files in the FilePad and attach metadata. We can use such metadata to query the output files after the workflow has run."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:45:59.830003Z",
+ "start_time": "2019-12-08T16:45:59.823755Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "query = { \n",
+ " \"metadata.project\": project_id,\n",
+ " \"metadata.velocity\": -25,\n",
+ " \"metadata.steps\" : 4000\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:46:03.563931Z",
+ "start_time": "2019-12-08T16:46:03.546477Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# use underlying MongoDB functionality \n",
+ "fp.filepad.count_documents(query)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:46:41.913046Z",
+ "start_time": "2019-12-08T16:46:41.905854Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "query = { \n",
+ " \"metadata.project\": project_id,\n",
+ " \"metadata.velocity\": -25,\n",
+ " \"metadata.steps\" : 4000,\n",
+ " \"metadata.type\": 'final configuration'\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:46:43.589144Z",
+ "start_time": "2019-12-08T16:46:43.544730Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "config_file, config_metadata = fp.get_file_by_query(query)[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:46:44.771878Z",
+ "start_time": "2019-12-08T16:46:44.742629Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'_id': ObjectId('5ded286311940f336850cb3f'),\n",
+ " 'compressed': True,\n",
+ " 'gfs_id': '5ded286311940f336850cb3b',\n",
+ " 'identifier': '/mnt/dat/fw/launcher_2019-12-08-16-42-47-414867/final.lammps',\n",
+ " 'metadata': {'project': 'fireworks-hands-on-2019-12-09',\n",
+ " 'steps': 4000,\n",
+ " 'timestep': 0.001,\n",
+ " 'type': 'final configuration',\n",
+ " 'velocity': -25},\n",
+ " 'original_file_name': 'final.lammps',\n",
+ " 'original_file_path': '/mnt/dat/fw/launcher_2019-12-08-16-42-47-414867/final.lammps'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "pprint(config_metadata)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:46:48.853820Z",
+ "start_time": "2019-12-08T16:46:48.544264Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# use ASE to read and visualize this final configuration\n",
+ "final_config = ase.io.read(\n",
+ " io.StringIO(config_file.decode()),\n",
+ " format='lammps-data',\n",
+ " style='atomic',\n",
+ " Z_of_type={1 : ase.data.atomic_numbers['Au']})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:47:27.294949Z",
+ "start_time": "2019-12-08T16:47:27.281636Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# rotate around y axis\n",
+ "roty = np.array(\n",
+ " [[0,0,1],\n",
+ " [0,1,0],\n",
+ " [-1,0,0]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:47:27.866031Z",
+ "start_time": "2019-12-08T16:47:27.860808Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# convert rotation matrix to quaternion\n",
+ "rot = scp.spatial.transform.Rotation.from_dcm(roty)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:47:28.401125Z",
+ "start_time": "2019-12-08T16:47:28.398057Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# circle leftwards for ngl quaternion representation\n",
+ "nglrot = list(np.roll(rot.as_quat(),-1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:47:46.235520Z",
+ "start_time": "2019-12-08T16:47:46.083362Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "dd0c14b7f0e545aeb257de83f049d251",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(NGLWidget(), VBox(children=(Dropdown(description='Show', options=('All', 'Au'), value='All'), D…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "v = ase.visualize.view(final_config,viewer='ngl')\n",
+ "v.view.remove_unitcell()\n",
+ "v.view.control.rotate(nglrot)\n",
+ "v.view.center()\n",
+ "v"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Parametric study"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The true advantage of arrag of arranging files as objects within a data base is the\n",
+ "comparably comfortable evalutation of parametric studies. For this purpose, we prepare \n",
+ "a simple parametric sweep across different cluster sizes and velocities in the above study:\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Build workflow"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have two gold cluster data files, one small cluster with 923 atoms and one larger cluster with 3871 atoms.\n",
+ "In this parametric study, we want to shoot the small cluster at 25 Angstrom / ps, but for the large cluster two vleocities are to be probed. In the following, we generate a workflow corresponding to the previous one, now branching at the cluster energy minimization step for different cluster sizes, and branching again at the last step for shooting the cluster at several velocities. Cluster size and velocity are attached to the output files as metadata, just as shown above, e.g. as\n",
+ "\n",
+ "```yaml\n",
+ " - _fw_name: AddFilesTask\n",
+ " compress: true\n",
+ " metadata:\n",
+ " cluster: large_cluster\n",
+ " cluster_size: 3871\n",
+ " material: gold\n",
+ " project: fireworks-hands-on-2019-12-09\n",
+ " type: trajectory\n",
+ " velocity: -50\n",
+ " paths: traj.dump\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:48:04.393699Z",
+ "start_time": "2019-12-08T16:48:04.387416Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# parametric study in two dimensions: cluster size and cluster velocity\n",
+ "clusters = {\n",
+ " 'small_cluster': {'cluster_size': 923}, # number of atoms\n",
+ " 'large_cluster': {'cluster_size': 3871}\n",
+ " }\n",
+ "\n",
+ "# depending on cluster size, we look at different sets of velocities:\n",
+ "cluster_velocities_dict = { \n",
+ " 'small_cluster': [-25],\n",
+ " 'large_cluster': [-25,-50] } # cluster approach velocity, Angtsrom / ps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:48:08.334247Z",
+ "start_time": "2019-12-08T16:48:08.293979Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# build workflow to span two parametric dimensions \"cluster size\" and \"velocity\"\n",
+ "fws = []\n",
+ "parent_child_deps = {}\n",
+ "\n",
+ "# default tasks:\n",
+ "get_potential_task = GetFilesTask(\n",
+ " identifiers = ['/'.join((project_id ,'potential'))],\n",
+ " new_file_names = ['Au-Grochola-JCP05.eam.alloy'] )\n",
+ "\n",
+ "run_lammps_task = ScriptTask.from_str('''\n",
+ " source /etc/profile; \n",
+ " module load LAMMPS; \n",
+ " mpirun -n 2 lmp -in lammps.in\n",
+ " ''')\n",
+ "\n",
+ "# substrate stays the same for all runs\n",
+ "\n",
+ "# ------------------\n",
+ "# Minimize substrate\n",
+ "# ------------------\n",
+ "\n",
+ "# define two tasks in Firework: Minimize substrate\n",
+ "# - GetFilesByQueryTask * 2\n",
+ "# - ScriptTask\n",
+ "ft = [ \n",
+ " get_potential_task, \n",
+ " GetFilesTask(\n",
+ " identifiers = ['/'.join((project_id ,'minimize_substrate'))],\n",
+ " new_file_names = ['lammps.in'] ) ]\n",
+ "\n",
+ "fw_A_substrate = Firework( \n",
+ " [*ft, run_lammps_task], \n",
+ " name=\"Minimize substrate\",\n",
+ " spec = { \n",
+ " '_category': \"bwcloud_noqueue\",\n",
+ " '_files_out': {'data_file': \"final.lammps\"},\n",
+ " 'metadata': {\n",
+ " 'project': project_id,\n",
+ " 'step': 'minimize_substrate' } } )\n",
+ "\n",
+ "# ---------------------\n",
+ "# equilibrate substrate\n",
+ "# ---------------------\n",
+ "\n",
+ "# define two tasks in Firework: Equilibrate substrate\n",
+ "# - FileTransferTask\n",
+ "# - ScriptTask\n",
+ "ft = [ \n",
+ " get_potential_task, \n",
+ " GetFilesTask(\n",
+ " identifiers = ['/'.join((project_id ,'equilibrate_substrate'))],\n",
+ " new_file_names = ['lammps.in'] ) ]\n",
+ "\n",
+ "fw_B_substrate = Firework( \n",
+ " [*ft, run_lammps_task], \n",
+ " name=\"Equilibrate substrate\",\n",
+ " spec={\n",
+ " '_category': \"bwcloud_noqueue\",\n",
+ " '_files_in': {'data_file': \"initial.lammps\"},\n",
+ " '_files_out': {'data_file_substrate': \"final.lammps\"},\n",
+ " 'metadata': {\n",
+ " 'project': project_id,\n",
+ " 'step': 'equlibrate_substrate' } } )\n",
+ "\n",
+ "fws.extend( [ fw_A_substrate, fw_B_substrate ] )\n",
+ "parent_child_deps.update( { \n",
+ " fw_A_substrate: [ fw_B_substrate ],\n",
+ " fw_B_substrate: [] } )\n",
+ "\n",
+ "for cluster, metadata in clusters.items():\n",
+ " \n",
+ " # ----------------\n",
+ " # Minimize cluster\n",
+ " # ----------------\n",
+ " \n",
+ " # define four tasks in Firework: Minimize cluster\n",
+ " # - FileTransferTask * 3\n",
+ " # - ScriptTask\n",
+ " ft = [ \n",
+ " get_potential_task, \n",
+ " GetFilesTask(\n",
+ " identifiers = ['/'.join((project_id ,cluster))],\n",
+ " new_file_names = ['initial.lammps'] ),\n",
+ " GetFilesTask(\n",
+ " identifiers = ['/'.join((project_id ,'minimize_cluster'))],\n",
+ " new_file_names = ['lammps.in'] ) ]\n",
+ "\n",
+ " fw_A_cluster = Firework(\n",
+ " [*ft, run_lammps_task], \n",
+ " name = \"Minimize cluster, N = {:d}\".format(metadata[\"cluster_size\"]),\n",
+ " spec = { \n",
+ " '_category': \"bwcloud_noqueue\",\n",
+ " '_files_out': { 'data_file': \"final.lammps\" },\n",
+ " 'metadata': {\n",
+ " 'project': project_id,\n",
+ " 'step': 'minimize_cluster',\n",
+ " 'cluster': cluster,\n",
+ " 'cluster_size': metadata[\"cluster_size\"] } } )\n",
+ " \n",
+ " # -------------------\n",
+ " # Equilibrate cluster\n",
+ " # -------------------\n",
+ "\n",
+ " # define two tasks in Firework: Equilibrate cluster\n",
+ " # - FileTransferTask\n",
+ " # - ScriptTask\n",
+ " ft = [ \n",
+ " get_potential_task, \n",
+ " GetFilesTask(\n",
+ " identifiers = ['/'.join((project_id ,'equilibrate_cluster'))],\n",
+ " new_file_names = ['lammps.in'] ) ]\n",
+ "\n",
+ " fw_B_cluster = Firework(\n",
+ " [*ft, run_lammps_task], \n",
+ " name = \"Equilibrate cluster, N = {:d}\".format(metadata[\"cluster_size\"]),\n",
+ " spec = {\n",
+ " '_category': \"bwcloud_noqueue\",\n",
+ " '_files_in': { 'data_file': \"initial.lammps\" },\n",
+ " '_files_out': {'data_file': \"final.lammps\" },\n",
+ " 'metadata': {\n",
+ " 'project': project_id,\n",
+ " 'step': 'equilibrate_cluster',\n",
+ " 'cluster': cluster,\n",
+ " 'cluster_size': metadata[\"cluster_size\"] } } )\n",
+ " \n",
+ " # ------------\n",
+ " # Melt cluster\n",
+ " # ------------\n",
+ "\n",
+ " # define two tasks in the Firework: Melt cluster\n",
+ " # - FileTransferTask\n",
+ " # - ScriptTask\n",
+ " ft = [ \n",
+ " get_potential_task, \n",
+ " GetFilesTask(\n",
+ " identifiers = ['/'.join((project_id ,'melt_cluster'))],\n",
+ " new_file_names = ['lammps.in'] ) ]\n",
+ " \n",
+ " fw_C_cluster = Firework(\n",
+ " [*ft, run_lammps_task], \n",
+ " name = \"Melt cluster, N = {:d}\".format(metadata[\"cluster_size\"]),\n",
+ " spec = {\n",
+ " '_category': \"bwcloud_noqueue\",\n",
+ " '_files_in': { 'data_file': \"initial.lammps\" },\n",
+ " '_files_out': { 'data_file_cluster': \"final.lammps\" },\n",
+ " 'metadata': {\n",
+ " 'project': project_id,\n",
+ " 'step': 'melt_cluster',\n",
+ " 'cluster': cluster,\n",
+ " 'cluster_size': metadata[\"cluster_size\"] } } )\n",
+ " \n",
+ " fws.extend( [ fw_A_cluster, fw_B_cluster, fw_C_cluster ] )\n",
+ " parent_child_deps.update( {\n",
+ " fw_A_cluster: [ fw_B_cluster ], \n",
+ " fw_B_cluster: [ fw_C_cluster ],\n",
+ " fw_C_cluster: [] } )\n",
+ "\n",
+ " for velocity in cluster_velocities_dict[cluster]:\n",
+ " #steps = velocity_steps_dict[velocity]\n",
+ " \n",
+ " # ----------------------------\n",
+ " # Deposit cluster on substrate\n",
+ " # ----------------------------\n",
+ " \n",
+ " # define five tasks in the Firework: Deposit\n",
+ " # - FileTransferTask\n",
+ " # - ScriptTask\n",
+ " # - AddFilesTask * 3\n",
+ " ft = [ \n",
+ " get_potential_task, \n",
+ " GetFilesTask(\n",
+ " identifiers = ['/'.join((project_id ,'deposit'))],\n",
+ " new_file_names = ['lammps.in'] ) ]\n",
+ "\n",
+ " st = ScriptTask.from_str('''\n",
+ " source /etc/profile; \n",
+ " module load LAMMPS; \n",
+ " mpirun -n 2 lmp -in lammps.in -v vel {vel:}\n",
+ " '''.format( vel=velocity ) ) # modify LAMMPS call to override parameters here!\n",
+ "\n",
+ " at = [ \n",
+ " AddFilesTask( {\n",
+ " 'compress':True ,\n",
+ " 'paths': \"final.lammps\",\n",
+ " 'metadata': {\n",
+ " 'project': project_id,\n",
+ " 'type': 'final configuration',\n",
+ " 'material': 'gold',\n",
+ " 'velocity': velocity,\n",
+ " 'cluster': cluster,\n",
+ " 'cluster_size': metadata[\"cluster_size\"]} } ),\n",
+ " AddFilesTask( {\n",
+ " 'compress':True ,\n",
+ " 'paths': \"traj.dump\",\n",
+ " 'metadata': {\n",
+ " 'project': project_id,\n",
+ " 'type': 'trajectory',\n",
+ " 'material': 'gold',\n",
+ " 'velocity': velocity,\n",
+ " 'cluster': cluster,\n",
+ " 'cluster_size': metadata[\"cluster_size\"]} } ),\n",
+ " AddFilesTask( {\n",
+ " 'compress':True ,\n",
+ " 'paths': \"log.lammps\",\n",
+ " 'metadata': {\n",
+ " 'project': project_id,\n",
+ " 'type': 'log',\n",
+ " 'material': 'gold',\n",
+ " 'velocity': velocity,\n",
+ " 'cluster': cluster,\n",
+ " 'cluster_size': metadata[\"cluster_size\"]} } ) ] \n",
+ "\n",
+ " fw_D_deposit = Firework(\n",
+ " [*ft, st, *at],\n",
+ " name = \"Deposit, N = {:d}, v = {:.2f}\".format(metadata[\"cluster_size\"],velocity), \n",
+ " spec = {\n",
+ " '_category': \"bwcloud_noqueue\",\n",
+ " '_files_in': { \n",
+ " 'data_file_cluster': \"cluster.lammps\",\n",
+ " 'data_file_substrate': \"substrate.lammps\" },\n",
+ " 'metadata': {\n",
+ " 'project': project_id,\n",
+ " 'step': 'deposit',\n",
+ " 'cluster': cluster,\n",
+ " 'cluster_size': metadata[\"cluster_size\"],\n",
+ " 'velocity': velocity} } )\n",
+ " \n",
+ " fws.extend( [ fw_D_deposit ] )\n",
+ " parent_child_deps[fw_B_substrate].append( fw_D_deposit )\n",
+ " parent_child_deps[fw_C_cluster].append( fw_D_deposit )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:48:11.647400Z",
+ "start_time": "2019-12-08T16:48:11.644172Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "wf = Workflow( fws, parent_child_deps, name = \"Gold cluster impact parametric study\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:48:13.110693Z",
+ "start_time": "2019-12-08T16:48:13.024235Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "wf.to_file('wf_parametric.yaml')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:49:09.021161Z",
+ "start_time": "2019-12-08T16:49:08.984580Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2019-12-08 17:49:09,018 INFO Added a workflow. id_map: {-11: 23574, -10: 23575, -9: 23576, -8: 23577, -7: 23578, -6: 23579, -5: 23580, -4: 23581, -3: 23582, -2: 23583, -1: 23584}\n"
+ ]
+ }
+ ],
+ "source": [
+ "fw_ids = lp.add_wf(wf)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Results overview "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:00:43.927231Z",
+ "start_time": "2019-12-08T17:00:43.909935Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "query = { \"metadata.project\": project_id }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:00:44.664460Z",
+ "start_time": "2019-12-08T17:00:44.647252Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "21"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# use underlying MongoDB functionality \n",
+ "fp.filepad.count_documents(query)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:01:20.485324Z",
+ "start_time": "2019-12-08T17:01:20.480446Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# query all files of type \"trajectory\" within project\n",
+ "query = { \n",
+ " \"metadata.project\": project_id,\n",
+ " \"metadata.material\": \"gold\",\n",
+ " \"metadata.type\": \"trajectory\"}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:01:21.219472Z",
+ "start_time": "2019-12-08T17:01:21.205071Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fp.filepad.count_documents(query) \n",
+ "# that many documents matching query"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we can use aggregation to get an overivew of the unique result datasets:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:01:25.332076Z",
+ "start_time": "2019-12-08T17:01:25.322790Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# use aggregation to determine unique parameter sets\n",
+ "# see https://docs.mongodb.com/manual/aggregation\n",
+ "\n",
+ "# first filter all files by a general query, here as above\n",
+ "# all trajectories in project\n",
+ "match_aggregation = {\n",
+ " \"$match\": query\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:01:27.347174Z",
+ "start_time": "2019-12-08T17:01:27.343032Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# group by parameters of interest, i.e. cluster_size and velocity:\n",
+ "group_aggregation = { \n",
+ " \"$group\": { \n",
+ " \"_id\": { \n",
+ " \"cluster_size\": \"$metadata.cluster_size\", \n",
+ " \"velocity\": \"$metadata.velocity\"\n",
+ " }\n",
+ " } \n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:01:27.867153Z",
+ "start_time": "2019-12-08T17:01:27.859845Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "aggregation_pipeline = [ match_aggregation, group_aggregation ]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:01:28.382977Z",
+ "start_time": "2019-12-08T17:01:28.369661Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# this aggregation yields all unique parameter sets as documents\n",
+ "cursor = fp.filepad.aggregate(aggregation_pipeline)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:01:28.863995Z",
+ "start_time": "2019-12-08T17:01:28.859417Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# we sort these sets by cluster_size\n",
+ "unique_parameter_sets = [ c[\"_id\"] for c in sorted(\n",
+ " cursor, key=lambda d: d[\"_id\"][\"cluster_size\"]) ]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Thereby, we get a simple overview on the unique combinations of parameters \"cluster_size\" and \"velocity\" that reside in our database:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:01:30.934963Z",
+ "start_time": "2019-12-08T17:01:30.929172Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'cluster_size': 923, 'velocity': -25},\n",
+ " {'cluster_size': 3871, 'velocity': -25},\n",
+ " {'cluster_size': 3871, 'velocity': -50}]"
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "unique_parameter_sets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:01:32.422757Z",
+ "start_time": "2019-12-08T17:01:32.299453Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# next, we use a bit of cryptic pandas formatting to get a simple\n",
+ "# two-dimensional visualization of existing and non-existing\n",
+ "# data sets\n",
+ "unique_parameter_sets_df = pd.DataFrame(unique_parameter_sets)\n",
+ "parameter_set_overview = unique_parameter_sets_df.groupby( \n",
+ " list(unique_parameter_sets_df.columns) ).size().unstack(\n",
+ " fill_value=0).astype(bool).style.applymap(\n",
+ " lambda v: 'color: {}'.format('green' if v else 'red'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:01:33.172787Z",
+ "start_time": "2019-12-08T17:01:33.153090Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ " #T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow0_col0 {\n",
+ " color: red;\n",
+ " } #T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow0_col1 {\n",
+ " color: green;\n",
+ " } #T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow1_col0 {\n",
+ " color: green;\n",
+ " } #T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow1_col1 {\n",
+ " color: green;\n",
+ " }</style> \n",
+ "<table id=\"T_62f0cd3e_19dc_11ea_899a_fa163eeb709b\" > \n",
+ "<thead> <tr> \n",
+ " <th class=\"index_name level0\" >velocity</th> \n",
+ " <th class=\"col_heading level0 col0\" >-50</th> \n",
+ " <th class=\"col_heading level0 col1\" >-25</th> \n",
+ " </tr> <tr> \n",
+ " <th class=\"index_name level0\" >cluster_size</th> \n",
+ " <th class=\"blank\" ></th> \n",
+ " <th class=\"blank\" ></th> \n",
+ " </tr></thead> \n",
+ "<tbody> <tr> \n",
+ " <th id=\"T_62f0cd3e_19dc_11ea_899a_fa163eeb709blevel0_row0\" class=\"row_heading level0 row0\" >923</th> \n",
+ " <td id=\"T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow0_col0\" class=\"data row0 col0\" >False</td> \n",
+ " <td id=\"T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow0_col1\" class=\"data row0 col1\" >True</td> \n",
+ " </tr> <tr> \n",
+ " <th id=\"T_62f0cd3e_19dc_11ea_899a_fa163eeb709blevel0_row1\" class=\"row_heading level0 row1\" >3871</th> \n",
+ " <td id=\"T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow1_col0\" class=\"data row1 col0\" >True</td> \n",
+ " <td id=\"T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow1_col1\" class=\"data row1 col1\" >True</td> \n",
+ " </tr></tbody> \n",
+ "</table> "
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f21310b53c8>"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "parameter_set_overview"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Thsi might look like overkill for only two parameters, but will help to keep an overview when mapping out a high-dimensional parametric space."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Results anaylsis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With properly formulated queries, evaluating the results selectively becomes easy."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T13:13:22.605452Z",
+ "start_time": "2019-12-08T13:13:22.601258Z"
+ }
+ },
+ "source": [
+ "#### Large cluster"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:02:12.833861Z",
+ "start_time": "2019-12-08T17:02:12.824176Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# query a specific \"trajectory\" file\n",
+ "query = { \n",
+ " \"metadata.project\": project_id,\n",
+ " \"metadata.type\": \"trajectory\",\n",
+ " \"metadata.velocity\": -25,\n",
+ " \"metadata.cluster_size\": 3871}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:02:14.678633Z",
+ "start_time": "2019-12-08T17:02:14.662808Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(fp.filepad.count_documents(query) )\n",
+ "# that many documents matching query"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:02:19.384046Z",
+ "start_time": "2019-12-08T17:02:18.707248Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# the FilePad yields trajectory and attached metadata\n",
+ "traj_file, traj_metadata = fp.get_file_by_query(query)[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:02:20.726326Z",
+ "start_time": "2019-12-08T17:02:20.711844Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'_id': ObjectId('5ded2ad811940f336850cbe5'),\n",
+ " 'compressed': True,\n",
+ " 'gfs_id': '5ded2ad611940f336850cbaa',\n",
+ " 'identifier': '/mnt/dat/fw/launcher_2019-12-08-16-53-00-210573/traj.dump',\n",
+ " 'metadata': {'cluster': 'large_cluster',\n",
+ " 'cluster_size': 3871,\n",
+ " 'material': 'gold',\n",
+ " 'project': 'fireworks-hands-on-2019-12-09',\n",
+ " 'type': 'trajectory',\n",
+ " 'velocity': -25},\n",
+ " 'original_file_name': 'traj.dump',\n",
+ " 'original_file_path': '/mnt/dat/fw/launcher_2019-12-08-16-53-00-210573/traj.dump'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "pprint(traj_metadata)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:02:29.412136Z",
+ "start_time": "2019-12-08T17:02:22.122160Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "traj_large = ase.io.read(\n",
+ " io.StringIO(traj_file.decode()),\n",
+ " format='lammps-dump-text',\n",
+ " index=':',\n",
+ " specorder=[ase.data.atomic_numbers['Au']])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:02:34.437739Z",
+ "start_time": "2019-12-08T17:02:34.251866Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "w = ase.visualize.view(traj_large, viewer='ngl')\n",
+ "w.view.remove_unitcell()\n",
+ "w.view.control.rotate(nglrot)\n",
+ "w.view.center()\n",
+ "w.view.control.zoom(-1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T13:13:22.605452Z",
+ "start_time": "2019-12-08T13:13:22.601258Z"
+ }
+ },
+ "source": [
+ "#### Small cluster"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:02:39.588581Z",
+ "start_time": "2019-12-08T17:02:39.584095Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# query a specific \"trajectory\" file\n",
+ "query = { \n",
+ " \"metadata.project\": project_id,\n",
+ " \"metadata.type\": \"trajectory\",\n",
+ " \"metadata.velocity\": -25,\n",
+ " \"metadata.cluster_size\":923}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:02:44.340553Z",
+ "start_time": "2019-12-08T17:02:44.328130Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(fp.filepad.count_documents(query) )\n",
+ "# that many documents matching query"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:02:51.283477Z",
+ "start_time": "2019-12-08T17:02:50.841214Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "traj_file, traj_metadata = fp.get_file_by_query(query)[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:02:56.137252Z",
+ "start_time": "2019-12-08T17:02:56.125394Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'_id': ObjectId('5ded2b3411940f336850cc20'),\n",
+ " 'compressed': True,\n",
+ " 'gfs_id': '5ded2b3111940f336850cbf3',\n",
+ " 'identifier': '/mnt/dat/fw/launcher_2019-12-08-16-54-48-943523/traj.dump',\n",
+ " 'metadata': {'cluster': 'small_cluster',\n",
+ " 'cluster_size': 923,\n",
+ " 'material': 'gold',\n",
+ " 'project': 'fireworks-hands-on-2019-12-09',\n",
+ " 'type': 'trajectory',\n",
+ " 'velocity': -25},\n",
+ " 'original_file_name': 'traj.dump',\n",
+ " 'original_file_path': '/mnt/dat/fw/launcher_2019-12-08-16-54-48-943523/traj.dump'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "pprint(traj_metadata)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:03:06.305422Z",
+ "start_time": "2019-12-08T17:03:00.612053Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "traj_small = ase.io.read(\n",
+ " io.StringIO(traj_file.decode()),\n",
+ " format='lammps-dump-text',\n",
+ " index=':',\n",
+ " specorder=[ase.data.atomic_numbers['Au']])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:03:13.623193Z",
+ "start_time": "2019-12-08T17:03:13.416401Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "72e7ae269396473eb396f457021b2ce0",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(NGLWidget(count=41), VBox(children=(Dropdown(description='Show', options=('All', 'Au'), value='…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "v = ase.visualize.view(traj_small, viewer='ngl')\n",
+ "v.view.remove_unitcell()\n",
+ "v.view.control.rotate(nglrot)\n",
+ "v.view.center()\n",
+ "v.view.control.zoom(-1)\n",
+ "v"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:03:21.549678Z",
+ "start_time": "2019-12-08T17:03:21.533811Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e5e4fe373616491b879f11e92f6e754c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(NGLWidget(count=41), VBox(children=(Dropdown(description='Show', options=('All', 'Au'), value='…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "w"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Selective analyisis at fixed cluster size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:19:52.472683Z",
+ "start_time": "2019-12-08T16:19:52.451156Z"
+ }
+ },
+ "source": [
+ "Here, we take a slice at fixed cluster size and compare thermodynamic \n",
+ "observables along the trajectroies for slow and fast impact velocity."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:03:43.667866Z",
+ "start_time": "2019-12-08T17:03:43.663197Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Compare along velocity dimension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:03:50.945288Z",
+ "start_time": "2019-12-08T17:03:50.940551Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# query a specific \"trajectory\" file\n",
+ "query = { \n",
+ " \"metadata.project\": project_id,\n",
+ " \"metadata.material\": \"gold\",\n",
+ " \"metadata.type\": \"log\",\n",
+ " \"metadata.cluster_size\": 3871}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:04:11.317491Z",
+ "start_time": "2019-12-08T17:04:11.300215Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(fp.filepad.count_documents(query) )\n",
+ "# that many documents matching query"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:04:28.022584Z",
+ "start_time": "2019-12-08T17:04:28.000551Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "files = fp.get_file_by_query(query)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:04:34.997340Z",
+ "start_time": "2019-12-08T17:04:34.993030Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Interseting thermodynamic data in LAMMPS log occurs between a table header \n",
+ "# line always starting with \"step\", e.g. \"Step KinEng PotEng TotEng Temp\" and\n",
+ "# a concluding line always starting with \"Loop\", e.g. \"Loop time of 111.167 \n",
+ "# on 2 procs for 4000 steps with 12671 atoms\". We use this knowledge to\n",
+ "# extract this table with a regular expression:\n",
+ "regex = re.compile(\"(^Step(?:.(?!^Loop))*)\", re.MULTILINE | re.DOTALL)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:04:42.409117Z",
+ "start_time": "2019-12-08T17:04:42.385127Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "data = pd.DataFrame()\n",
+ "for content, metadata in files:\n",
+ " m = regex.findall(content.decode())[0] # takes the first thermo output block\n",
+ " \n",
+ " d = pd.read_csv(io.StringIO(m),sep='\\s+')\n",
+ " d[\"velocity\"] = metadata[\"metadata\"][\"velocity\"]\n",
+ "\n",
+ " data = data.append(d,ignore_index=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:04:49.630293Z",
+ "start_time": "2019-12-08T17:04:49.626634Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "columns = ['KinEng', 'PotEng','Temp', 'Press',]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:04:57.159769Z",
+ "start_time": "2019-12-08T17:04:57.155454Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "V = data.velocity.unique() # unique velocities, should be -25 and -50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:05:06.561983Z",
+ "start_time": "2019-12-08T17:05:04.947042Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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/n9AuIpiucWF0bhPKdT3jGNE7/oLHeVVdN6ugmC8OFrD5QB4f7Mjip4s+A6BfUiTf6NaGLnFhXNk5xgpmYy7EsplweEvdnrPd5TDyqbPunjlzJklJSUyfPh2Axx9/nLCwMB544IE6uXxISAjDhg0DIDAwkAEDBpCRkVEn5zZNQ2beSX7/zk5e35RBeJA/vxl7ObekJtlQCh+yAtk0SX5+wqSvpTCmXwKvb8zg04x8Xlm9j5c+2UtKbCixYUHsOFJIXlHZGcdGhQQQGRJIYXE5USEB3Da4A0O7x3FV5xgC6mAohIgQ0SqAiFYBdG0bzk39E1DtxZbMfFbuzObtLw7zzPu7qHqI5VWdY/hWn3iu6hxLckyI3RCNaWTS0tK49957TxXICxcuZPny5We0u+aaaygsLDwj/vTTTzN8+PBaXSsvL48333yTe+6551TstddeY+XKlXTr1o1Zs2aRlJR0kZ/ENDaVlcof3tvF3z7cDcC0azrxw6GdiQwJ9HFmxgpk06RFhwYy5ZpOAGSM6M7CdQfYkpnPiZIKruvRlp7x4USHBhIVGkh0SCBRIYG0jwxu8DHBIkKfxEj6JEYy49quFJdVsONwIe9vz+L/Nmfy8BufAxAe7M/glBjGD0piaPc4PLZ8jzGnO0dPb33p378/WVlZHDx4kOzsbKKiomosUletWnVJ1ykvL2f8+PHcfffddOrk3NdGjRrF+PHjCQoK4rnnnmPixIm8//77l3Qd0zgUFpfx//7vc/5v80HG9GvPgyN62OOgGxHRqm6sFi41NVXXr1/v6zRMC6Sq7M4+wbq9x9iSmc+7W4+QVVhCfEQwfRMj6dYunAEdIumfFEVEiE3SaIpEZIOqpvo6j6agpnvxtm3b6Nmzp48ycjz66KPExsZy+PBh2rVrx913331Gm9r2IFdUVDBw4EAARo8ezRNPPAHAXXfdRVhYGM8880yNOVRUVBAdHU1+fv4Z+xrDn5GpnYpK5ePdR3l08Rd8efQED1zvrGds3x42jNrej60H2RgfExG6xIXRJS6M8cDPR/fmvW1HeH1jJjuPFPLO1sNUuv+P7RIXRv+kSAZ0jKJ/h0i6xoVbL7MxDSAtLY2pU6dy9OhRPvzwwxrb1LYH2ePxsHnz5tNijzzyCPn5+Tz//POnxQ8dOkR8vPME0SVLllgR3MRl5Bbxo/kb+Swjn+jQQF6dNoTBnWJ8nZapgRXIxjQyAR4/RlwWz4jLnF+Kx0vK+fRAHpv257Jpfx7vbjvCvzY4E3jCgvzpmxTBwA5RXN+7HV3bhhHkb4vHm/MTkV8AY4BKIAu4U1UPisj3gAcBAQqBH6rqp+4xe91YBVBe1QsjItHAq0AysBe4RVVzxekS+yNwI1DkXmNjQ33GutS7d28KCwtJSEg4VbDWlYyMDJ588kl69OjBgAEDgK+Wc3vmmWdYsmQJ/v7+REdHM3fu3Dq9tmk4u7OPk/bcJ5SUVfK/4/ryrT7xBAfY/bqxsiEWLhtiYZoKVWVfThEb3YJ504Fcth0qpKJS8RPoGBNK17gw+rnDMvokRhAaZP8X9qXGOMRCRFqraoH7/m6gl6r+QESuAra5Be5I4HFVHey22wukqurRauf6LXBMVZ8SkZlAlKo+KCI3Av+DUyAPBv5Yda6zaaxDLBo7+zNq3A4cK+KW5z6hrKKSBdOG0CXu4pcmNZfGhlgY00yJCMmxoSTHhvLdAYkAZBUU88meHHZnHWdX1nG2HSrgna1HAPAT6NY2nL6JkXSICSExqhXJMaF0axtujyptwaqKY1cooG78Y6/4aiCxFqcbAwx1388DPsDphR4DvKROT8xqEYkUkXhVPXRp2RvTdGQVFHP7nDWcKCnn1e9facVxE2EFsjHNQFzrYMb0SzgtduxEqTM0wx2esWLbEY6dKD21XwTatQ4mPiKY+MhWtI8IJj6iFe0jg2kX4WzHhgXhZ2Ocmy0ReRKYAOQDw2poMhlY5rWtwDsiosBzqjrbjbf1KnoPA23d9wnAAa/jM9zYaQWyiEwDpgF06NDhoj+PMY3NsROlfO/5NRwtLOGVKYPpGd/a1ymZWrIC2ZhmKjo0kGE94hjWI+5U7ERJOQfzTrI7+zjbDxdy4NhJDuWfZOvBAt7deoSS8srTzhHgEdq2DqZ9RCviI78qoOMjWhEfEUy7iGDCgvwJ8vezGdiNkIi8C7SrYdfDqrpYVR8GHhaRh4AZwGNexw7DKZCv9jrualXNFJE4YIWIbFfVld4nVlV1C+hacwvt2eAMsThLG/s3dhY2VLJxSs8q5EfzN7L/WBFzJw2if4coX6dkLoAVyMa0IKFB/nRtG07XtuGnJgFWUVVyi8o4mHeSQ/nFHMo/ycG8Yg7nn+RgfjEb9+dyOP8QZRVn/jL2EwgN9KdNeBDt3MLZKaBbEd86mPjIYNqEBRHo70eAp+olVvDUM1Wt3dMpYD6wFLdAFpE+wPPASFXN8TpfpvszS0TeAAYBK4EjVUMnRCQeZ9IfQCbgvWBwohu7IMHBweTk5BATE2P/ZqpRVXJycggODvZ1KsalqizakMGji78gJNDDC3dewZWdbaWKpsYKZGMM4Ixtjg4NJDo0kMsSImpsU1mpHD1RwqE8p4A+UlDCidJyikoqOF5STvbxEg7lnWT17hyOFJZQUXnunq1Ajx+tAj2EBnoIDfInJMif0EAPIYH+hAa5PwM9lFZUUlRaQUSrACJbBZxaDzrneCnHS8qJaBVAu9bBRIUGEhzgR3CAh2B/D0Wl5eSfLCOiVYDzsJjQQGJCA63IAkSkq6rucjfHANvdeAfgdeAOVd3p1T4U8FPVQvf99cAT7u4lwETgKffnYq/4DBFZgDNJL/9ixh8nJiaSkZFBdnb2hR7aIgQHB5OYWJuh4qa+qSoPvvYZC9dnMDglmmfG96dta/vPS1NkBbIxptb8/IS48GDiwoPpmxR5zrYVlcrR4yUcynd6obOPl1JWXklZRdVLKSmv5GRpOSdKKygqLed4SQVFJeXkFp2kqLScEyUVnCgpJ9Dfj5BADwUnyzhRWnHadUICPRRVi51LaKCH+MhWqOqpx32HBHkIDwogwN+P4rIKSsoqKCmvpHVwAJ3jQkmOCSUs2J/QQH+iQwPp1jacdhFN/pfeUyLSHWeZt33AD9z4o0AM8Bf3PxJVy7m1Bd5wY/7AP1T17apzAQtFZLJ7rlvc+FKcFSzScZZ5m3QxiQYEBJCSknIxhxrToFbuOsrC9RlMvSaFmSN72jr1TZgt8+ayZd6MaRpKyyvJP1mGokSHBOLvcYra7MIS8orKKC6voLisguKySoID/IhoFUD+yTLyiso4eryEfTlFHM4vxuMn+PkJqkpRaQWFxWWUVijB/k4PdJC/H7lFpezOPnHa5EaAR7/di7uurn3B1hiXeWus7F5smqqjx0v4zl8+AuC9Hw8l0N/PxxmZmtgyb8aYZinQ34824UGnxYIDPCRFh5AUXT/XLCguo6ikghOl5Rw7UUpCZKv6uZAxpknKKijm1tmryS4sYf6UIVYcNwM++RsUkftE5AsR+VxE/ikiwSKSIiJrRCRdRF4VkUC3bZC7ne7uT/Y6z0NufIeI3OAVH+HG0t1F640x5qK1Dg6gXUQwnduEcUVyNO2tQDbGuPZkH+d7z6/hcEExL08ezMCOtlpFc9DgBbKIJAB34zyN6TLAA9wK/AaYpapdgFyc5YVwf+a68VluO0Skl3tcb2AEzng5j4h4gD8DI4FewHi3rTHGGGNMnflwZzY3PrOKrMISXrjzCq5IrqevsUyD89V3AP5AKxHxB0JwFo2/Fljk7p8H3OS+H+Nu4+6/TpxZImOABapaoqpf4kwCGeS+0lV1j6qWAgvctsYYY4wxdSLneAn3L9xMckwo79z3dYZ0sqXcmpMGL5DddTSfBvbjFMb5wAYgT1XL3WZVT1sCrycxufvzcWZYn+0JTWeLn0FEponIehFZb8sHGWOMMaY2sgqKmfLSegpOlvPHW20pt+bIF0MsonB6dFOA9kAozhCJBqeqs1U1VVVT27Rp44sUjDHGGNOEHDhWxK2zV7PjcCGz0vrRvV24r1My9cAXq1gMB75U1WwAEXkd+BoQKSL+bi+x99OWqp7ElOEOyYgAcjj3E5ou+clNxhhjjDHeNh/IY8q8dZRVKC/dNYhUG3PcbPliDPJ+YIiIhLhjia8DtgL/AW5221R/EtNE9/3NwPvqLN68BLjVXeUiBegKrAXWAV3dVTECcSbyLWmAz2WMMcaYZmpLRj7f+/tqWgV6eO2HV1lx3Mw1eA+yqq4RkUXARqAc2ATMBv4NLBCRX7qxOe4hc4CXRSQdOIZT8KKqX4jIQpziuhyYrqoVACIyA1iOs0LGC6r6RUN9PmOMMcY0H6rKhzuz+fHCT4kMCeRf37+qOTxJ05yHTx4UoqqPAY9VC+/BWYGiettiYNxZzvMk8GQN8aU4jzg1xhhjjLkoqsrP39zK3I/30ik2lDl3XmHFcQthT9IzxhhjjKnBur25zP14L3cM6cjD3+pJcIDH1ymZBmLPQjTGGGOMqcHslXuIDAngZzdacdzSWIFsjDEtkIj8QkQ+E5HNIvKOiLR340NFJN+NbxaRR72OGSEiO0QkXURmesVTRGSNG3/VnSCNO4n6VTe+RkSSG/pzGnOxth4s4N1tR7jzqmRaBVpx3NJYgWyMMS3T71S1j6r2A94CHvXat0pV+7mvJwBExAP8GRgJ9ALGi0gvt/1vgFmq2gXIBSa78clArhuf5bYzpkl49j+7CAvyZ9JVKb5OxfiAFcjGGNMCqWqB12YooOc5ZBCQrqp7VLUUWACMcZfrvBZY5LabB9zkvh/jbuPuv85tb0yjpaq8/Mlelm45zF1fSyYiJMDXKRkfsEl6xhjTQonIk8AEIB8Y5rXrShH5FDgIPOAulZkAHPBqkwEMBmKAPPchT1XxBPf9qWNUtVxE8t32R+vnExlzaSoqlYff2MKCdQcY2r0NM67t6uuUjI9YD7IxxjRTIvKuiHxew2sMgKo+rKpJwHxghnvYRqCjqvYF/gT8XwPkOU1E1ovI+uzs7Pq+nDFn9dvl21mw7gDTh3VmzsQrCPS3Mqmlsr95Y4xpplR1uKpeVsNrcbWm84Gx7jEFqnrcfb8UCBCRWCATSPI6JtGN5QCRIuJfLY73Me7+CLd99Txnq2qqqqa2adOmDj65aQxUlf/syGLXkUJfp1Irn+zOYfbKPdw2uAM/uaEHHj8bDdSSWYFsjDEtkIh4f3c8BtjuxttVjRMWkUE4vydygHVAV3fFikCcp5ouUVUF/gPc7J5rIlBVgC9xt3H3v++2N81cRaXy+xU7mfTiOr45ayV/X7nH1ymdU1lFJQ+9/hnJMaE88q2evk7HNAI2BtkYY1qmp0SkO1AJ7AN+4MZvBn4oIuXASeBWt6gtF5EZwHLAA7zgjk0GeBBYICK/BDYBc9z4HOBlEUkHjuEU1aYF+O3y7Tz34R5SO0YRGRLAr5ZtI/t4CdOHdmmUk94WrN3P3pwi5kxMJSTQSiNjBbIxxrRIqjr2LPFngWfPsm8psLSG+B6cVS6qx4uBcZeWqWlqjh4v4cWP9vKty+N5Znx/TpSW89jiL5i9cg/Pr9pDcmwow7rHcV3POK7qHOvrdPn0QB6/Wrqdq7vEcm2POF+nYxoJK5CNMcYYU2eWbD5IaXkl9wzvisdPaB0cwKy0fqRdkcRH6Ud5d1sWc/77JXP++yX/O64vvRNac+xEKX0SIwkLariyRFWdHN7ZSUxYIL+/pS+2CqGpYgWyMcYYY+pERaUyf80+Lk+IoFvb8NP2DekUw5BOMdw3vBuZeSeZ8c9N3P+vT0/t792+Nf/6wZUNNsThXxsy+OW/t3FN11ieHteXuNbBDXJd0zTYJD1jjDHGXLLS8kp++/Z2dmef4IdDO5+1nZ+fkBQdwp9v68/Iy9rxo6Gd+ckN3fniYAG/fXtHg+Sac7yEJ/+9jUEp0cydNIi2VhybaqwH2RhjjDGX7LElX/DPtftJS01iRO92522fGBXCX28feGq7auxyv6RIbuqfcI4jL01peSXT/7GRk2UV/GLMZbacm6mRFcjGmAtSVlZGRkYGxcXFvk6l0QkODiYxMZGAgMY3S9/tP5NWAAAgAElEQVSY+lReUcnSLYcYeVk7nhp7+UWN5X3kW73YuD+Pp5Zt5xvd2hAVGlgPmcKvlm5j9Z5jzErrS/d24ec/wLRIViAbYy5IRkYG4eHhJCcn24QWL6pKTk4OGRkZpKSk+DodYxrUJ3tyyD9Zxqi+7S/6vuDxE34+uje3/O0TBv3qXZ4e15cx/eq2J3n93mPM/Xgvd16VzHf6J9bpuU3zYmOQjTEXpLi4mJiYGCuOqxERYmJirGfdtEi/X7GTdq2DGdb90pZJ65cUyStTBtMrvjU/XvgpU19az6OLP6cuni9TUan89u0dxIYF8uCIHpd8PtO81aoHWUS2ANX/deYD64FfquoZjw41xjRfVhzXrD7/XESkkLPfh+931yI2psF9cTCfTfvzeHxUL1oFei75fINSovnH1CHM+MdGVmw9AsCVnWIYeXn8RZ+zrKKSRxd/ztq9x/jt2D51kqdp3mo7xGIZUAH8w92+FQgBDgNzgVF1npkxxhhvfwAycO7DgnMf7gxsBF4AhvosM9OivfXZIQI8UqfDIUKD/Hlx0iAqKpVv/v5DZq/ac9EFcu6JUu5esIlVu44yfVhnxqXa0ApzfrUtkIer6gCv7S0islFVB4jI7fWRmDHG+MrQoUM5dOgQrVq1AuCdd94hLi6OkpISJkyYwIYNG4iJieHVV18lOTm5odIarap9vbZni8hmVX1QRH7WUEkYU91nGXn0jG9dL5PqPH7C7UM68sRbW/nvrqNc3bX2T97LzDvJk//eyn+2Z1NWUclvx/bhliuS6jxH0zzVdgyyR0ROPUZURK4Aqr6fKK/zrIwxxsfmz5/P5s2b2bx5M3FxzrjKOXPmEBUVRXp6Ovfddx8PPvhgQ6ZUJCK3iIif+7oFqBrwfOkDNI25CKrK1oMF9IpvXW/XuG1wBzrFhjLz9c84UVK7kmN/ThFpz33Cqp1HGTswgaX3XGPFsbkgtS2QpwBzRORLEdkLzAGmikgo8Ov6Ss4YY6qbOXMmf/7zn09tP/744zz99NMNcu3FixczceJEAG6++Wbee++9Opk8VEvfA+4AsoAj7vvbRaQVMKOhkjDG25GCEnKLyuhZjwVycICH397ch8y8k0x6cR37c4pO7VNVVJXS8koKi8v4x5r9PLjoM0b8cSUFJ8v4x9Qh/PKmy894qp8x51OrIRaqug64XEQi3O18r90L6yMxY0zj9/M3v2DrwYI6PWev9q15bFTvs+5PS0vj3nvvZfr06QAsXLiQ5cuXn9HummuuobCw8Iz4008/zfDhw8+bx6RJk/B4PIwdO5ZHHnkEESEzM5OkJKcXyt/fn4iICHJycoiNrf3XvhfLnYR3tvke/633BIypwfvbswAY0CGqXq+TmhzNXV9LYc5/v+Rbf1rFj4Z24d1tR9h1pJCIkAAOHDt5qm2rAA83Xh7PvcO7khQdUq95meartqtYBAFjgWTAv2qmtqo+UW+ZGWNMDfr3709WVhYHDx4kOzubqKioU0Wrt1WrVl30NebPn09CQgKFhYWMHTuWl19+mQkTJlxK2pdMRNoAU3Hvw1VxVb3rIs/3C2AMUInTK32nqh4UkZ/g9FbjXqcn0EZVj7nfIBbiTNouV9VU91zRwKtubnuBW1Q1V5xfFn8EbgSK3GtsvJh8TeP02sYMusSFcVlC/fUgV/nZjT25ukssf3h3J795ezvRoYEMSomhuKyCb/dpT15RKbekJnFZQgQBHlvF1lya2k7SW4yznNAGoKT+0jHGNCXn6umtT+PGjWPRokUcPnyYtLS0GtvUtge5oqKCgQOdx92OHj2aJ554goQEZzZ+eHg4t912G2vXrmXChAkkJCRw4MABEhMTKS8vJz8/n5iYmHr4hDVaDKwC3sUpUC/V71T1/wGIyN3Ao8APVPV3wO/c+CjgPlU95nXcMFU9Wu1cM4H3VPUpEZnpbj8IjAS6uq/BwF/dn6YZ+PLoCTbsy+XBET0aZOlHj58wrEccw3rEcTDvJBGtAggNsuedmfpR239Ziao6ol4zMcaYWkpLS2Pq1KkcPXqUDz/8sMY2te1B9ng8bN68+dR2eXk5eXl5xMbGUlZWxltvvXWqoB49ejTz5s3jyiuvZNGiRVx77bUNuSZ0iKrW2axAVfUeGxNKzRP9xgP/rMXpxvDVMnPzgA9wCuQxwEvqDNReLSKRIhKvqocuNm/TeCz73Plr/E7/un3aXW20j2zV4Nc0LUttC+SPReRyVd1Sr9kYY0wt9O7dm8LCQhISEoiPv/iHB9SkpKSEG264gbKyMioqKhg+fDhTp04FYPLkydxxxx106dKF6OhoFixYUKfXPo+3RORGVV1aVycUkSeBCTjfEA6rti8EGMHpEwAVeEdEFHhOVWe78bZeRe9hoK37PgE44HV8hhs7rUAWkWnANIAOHTpc4qcyDWXboUISo1rRLiLY16kYU+dqWyBfDdwpIl/iDLEQQFW1T71lZowx57BlS/38fz00NJQNGzbUuC84OJh//etf9XLdWrgH+JmIlABlfHUfPuvgTxF5F2hXw66HVXWxqj4MPCwiD+EUwo95tRkFfFRteMXVqpopInHAChHZrqorvU+squoW0LXmFtqzAVJTU23JuiZi15FCusaF+ToNY+pFbQvkkXV5URGJBJ4HLsPpkbgL2MEFTvIQkYnAI+5pf6mq89z4QJwn/LUClgL3aAOuxWSMMXVNVS94nSpVPf9yHY75OPdK7wL5VqoNr1DVTPdnloi8AQwCVgJHqoZOiEg8zqQ/gEzAewZlohszTVx5RSV7sk/wjW5tfJ2KMfXinNM8ReRaAFXdB/ip6r6qFzDwEq77R+BtVe0B9AW28dUkj67Ae+42nD7JYxrOJI+qWdOP4Uz4GAQ8JiJV68z8FWe2d9VxNn7aGNMkeT+tVES+Vm3fRa9/LCJdvTbHANu99kUA38CZGFgVCxWR8Kr3wPXA5+7uJcBE9/1Er+OWABPEMQTIt/HHzcPu7BOUVlTa+sKm2TrfOijeq++/Vm3fI1wE98b7dZyHjaCqpaqah3ODnuc2mwfc5L4/NclDVVcDkW4PxQ3AClU9pqq5wApghLuvtaqudnuNX/I6lzHGNDU/9nr/p2r7LmqJN9dTIvK5iHyGU+ze47XvO8A7qnrCK9YW+K+IfAqsBf6tqm9XnQv4pojsAoa72+D0Su8B0oG/Az+6hHxNI7J2rzPy5orkaB9nYkz9ON8QCznL+5q2aysFyAZeFJG+OEvH3cOFT/I4VzyjhvgZbGKIMaYJqI/7MKo69hz75uIMU/OO7cH5xq+m9jnAdTXEFZh+sTmaxmvtl8do1zqYpGhbTcI0T+frQdazvK9pu7b8gQHAX1W1P3CCr4ZTOCd2bqr1PmZYVWeraqqqprZpY+OojDGNUn3ch425JF9k5tMvKbIhlzk0pkGdrwe5k4gswemlqHqPu51ykdfMADJUdY27vQinQL7QSR6ZfLXuZlX8AzeeWEN7Y4xpinq4wyAE6Oy+x93u5Lu0TEtVXFbB3pwTjOrb3tepGFNvzlcgj/F6/3S1fdW3a0VVD4vIARHprqo7cL6W2+q+JuKMXas+yWOGiCzAmZCX7xbRy4FfeU3Mux54yH0caoE7IWQNzhqf1cftGWNMjYqKihg3bhy7d+/G4/EwatQonnrKGVI7d+5cfvKTn5x60t6MGTOYMmVKfafUs74vYMyFSM86TqVC93Y2Qc80X+cskFW15kdUXbr/AeaLSCDOBI5JOMM9ForIZGAfcIvbdinOEm/pOMu8TXJzOyYivwDWue2e8Fqv80d8tczbMvdljDG18sADDzBs2DBKS0u57rrrWLZsGSNHOqtdpqWl8eyzzzZYLu6qQYjIb6o/SU9EfoPzxDpjGsyOw84j3Lu1tTWQTfNVq3WQ3aWFHgc6usdULVB/UV/vqepmILWGXRc0yUNVXwBeqCG+HmeNZWNMMzNz5kySkpKYPt25LTz++OOEhYXxwAMP1Mn5Q0JCGDbMeahcYGAgAwYMICMj4zxHNYhvcmYxPLKGmDH1atOBXMKD/EmJtQLZNF+1fVDIHOA+nBUnKuovHWNMk7JsJhyu4yfatbscRj511t1paWnce++9pwrkhQsXsnz58jPaXXPNNRQWFp4Rf/rppxk+vHbPz8jLy+PNN9/knnu+WgHttddeY+XKlXTr1o1Zs2aRlJR0jjNcOhH5Ic63Yp28xh8DhAMf1evFjanB+r259OsQicfPJuiZ5qu2BXK+qtowBWOMz/Xv35+srCwOHjxIdnY2UVFRNRapq1atuqTrlJeXM378eO6++246dXK+LBs1ahTjx48nKCiI5557jokTJ/L+++9f0nVq4R84w8R+zekr/hRWewy0MfWuoLiMHUcKGXFZTU8wN6b5qG2B/B8R+R3wOlBSFax65LMxpoU6R09vfRo3bhyLFi3i8OHDpKWl1dimtj3IFRUVDBzoPBh09OjRPPHEEwBMmzaNrl27cu+9955qGxMTc+r9lClT+OlPf1onn+dcVDUfyAfGu2vHX+PuWgVYgWwa1Ob9eahCakd7QIhp3mpbIA92f3qPG1bg2rpNxxhjzi8tLY2pU6dy9OhRPvyw5rnEte1B9ng8bN68+bTYI488Qn5+Ps8///xp8UOHDhEfHw/AkiVL6Nmz4RaYEJG7cR5s9LobekVEZquqrdJjGsyGfbn4CfRNivB1KsbUq1oVyKo6rL4TMcaY2urduzeFhYUkJCScKljrSkZGBk8++SQ9evRgwIABwFfLuT3zzDMsWbIEf39/oqOjmTt3bp1e+zymAIOrHv/srmDxCbaMpWlAG/fn0q1tOOHBAb5OxZh6dc4CWURuV9VXROTHNe1X1d/XT1rGGHNuW7bU8eRAV2JiIs7iOWf69a9/za9//et6uW4tCKdPkq7gEh41bcyFqqhUNu3PY0w/e0CIaf7O14Mc6v6saTVwe8SpMcY0nBeBNSLyhrt9E84KQ8Y0iF1ZhRwvKWdgx6jzNzamiTtfgbwUQFV/Xn2HiHy7XjIyxhhzBlX9vYh8AFzthiap6iYfpmRamC0Z+QD0SYz0cSbG1D+/8+xfISLJ1YMiMgn4Y30kZIxp/M42BKGlq48/FxEJFpF7ReRZ4ArgL6r6TF0VxyJyv4ioiMS62yIiz4hIuoh8JiIDvNpOFJFd7muiV3ygiGxxj3lGRMSNR4vICrf9ChGxrscmLD37OAEeITkmxNepGFPvzlcg/xh4R0S6VgVE5CE3/o36TMwY0zgFBweTk5NjRXI1qkpOTg7BwcF1fep5OCsIbcF5ct7TdXViEUkCrgf2e4VHAl3d1zTgr27baOAxnFWNBgGPeRW8fwWmeh03wo3PBN5T1a7Ae5y+jrNpYtKPHCclNhR/z/lKB2OavnMOsVDVpSJSAiwTkZtwZlEPAr6uqrkNkaAxpnFJTEwkIyOD7OxsX6fS6AQHB5OYmFjXp+2lqpcDiMgcYG0dnnsW8FNgsVdsDPCSOv8DWi0ikSISDwwFVlQ9nEREVgAj3GEfrVV1tRt/CWd89DL3XEPd884DPsAejd1kpWcf57L2trybaRnOu8ybqr7nDqn4APgYuFZVi+s7MWNM4xQQEEBKSoqv02hJyqreqGq5O3rhkonIGCBTVT+tds4E4IDXdoYbO1c8o4Y4QFtVPeS+Pwy0PUsu03B6q+nQocPFfBxTz4rLKjhwrIgx/RLO39iYZuB8y7wV4qxWIUAQcB2Q5Y4vU1VtXf8pGmNMi9ZXRArc9wK0crfPex8WkXeBmp4J/DDwM5zhFQ1CVVVEahyXo6qzgdkAqampNnanEdqTfYJKha5xYb5OxZgGcb4hFjUt72aMMaaBqKrnEo4dXlNcRC4HUoCq3uNEYKOIDAIygSSv5oluLJOvhktUxT9w44k1tAc4IiLxqnrIHaaRdbGfxfhWevZxALpYgWxaCBtpb4wxLYyqblHVOFVNVtVknGERA1T1MLAEmOCuZjEEyHeHSSwHrheRKHdy3vXAcndfgYgMcb9dnMBXY5qXAFWrXUzk9LHOpglJzzqOn0BKbOj5GxvTDNTqUdPGGGNajKXAjUA6UARMAlDVYyLyC2Cd2+6Jqgl7wI+AuUArnMl5y9z4U8BCEZkM7ANuaYgPYOrepv25dIkLIzjgor/QMKZJsQLZGGNaOLcXueq9AtPP0u4F4IUa4uuBy2qI5+DMXTFNWGl5Jev35nJLap2v0GJMo2VDLIwxxhhzVp9l5HGyrIIrO8f4OhVjGowVyMYYY4w5q7V7nZE0VyRH+zgTYxqOFcjGGGOMOav1e3Pp3CaUmLAgX6diTIOxAtkYY4wxNaqsVNbvPcagFOs9Ni2LFcjGGGOMqdHOrEIKistJ7WgFsmlZrEA2xhhjTI3W7c0FsB5k0+JYgWyMMcaYGm07VEBkSACJUa18nYoxDcoKZGOMMcbU6MvsE3SKDcV9JLkxLYYVyMYYY4yp0Z6jx0mJDfN1GsY0OCuQjTHGGHOG4yXlHCkooVObUF+nYsyZSotg25v1dnp71LQxxhhjzrD9UAEA3dqG+zgTY7xkbYMv3oD1L8KJLJi+Dtp0q/PLWIFsjDHGmDNs2p8HQL+kSB9nYgxwYC2sfBp2LQfxg05D4es/qZfiGHw4xEJEPCKySUTecrdTRGSNiKSLyKsiEujGg9ztdHd/stc5HnLjO0TkBq/4CDeWLiIzG/qzGWNMUyEi94uIikisu/09EflMRLaIyMci0ter7V43vllE1nvFo0VkhYjscn9GuXERkWfce/FnIjKg4T+huVibDuSSFN2KNuH2BD3jQ/s+hnmjYM43IWMdXPsI3L8T7ngDOl5Vb5f15Rjke4BtXtu/AWapahcgF5jsxicDuW58ltsOEekF3Ar0BkYAf3GLbg/wZ2Ak0AsY77Y1xhjjRUSSgOuB/V7hL4FvqOrlwC+A2dUOG6aq/VQ11Ss2E3hPVbsC77nb4NyHu7qvacBf6/5TmPqyaX8e/ZOifJ2Gaaly98LCifDiSMjeAdc/CfducXqNw9rU++V9UiCLSCLwLeB5d1uAa4FFbpN5wE3u+zHuNu7+69z2Y4AFqlqiql8C6cAg95WuqntUtRRY4LY1xhhzulnATwGtCqjqx6qa626uBhJrcR7v+3T1+/dL6lgNRIpIfJ1kburVofyTHMovpn8HG15hGlB+Bqz9Oyy6C/40EHYuh2EPwz2fwlUzIKjhVlTx1RjkP+DclKtG/scAeapa7m5nAAnu+wTgAICqlotIvts+AefmTQ3HHKgWH1xTEiIyDadXgw4dOlzCxzHGmKZFRMYAmar66TnWuJ0MLPPaVuAdEVHgOVWt6l1uq6qH3PeHgbbu+1P3b1fVffoQplHb7I4/7t/BepBNA6ishLWz4d3HoLwYQttA6l1w9X3Qur1PUmrwAllEvg1kqeoGERna0Nf35t7cZwOkpqbqeZobY0yTIiLvAu1q2PUw8DOc4RVnO3YYToF8tVf4alXNFJE4YIWIbFfVld7Hqaq6BfSF5GmdFY3MpgN5BPr70Su+ta9TMc1ZZYUz+e6DX8OXH0LX62HEUxDdCXz8cBpf9CB/DRgtIjcCwUBr4I84X735u73IiUCm2z4TSAIyRMQfiAByvOJVvI85W9wYY1oMVR1eU1xELgdSgKre40Rgo4gMUtXDItIHZwjcSFXN8TpfpvszS0TewBnSthI4IiLxqnrIHUKR5R5yrvu0d57WWdHIbNqfy2XtWxPob49LMHVM1SmKN70MO5ZCUQ4EhMK3/wAD7/R5YVylwf/lq+pDqpqoqsk4k+zeV9XvAf8BbnabTQQWu++XuNu4+99XVXXjt7qrXKTgTAJZC6wDurqrYgS611jSAB/NGGOaBFXdoqpxqprs3oszgAFucdwBeB24Q1V3Vh0jIqEiEl71Hqf3+XN3t/d9uvr9e4K7msUQIN9rKIZppMoqKvksI9+GV5i6VXLcGV/8lyvhhevh89eh0zC4+QW4fxukTmo0xTE0rnWQHwQWiMgvgU3AHDc+B3hZRNKBYzgFL6r6hYgsBLYC5cB0Va0AEJEZwHLAA7ygql806Ccxxpim61GceR5/cXuXy90VK9oCb7gxf+Afqvq2e8xTwEIRmQzsA25x40uBG3EmURcBkxrqQ5iLt/1QISXllTZBz1yawsOw/xPYvxqyt0PmJijJh/YDYPSfoPd3IKjxPoTGpwWyqn4AfOC+34PzdV31NsXAuLMc/yTwZA3xpTg3ZmOMMefh9iJXvZ8CTKmhzR6gb/W4uy8HuK6GuALT6yxR0yA2HXAWMbEHhJgLVlkJu9+H1X+B3e85Mf9W0LYX9Pw2DJwESVf4Nsdaakw9yMYYY4zxsU3782gTHkRCZCtfp2Iau+ydsOPfzpjikkJn7eL8AxDWFoY+BF2/Ce36gCfA15leMCuQjTHGGHPKhn25DOgQyTmW/zMtTcEhOLgRigsgbx98udIZNlHkzuGN7Q4hMdC+P1z3KPS6CfwDfZvzJbIC2RhjjDEAZBUWs/9YEXcM6ejrVExDKSuGozvhyBcQEAxBrZ33B9ZAzm4ozofCg14HCMT3hR7fcnqHu98IEQlnPX1TZQWyMcYYYwDYuM8Zfzygo61g0eSVHAethODWUFEOFaVwdAcc/twpiI/udB7hnLfPaVdddCeI6wXBkRDbBZKvgZBoCIl1ztnMWYFsjDHGGMAZXhHo78dlCc2/AGqWykucccAbX4I1zwEKsd0gJ90pkKt4giCmi9MT3OcWp027Pk6b4jxnOyzOV5+iUbAC2RhjjDEArN+XS5+ECIL8Pb5OxdTGyVz4dAHsWAZHPv9qTLB44LKxTpGbvR06X+uMEW6dAImpEJUMfvZ3fC5WIBtjjDGG4rIKPs/M566rU3ydijmXijJY8zfYssgZK1xZ5gyF6DnKKYDD20GX4dC6va8zbdKsQDbGGGMMWzLzKatQBtoT9BofVdj+Fnz8rNNTXHockgbDlT+Cy8dBu8t9nWGzYwWyMcYYY9jgTtAbaBP0Go/KSqcw/u/v4eAmZ2xw/9udHuKu3/R1ds2aFcjGGGOMYf3eXFJiQ4kJC/J1Kgbg6C5YdBcc/gyiUuDbf4D+d4DHSreGYH/KxhhjTAunqmzcn8u1PVr2ygWNwsk8WPk7WDcHAlrBd/8Ovb9rhXEDsz9tY4wxpoX78ugJjp0oteEVvnYyD+ZcDzm7oE8aXPcYtI73dVYtkhXIxhhjTAtXNf441Qpk38nPhNenwrHdcMcb0GmorzNq0fx8nYAxxhjfEZH7RURFJNbdHioi+SKy2X096tV2hIjsEJF0EZnpFU8RkTVu/FURCXTjQe52urs/uaE/n6mdDftyaR3sT+c2Yb5OpWXa8Tb8ZQhkboDvPGfFcSNgBbIxxrRQIpIEXA/sr7Zrlar2c19PuG09wJ+BkUAvYLyI9HLb/waYpapdgFxgshufDOS68VluO9MIrd17jAEdo/DzE1+n0rJUVsIHv4F/pkF0CvxoNVx+s6+zMliBbIwxLdks4KeA1qLtICBdVfeoaimwABgjIgJcCyxy280DbnLfj3G3cfdf57Y3jcjOI4XsyT7B0G5tfJ1Ky1JcAK/eDh/8CvrcCnctd4pk0yhYgWyMMS2QiIwBMlX10xp2Xykin4rIMhHp7cYSgANebTLcWAyQp6rl1eKnHePuz3fbV89lmoisF5H12dnZl/rRzAX692eH8BO4sY9NBmsw2Tvh79fCzrdhxG/gO39zVqwwjYZN0jPGmGZKRN4F2tWw62HgZzjDK6rbCHRU1eMiciPwf0DX+ssSVHU2MBsgNTW1Nr3Zpg59sDObfkmRxIUH+zqVluHTV+Hf94N/EExcAslX+zojUwMrkI0xpplS1eE1xUXkciAF+NQd8ZAIbBSRQap62Ov4pSLyF3cCXyaQ5HWaRDeWA0SKiL/bS1wVx+uYDBHxByLc9qaRyDlewmcZedx7XTdfp9L8ZW6Aj/4IWxdDhyud9Y0jk85/nPEJK5CNMaaFUdUtwKknQojIXiBVVY+KSDvgiKqqiAzCGYqXA+QBXUUkBafwvRW4zW33H+BmnHHJE4HF7qmXuNufuPvfV1XrIW5EFm8+iCpc37utr1NpvspL4Z1HYO1zEBgGwx6Ba34Mfh5fZ2bOwQrki1FeAvkZIH7OP3DxA/F4vfc7c99p2zZHxRjTaN0M/FBEyoGTwK1uUVsuIjOA5YAHeEFVv3CPeRBYICK/BDYBc9z4HOBlEUkHjuEU1aYReW1jBn0SI+gZ39rXqfx/9u48vqrqXPz/5znnJDmZJwKEJMyToMikONQBpYoT9l61KfZWah064LfSfm3FW3+31tav9l5bq623t161DtfWIrYVvSpa51oVGaKAAzIJAUJCSEIgZDp5fn/slXAISQjJyTkZnvfrdV5n77WHZ+0MO0/WWXut/qm6BJYsgO3vwqxvw+x/haB9rfsCS5C7onwT/PbUbpxADk+YCUuYD0ueu1MeVqxHLHRct2Mo7mBDB/8ItBejo38cjvWY/hBDICEFEjMhOQdShkDqEO89ZSikDoXMEZCQ2kE8Y45OVUeGLf8G+E07+z0PPN9G+Wa8US5al9cCV0SsoiaiSqpqWb9zHzfPnRjrqvRPn/8Dnvo61FXDZQ/Z8G19jCXIXZE2DP7pAdAQNIVAm7xlbfLGNDxsPXy7trF/KOzEYQlse59CHlbe3v6tykVoScw6St7a/eSzM3Xp7jExjNGVY7oU4xg3aJN3Yz1YAbvXw6bXoK7qyP2SBkHmSO+VMxFyp8CQ472fU/u0whjTjpc+8rqbnzNx8FH2NMds7VL4yzchYwR87a8wZNLRjzG9iiXIXZGYAScWxroWZiCqr4EDpbC/FPbtgIrPoWILVGyF4hWwbumhfeNTYNA4yB4Hg8Z7y4PGQ9ZoiLOn1Y0ZyJqalN+/vZUp+emMH2Kz50XM3s3w/kPwzm9gxBfgKw5l5AoAACAASURBVE94OYPpcyxBNqYviU+C+JFea3Fb6qqhZB3sXgflG2HPBtj2Dqxdcmgf8UHGcJc0jz88iU4eZK3OxgwARcWVbNlzgF9++URs7pYI+XAJ/PXb0NQIJ86Hi39ljRF9mCXIxvQnCakw4lTvFa7+gNd3fs8G2PPZofctb0Jj7aH9ghlhrc3jIHOU19c5ZTAkZUNCmiXQxvQDr3y8G79POHeijV7RbXs+g1d/Bh/91Ws1/ucHID3v6MeZXs0SZGMGgvhkr29y7pTDy5uaoGq7d4MvD0ucN/4Nip448jy+ACRmeQ8OJmZAMN1LyuMSIS4Zkty2hhpAvPJA8PB3f7w3QH7LewIE4g+9B4Less8m+jSmJxysD7FkZTGnjckmPSku1tXpuza/ASse8GbDi0uCL3wfzrrZWo37CUuQjRnIfD5vJIzMETCu1ZwSByuhctuhPs815e61F2orve37d3tdORpqvVbqth4i7HLdAmFJc6KXXMcneYl4QorXxzohxSXeSV73kOQc76HF5GyvNTwx00vibbxRYwBQVX7017WUVddx/5XTY12dvqexDko/hqI/eOMapwyBk6/3kuOUnFjXzkSQJcjGmLYlZhz7wyWhBqit8pJZ8BLnxoNh7we9PzChOm/w/MPe6yBU3/Z787H1NdBwwBvZo3Ib1O33uojU7/f2bZN4444mZnqt36luiLzUYe4910uuk7K9BNtaf0wP0KYmag8eoLZmPwA+fwCf34/f7/eWfX6aQo2EQo2EQiGaGhsIhRrRUAjx+wnEJeAPBPD5fIh4n67UHTxA7UHvfIlJqSQkpZCQkIi4T1+aGhuprz9Ifd1BghIiPj6BFz8pZ9nqz7nxnImcPCorNl+Mvqah1psF77OXYNUjXgMBAiddC+f97ND9zvQrUU+QRaQAeAwYgje+1QOqeq+IZAF/AkYCW4Evq2qFeE8P3AtcCNQAX1fV1e5cC4Bb3al/pqqPuvIZwCNAIt6YnTfa7E3GRIE/zks2m8UnRyeuqpckHyiDA+VQs8dr4a6t9JLpg+69ptxLrLe9Cwf3tn2uYDqkF0B6/qFXWr7XDzs5x7VSZ1mrdC/RFApRvns7e7Z/yr4tq5GSDwnW7iYQqsOvjYAiKKJN3jvqlakevg4ITUgHfyp8hPBrE34a8RPCTwjB+0PWhK/5DDQSoFEC+LWJBOpI0DqC1JMoSiRTqbbO1aRCLXEECBEnIYJA+L98FwAbg6D/EPhHR2ePwJ9M8YEv7vChRn1+170q6HWx8vm9f5K1yVv2BdzvVjvj/at6w6SG6t0/zK47V3g3rZaYreoi4ZN5NZ9Lvdiod/+KSzr0u167z+uCtnON+wdc4LiLYfI/Q8Es62fcz8WiBbkR+L+qulpEUoFVIvIy8HXgFVW9S0QWA4vxZme6ABjnXrOA3wKzXEL9Y2Am3m/yKhFZpqoVbp/rgPfwEuS5wAtRvEZjTDSJeH2hE1K9Yew6o6EW9pd4M13VlMOBPV53kurd3hB6ldu9RLq28shjv/hTOP27kb0G0yXvPbiIU3c9RvOH2xWkURYYSoMvSL0vCRWfl/6KoPjcu0vY2loOT+ZaUfGjvkDLu5dwiRsX3SVa2oQ0NeJrqkclQFMgiAYS0UAQElKR5tbG5nH0m0Jo87j44kfCkkTx+b0YGkJDjdDU0JLUKSBxQXzxSQA01deg9Qeg4SDScBD1x6P+BC8JDQSpbfJx4GAtEqrnzNFppMU1tXudLY76QG5H2924/811bilrcp8M1XqfDjU1enUUn9u/0XuFn6c1X8BLguOTvIRW1Ttn8ydSTQ2tqnLoe+O93HrzzLbN3/OmRu8f7bINsPVt75/l1KEw65sw/DQYfor3z7EZEKKeIKvqLmCXW64WkY+BPOBS4Gy326PA63gJ8qXAY64F+F0RyRCRXLfvy6q6F8Al2XNF5HUgTVXfdeWPAV/CEmRjTLi44KEJVjpSVw37drrW6TIvkS6YFY0amk7InH4p720pIJgziqHjpjN42Cgy7QFPY0w3xbQPsoiMBKbhtfQOcckzQAleFwzwkuftYYcVu7KOyovbKG8r/vXA9QDDhw/v+oUYY/qvhFTImeC9TK8z8aQ5cNKco+9ojDHHIGb/ZotICvA0sEhV94Vvc63FPd5nWFUfUNWZqjozJ8eePjXGGGOMMTFKkEUkDi85fkJV/+yKd7uuE7j3Ule+AygIOzzflXVUnt9GuTHGGGOMMUcV9QTZjUrxEPCxqv4ybNMyYIFbXgA8E1Z+lXhOAapcV4zlwHkikikimcB5wHK3bZ+InOJiXRV2LmOMMWFE5P+KiIrIILf+AxEpcq91IhJyD0UjIltFZK3btjLsHFki8rKIfObeM125iMh9IrJRRD4UERt41xjTJ8SiBfl04GvAOWE34QuBu4AvishnwBy3Dt4oFJuBjcB/A98BcA/n/RR4371ub35gz+3zoDtmE/aAnjHGHMENu3kesK25TFX/Q1WnqupU4BbgjbB7K8Bst31mWNlivFGIxgGvuHU4fBSi6/FGGDLGmF4vFqNY/J32x4Y5t439FVjYzrkeBh5uo3wlcHw3qmmMMQPBPcAPaf9TtvnAHztxnmMahSjsgWxjjOmVbCY9Z9WqVXtE5PNjPGwQsKcn6tPLY1t8i2/xjy3+iJ6qSFeJyKXADlX9QNoY71ZEkvDGkL8hrFiBl0REgd+p6gOu/FhHITosQQ4fUQjYLyKfHuPl9LWfB4tv8ftL/L547Z26H1uC7KjqMQ9jISIrW33MGDWxjG3xLb7Fj238zhKRvwFD29j0I+Bf8bpXtOcS4O1W3Su+oKo7RGQw8LKIfKKqb4YfpKrqEuhOc4n2A0fdsR2x/n5YfIs/UOP352u3BNkYY/opVW1zgGAROQEYBTS3HucDq0XkZFUtcbt9hVbdK1R1h3svFZG/ACcDb+JGIVLVXZ0chcgYY3o1m27IGGMGGFVdq6qDVXWkqo7E6/owvTk5FpF04CzC+iaLSLKIpDYv47U+r3Obj3UUImOM6dWsBbl7uvyRYB+PbfEtvsXv3/4JeElVD4SVDQH+4lqcA8AfVPVFt+0uYImIXAN8DnzZlT8PXIg3olANcHUP1TfW3w+Lb/EHavx+e+3iPVxsjDHGGGOMAetiYYwxxhhjzGEsQTbGGGOMMSaMJchdICJzReRTN33q4qMf0eU4R0zr2pNTuorIwyJSKiLrwsqOOZ6ILHD7fyYiC9qKdQzxbxORHa1mXWzedouL/6mInB9WfszfHxEpEJHXROQjEVkvIjdG8/o7iB+t6w+KyAoR+cDF/4krHyUi77lz/UlE4l15glvf6LaPPFq9uhj/ERHZEnb9U115xH/+3LF+EVkjIs9F8/pN13Xl572LcQbM/bid2FG5F7nj7H48wO/H0hvuxapqr2N4AX686atHA/HAB8CkHoq1FRjUquzfgcVueTHwc7d8Id6U2gKcArzXhXhnAtOBdV2NB2ThTQ2eBWS65cxuxL8NuKmNfSe5r30C3nBVm9z3pkvfHyAX7yl+gFRgg4sRlevvIH60rl+AFLccB7znrmsJ8BVX/l/At93yd4D/cstfAf7UUb26Ef8R4PI29o/4z587/vvAH4Dn3HpUrt9eXXt19ee9i7G2MkDux+3Evo0o3IvcOe1+PMDvx/SCe7G1IB+7k4GNqrpZVeuBJ/GmU42WS/GmcsW9fyms/DH1vAtkiDceaaepN+D/3lbFxxrvfOBlVd2rqhXAy3izcXU1fnsuBZ5U1TpV3YL3lPzJdPH7o6q7VHW1W64GPsab8Ssq199B/Ghdv6rqfrca514KnAMsbef6m78uS4FzRUQ6qFdX47cn4j9/IpIPXAQ86NaFKF2/6TK7H3ccr6v3o5jdi118ux8P4Ptxb7kXW4J87NqbOrUnNE/rukq8qVjh2Kd07a5jjdcT9bjBfWzzcPNHaj0Z331EMw3vv+aoX3+r+BCl63cfaRXhTfLwMt5/3JWq2tjGuVriuO1VQHYk46tq8/Xf4a7/HhFJaB2/VZzufP1/BfwQaHLr2UTx+k2X2P2443iRrkdU78Vg92MG5v24V9yLLUHu3b6gqtOBC4CFInJm+EZVVTr+ry6ioh3P+S0wBpgK7AJ+0ZPBRCQFeBpYpKr7wrdF4/rbiB+161fVkKpOxZvt7GRgYk/F6kx8ETkeuMXV4yS8j+lu7onYInIxUKqqq3ri/KZfGOj346jei8HuxwPxftyb7sWWIB+7qE2dqmHTugLN07rubv6oTqIzpeuxxotoPVR1t/tFbQL+m0MfkUQ8vojE4d0Mn1DVP7viqF1/W/Gjef3NVLUSeA04Fe+jsuYJhcLP1RLHbU8HyiMcf677qFNVtQ74PT13/acD80RkK97HoOcA9xKD6zfHxO7HHceLWD2ifS+y+7FnAN6Pe8+9WI/xwYGB/sKbQWozXqfv5k73k3sgTjKQGrb8D7y+O//B4Q8p/LtbvojDO8mv6GLckRz+YMYxxcP7r3ILXof8TLec1Y34uWHL38PrUwQwmcM74G/GeyCiS98fdx2PAb9qVR6V6+8gfrSuPwfIcMuJwFvAxcBTHP5gxHfc8kIOfzBiSUf16kb83LCvz6+Au3ry58+d42wOPRgSleu3V9deXf1570KcAXc/biN2VO5F7px2P7b7McT4XhzzG1xffOE9sbkBr0/Qj3ooxmj3zf0AWN8cB69vzSvAZ8Dfmn/Y3A/m/a5Oa4GZXYj5R7yPjRrw+utc05V4wDfwOsRvBK7uZvzH3fk/BJZx+A3qRy7+p8AF3fn+AF/A+7juQ6DIvS6M1vV3ED9a1z8FWOPirAP+LezncIW7lqeABFcedOsb3fbRR6tXF+O/6q5/HfA/HHqyOuI/f2HHn82hm3JUrt9eXX915ee9CzEG1P24ndhRuRe54+x+bPdjiPG92KaaNsYYY4wxJoz1QTbGGGOMMSaMJcjGGGOMMcaEsQTZGGOMMcaYMJYgG2OMMcYYE8YSZGOMMcYYY8JYgmxMKyLyIxFZ76bTLBKRWSKySESSYl03Y4wZKOxebGLJhnkzJoyInAr8EjhbVetEZBDeAO//wBvbcU9MK2iMMQOA3YtNrFkLsjGHywX2qDeVJu4mfDkwDHhNRF4DEJHzROQdEVktIk+JSIor3yoi/y4ia0VkhYiMjdWFGGNMH2b3YhNTliAbc7iXgAIR2SAi/ykiZ6nqfcBOYLaqznYtGbcCc1R1OrAS+H7YOapU9QTgN3jTcRpjjDk2di82MRWIdQWM6U1Udb+IzADOAGYDfxKRxa12OwWYBLwtIuB97PdO2PY/hr3f07M1NsaY/sfuxSbWLEE2phVVDQGvA6+LyFpgQatdBHhZVee3d4p2lo0xxnSS3YtNLFkXC2PCiMgEERkXVjQV+ByoBlJd2bvA6c192kQkWUTGhx1TGPYe3pphjDGmE+xebGLNWpCNOVwK8GsRyQAagY3A9cB84EUR2en6vn0d+KOIJLjjbgU2uOVMEfkQqHPHGWOMOTZ2LzYxZcO8GRNBIrIVG4LIGGNiyu7Fprusi4UxxhhjjDFhrAXZGGOMMcaYMNaCbIwxxhhjTBhLkI0xxhhjjAljCbIxxhhjjDFhLEE2xhhjjDEmjCXIxhhjjDHGhLEE2RhjjDHGmDCWIBtjjDHGGBPGEmRjjDHGGGPCWIJsjDHGGGNMGEuQjTHGGGOMCWMJsjHGGGOMMWEsQTbGGGOMMSaMJciOiLwY6zoYY/onu790nn2tjDE9qbP3mEBPV6SvSEtLO3/mzJka63oYY/qlfbGuQF9h92JjTA/r1P3YEmRn3LhxrFy5MtbVMMb0QyLyWazr0FfYvdgY05M6ez+2LhbGGGOMMcaEsQTZGGOMMcaYMJYgG2OMMcYYE8b6IBtjzAAgIhnAg8DxgALfAD4F/gSMBLYCX1bVChER4F7gQqAG+LqqrnbnWQDc6k77M1V91JXPAB4BEoHngRtVVUUkq60YPXu1xvR/DQ0NFBcXU1tbG+uq9ErBYJD8/Hzi4uK6dLwlyF2wfW8NNz/9IT4RfD7BJ+APX/YJIuKVCa7crfvwjhNx+4EgLecWOTxW+Gr4Nmm1o7S7gven8NAbqoceEFdtc9d2th1e0Hr74dsO3zhmcApnjx/M8Oyk9g8yxvSke4EXVfVyEYkHkoB/BV5R1btEZDGwGLgZuAAY516zgN8Cs1yy+2NgJt7tYpWILHMJ72+B64D38BLkucAL7pxtxTADwFuflfHw37dQeNJw5h4/NNbV6VeKi4tJTU1l5MiRR+QEA52qUl5eTnFxMaNGjerSOWKSIMeqJSNS9VeFhlAToSYlpN43ItSkNCk0NSlNqoRUUcWVqyvHlR++f8t5j4jT9raOEte2El7hUHLdnIwflmy3ihv+i3bEr1yHCXyrpN2thpqU6tpGYD0njczkp186nolD01qf2RjTQ0QkHTgT+DqAqtYD9SJyKXC22+1R4HW85PVS4DF333xXRDJEJNft+7Kq7nXnfRmYKyKvA2mq+q4rfwz4El6C3F4M0481NSnXP76Kv328G4D3t1Zw3yufcfmMfBLifFx4fC6ZyfExrmXfVltba8lxO0SE7OxsysrKunyOWLUgx6olIyKGZyfx1LdOi9TpBoQtew7wt492819vbOKCe99iwakjWXzBRIJx/lhXzZiBYBRQBvxeRE4EVgE3AkNUdZfbpwQY4pbzgO1hxxe7so7Ki9sop4MYLUTkeuB6gOHDh3fh8kxv0tSk3PLntfzt492cNT6Hb545ml+8vIHKmnpuf+4jAH70l3V87ZQRfPfcceSkJsS4xn2XJcft6+7XJuoP6YW1ZDwEXkuGqlbitTI86nZ7FK/1AcJaMlzrRHNLxvm4lgyXFDe3ZOTiWjJc68djYecyMTJqUDLXnTmal753Jv8yawSP/GMrl/z673xYXBnrqhkzEASA6cBvVXUacACvEaKFu1/26AQd7cVQ1QdUdaaqzszJyenJKpgoeHdLOX9auZ35JxfwyNUncdrYQTz97dNYdsMXuG/+NOafXADA4+9+zsNvb4lxbU1vdvbZZzNhwgSmTp3K1KlTKS0tBaCuro7CwkLGjh3LrFmz2Lp1a8Rjx2IUi/CWjDUi8qCIJBOdlozDiMj1IrJSRFZ2pxnedF52SgI//dLxPPqNk9lX28Dlv32Hv320O9bVMqa/KwaKVfU9t74UL2He7RoVcO+lbvsOoCDs+HxX1lF5fhvldBDD9FP/++EuEuP8/NvFkw9rxUtOCDDvxGHcfunxLF90Jl+cNIQ/rtjGlj0HYlhb09s98cQTFBUVUVRUxODBgwF46KGHyMzMZOPGjXzve9/j5psj32srFglyr2jJcHGs1SJGzhqfw/JFZ3Jcbirf+p9VvLhu19EPMsZ0iaqWANtFZIIrOhf4CFgGLHBlC4Bn3PIy4CrxnAJUuQaM5cB5IpIpIpnAecByt22fiJzinhu5qtW52oph+qH9dY0sK9rJ+ZOHkBjfdhe6OL+PCUNTuXnuRHwifOvxVdQ1hqJcU9Ndixcv5v77729Zv+2227j77rujEvuZZ55hwQLvtnL55ZfzyiuvHDE4QHfFog9yWy0Zi3GtDKq66xhaMs5uVf46HbdkmF4kIymex6+dxdcfXsHCP6zhnkJl3onDYl0tY/qr/wM84Z772AxcjddIskRErgE+B77s9n0e78HojXgPR18NoKp7ReSnwPtuv9ubH9gDvsOhh6Nf4NBzH3e1E8P0M42hJhY+sZrquka+fvrRRw4YOziFn182heseW8mzH+zi8hn5Rz3GtO0nz67no537InrOScPS+PElk9vdXlhYyKJFi1i4cCEAS5YsYfny5Ufsd8YZZ1BdXX1E+d13382cOXOOWo+rr74av9/PZZddxq233oqIsGPHDgoKvNQwEAiQnp5OeXk5gwYN6uzlHVXUE2RVLRGR7SIyQVU/5VBLxkd4rQt3cWRLxg0i8iTeQ3pVLoleDvw/14oBXkvGLe4Gvs+1eryH15Lx66hdoDkmacE4HrtmFt/4/fssenINjaEm/nm63SSNiTRVLcJ7qLm1c9vYV4GF7ZznYeDhNspX4o1M1Lq8vK0Ypv95enUxb2wo498unsTUgoxOHTPnuMGMzE5iycrtliD3MdOmTaO0tJSdO3dSVlZGZmZmS9Ia7q233upyjCeeeIK8vDyqq6u57LLLePzxx7nqqqu6U+1Oi9UoFrFqyTC9UEpCgEe+cRLXPrqSm576gISAn4um5Ma6WsYYY47BH1ZsZ1JuGlefPrLTx4gIV8ws4D+Wf8qWPQcYNSi55yrYj3XU0tuTrrjiCpYuXUpJSQmFhYVt7tPZFuRQKMSMGTMAmDdvHrfffjt5ed4jZKmpqVx55ZWsWLGCq666iry8PLZv305+fj6NjY1UVVWRnZ0d0WuLSYIcq5YM03slxQd4cMFMrnpoBTc+uYakeD+zJw6OdbWMMcZ0Qml1LR9sr+Sm88Yf8/Bal8/I5xcvfcqSldu5ee7EHqqh6QmFhYVcd9117NmzhzfeeKPNfTrbguz3+ykqKmpZb2xspLKykkGDBtHQ0MBzzz3XklDPmzePRx99lFNPPZWlS5dyzjnnRHzIu1g8pGdMm5LiAzx89UlMdA/uvbOpPNZVMsYY0wlPvLsNgPMmH/tseUPSgsyeMJinVxXTGGqKdNVMD5o8eTLV1dXk5eWRmxvZT37r6uo4//zzmTJlClOnTiUvL4/rrrsOgGuuuYby8nLGjh3LL3/5S+66666Ixgabatr0MmnBOB77xiwKf/cO1z76Po9dM4sZIzKPfqAxxpiYqG9s4uG3tzB38lDGD0nt0jkum5HPK5+U8v7WCk4dE9mPyk3PWrt2bY+cNzk5mVWrVrW5LRgM8tRTT/VI3GbWgmx6nazkeJ64dhY5qQl89cF3Wb2tItZVMsYY046Vn++luraRf5re5pQDnXLGuEEEfMIbG2xOAtM7WIJseqXBaUGWfvs0BqUk8H/+sIaqgw2xrpIxxpg2vLGhjDi/8IWxXR9iKzUYx8yRmZYgm17DEmTTaw1KSeDX86exe18tC59YbQPJG2NML1S0rZLJw9JJTuher82zxg/m41372L2vNkI1M6brLEE2vdq04Znc+c8n8PeNe7jxj0X2AIcxxvQioSZl3Y4qpuSnd/tcZ0/wZrS1VmTTG1iCbHq9K2YW8P9dPIkX15dwy5/X0tTU47OQG2OM6YSNpfs5UB9iSn7nJgbpyMShqQxJS+CNTy1BNrFno1iYPuGaL4yi6mAD973yGWmJcdx60XERH/PQGGPMsXlpfQkAp4/t/sgTIsJZ43N4cV0JjaEmAn5rwzOxYwmy6TO+N2cc+w428NDft7Btbw0/mTeZYRmJPRqz6mADr36ym/L99WzfW0N9qIkhaUHqGpuI8/sQIBjnJys5jsGpQYamBxk7OIU4u7EbYwaAF9aVMHNEJrnpkbkXnz1hMEtWFvNBcSUzRmRF5Jymb6qpqeGKK65g06ZN+P1+Lrnkkpbxjh955BF+8IMftMy0d8MNN3DttddGNL4lyKbPEBH+7eJJDE0Pct8rn/GVB97lkatPYnROSsRiVNc2sLF0P3v21/PsBztZvr6Eukav33NqMEC830f5gXoCPqGxna4eCQEfeZmJDEpJYMKQVKYWZHDSyCyGZydFrJ7GGBNrew/U89Guffzg/AkRO+fpYwfh9wlvfFpmCbLhpptuYvbs2dTX13PuuefywgsvcMEFFwDeLH6/+c1veiy2JcimT/H5hG+dNYaTR2Wx4KEVnHfPm/zLKSM4bUw2p48d1KmnqA/Wh6hrDJGeGMeOyoP8/bM9LF9fwuptlYcNJ5cWDFB4UgH/NC2PkdnJZCTFISI0hJoI+ARVUKC2IcTeA/WU7a9j+94a1hZXsauqlt37avnz6mIef/dzAKYNz+CcCYM597ghHJebal1EjDF92oot3mynp4yOXCKbnhjHzBGZLPtgJ4vmjMfns/tkb7V48WIKCgpYuHAhALfddhspKSncdNNNETl/UlISs2fPBiA+Pp7p06dTXFwckXN3hiXIpk+aPjyTV286m1+89CmPvbOVR/6xlYykOBacOpLTxmSTk5rAJyXVbNtbQ8WBevYeqKeipp6y/fWs21FFqFXrb15GIheekEt+ZiLjBqeQkhBg+ohMgnH+I2I3d59ozm+TEwIkJwQoyEpi+vBMLp16aLD8UJOyqWw/r31SyrMf7uQXL2/gFy9vICc1gXGDUzhtTDYXTRnGqEHJPfa1MsaYnrB6WyXxfh8n5HX/Ab1wV84azo1PFvH2pj2cMS4noufut15YDCURntFu6AlwQftTOBcWFrJo0aKWBHnJkiUsX778iP3OOOMMqqurjyi/++67mTNnTqeqUllZybPPPsuNN97YUvb000/z5ptvMn78eO655x4KCgo6da7OsgTZ9Fk5qQncddkUfnTRcazdUcXDf9/Cva98xr2vfHbYfvEBH9nJ8WQmxZOZHMe1XxhFdko8+2sbyUiK54xxgxg7OKVHWnT9PmH8kFTGD0nlm2eNYc/+OpavL2HNtko+3rWPu1/awN0vbWDysDROHzuIE/LSOWlkFkPTgxGvizHGRNInJdWMHZxCfCCyz1ycP3ko8X4fb24oswS5F5s2bRqlpaXs3LmTsrIyMjMz20xS33rrrW7FaWxsZP78+Xz3u99l9OjRAFxyySXMnz+fhIQEfve737FgwQJeffXVbsVpzRJk0+elBuM4bcwgThsziJ2VB1m3o4oD9Y0Mz0pm4tBUkuL9vaY7w6CUBL46awRfnTUCgJKqWv5atINXPy7l929voSGk+ARmTxjMlbOGM314ZkvXDmOM6U0+LdnH6WO6Pntee4JxfqaPyOAfm8ojfu5+q4OW3p50xRVXsHTpUkpKSigsLGxzn862IIdCIWbMmAHAvHnzoROQwwAAIABJREFUuP322wG4/vrrGTduHIsWLWrZNzv70Kgp1157LT/84Q8jcj3hLEE2/cqwjMQeH9kikoamB/nWWWP41lljqG9sYsPual5cV8KT72/nlU9WAl5L+fThGUwfnsm04ZmckJdOYvyRXT+MMSZa9h6oZ/e+OiYMTe2R8582ZhD3/G0DlTX1ZCTF90gM032FhYVcd9117NmzhzfeeKPNfTrbguz3+ykqKjqs7NZbb6WqqooHH3zwsPJdu3aRm5sLwLJlyzjuuOO6UPuOWYJsTC8RH/BxfF46x+elc+Occfx94x42le5n3Y4qVm+rZPn63QAEfMJxuWlMa0maMxielWStzKZDIrIVqAZCQKOqzhSRLOBPwEhgK/BlVa0Q74fpXuBCoAb4uqqududZANzqTvszVX3Ulc8AHgESgeeBG1VV24vRw5dreljRdu9bGIkJQtpy2phsfvkyvLt5L3OPH9ojMUz3TZ48merqavLy8loS1kgpLi7mjjvuYOLEiUyfPh04NJzbfffdx7JlywgEAmRlZfHII49ENDZYgmxMrxTn9zF7wmBmTxjcUrZnfx1F2ypZs72C1Z9XsnRVMY+9442QMSglgfMmD+G4oamMHZzK+CEpZKckxKr6pvearap7wtYXA6+o6l0istit3wxcAIxzr1nAb4FZLtn9MTATbxCXVSKyzCW8vwWuA97DS5DnAi90EMP0Yas+r8DvE04s6P4U022Zkp9BUryfNz8rswS5l1u7NsIPBzr5+fmotj2c6p133smdd97ZI3GbWYJsTB8xKCWBOZOGMGfSEMAbIWPD7mrWbKvkjQ2lPLNmB3+oD7XsX5CVyNSCTKYVZDBteAaThqWRELCuGeYwlwJnu+VHgdfxktdLgcfU++v0rohkiEiu2/dlVd0LICIvA3NF5HUgTVXfdeWPAV/CS5Dbi2H6sNWfVzIpN42k+J5JI+IDPs6bNIRnP9jJrRcd12NxjGmP/cQZ00f5XVeL43LTuHLWcFSVkn21bCzdz8e79lG0vZKVW/fy7Ac7W/bPTQ+Sl5HIqEHJTBiaysjsZHIzguSmJ5IWDFg3jf5NgZdERIHfqeoDwBBV3eW2lwBD3HIesD3s2GJX1lF5cRvldBCjhYhcD1wPMHz48C5dnIkeVeWjXfu48ITIfqTe2vyTh/PXop28/NHuw4bPNCYaLEE2pp8QEXLTE8lNTzxsaKRdVQcp2lbJup1VFFccZEfFQZav9x4EDJcc7yc3I5Hc9CDD0hPJzTj0npueyLCMoLXi9G1fUNUdIjIYeFlEPgnf6PoLt/15ZoS0F8Ml6w8AzJw5s0frYLqvZF8tVQcbmJTbMw/oNTtpZBZD0hJ4fu0uS5BN1NlfO2P6udz0RHJPSOSCsNYeVaWsuo7tFTXsrKxlV9XBlvddVbV8vKuaPfvrjjhXemKcl0C7RHpoWtBNlOInKT5ATmoCuelBhqQF25xkxcSOqu5w76Ui8hfgZGC3iOSq6i7XhaLU7b4DCB/QNN+V7eBQd4nm8tddeX4b+9NBDNNHfbxrHwATc9N6NI7PJ3xx0hD+vHoHoSbFb7PqHUFV7ZO/drTXf7mzLEE2ZgASEQanBRmcFmTGiLb3qWsMsbuqriVp3ll1kF1hyfSabRVU1DS0fTCQlRzP0LQgwzKCDE0PMiglgfiAj3i/jzj3ig/4SIr3kxTvJyUhQFL8oWQ7OcFPYpw3hrWq0tikLbMYmmMjIsmAT1Wr3fJ5wO3AMmABcJd7f8Ydsgy4QUSexHtIr8oluMuB/ycimW6/84BbVHWviOwTkVPwHtK7Cvh12LnaimH6qE9KvDFte2qIt3AzR2TxP+9uY8Puao7r4YS8rwkGg5SXl5OdnW1JciuqSnl5OcFg1yfdikmCHKvhhqJyccb0EwkBP8OzkxiendTuPnWNIQ7WhzhQH+JAXSNl1XXsqqqlxCXVJVW17KysZdXnHSfT7RGBpDg/9aEmGkJKUryfjMQ40hLjACg/UM+BukbSE+MYkhYkKzmeYJyPYMBPQpyfg/WNVB1sID0xjoykeLKT4xmenURuujdWdpO7LSTHB0gNBogL+KhtCFHbEKKusYm0YBwjspP6Q2I+BPiL+yMaAP6gqi+KyPvAEhG5Bvgc+LLb/3m8e+5GvPvu1QAuEf4p8L7b7/bmB/aA73DovvuCe4GXGLcVw/RRn5ZUk5eRSFowrsdjTRvuDSO3ZlulJcit5OfnU1xcTFlZWayr0isFg0Hy8/OPvmM7YtmCHIvhhowxEZQQ8JMQ8JPhcujxQ9pvUQo1KQ2hJvfylusamqhpaORAXYia+rD3+hA1dY0tiXec32tp3newgcqDDVQdbEAVphZkkJIQoPJgA7v31VJaXUttQ5NLcpsIxvnISIpjY9l+Kg80UF3XeMzXGPAJ+ZmJpAS9Fu7s5Hi+PLOA2RMHH/3gXkJVNwMntlFeDpzbRrkCC9s518PAw22UrwSO72wM03d9WlLN+CEpUYk1PCuJrOR4irZXcOUse4AzXFxcHKNGjYp1Nfqt3tTFIhrDDRljYsTvE/w+f0z7Jtc2hNi2t4aSqlp8Ivh8gMKB+hD76xqob2wiGOcl/QlxPioO1LO57ABbyw9Q45L1jaX7qaipj9k1GBNLDaEmNpXt5+wJ0fkHUUSYWpDBmm2VUYlnTLNYJcixGm7oMDa0kDEDSzDOz/ghqR22dBtj2rdlzwEaQsrEKPQ/bjatIINXPyllX21DVLp1GAMQq451X1DV6XjdJxaKyJnhG11rcY/3GVbVB1R1pqrOzMnJOfoBxhhjzADW/IBeNP/JnDTM63u8sXR/1GIaE5MEOXy4IeCw4YYAjmG4ofbK2xtuyBhjjDFd9GnJPvw+Yczg5KjFzMv0HqrdVVkbtZjGRD1BFpFkEUltXsYbJmgdh4YCgiOHG7pKPKfghhsClgPniUimG3LoPGC527ZPRE5xI2BchQ0rZIwxxnTbRzv3MSYnOarT1g/L8BLkHZU1UYtpTCz6IMdyuCFjjDHGdEFTk7Lq8woumtKzU0y3lhaMIzUhwE5rQTZRFPUEOZbDDRljjDGmazaUVrOvtpGZI7KiHntYRiI7Kg9GPa4ZuPr86PfGGGOM6XnNU0yfWJAe9djDMoLstATZRJElyMYYY4w5qm3lBxGB/Mz2Z9fsKXmZ1oJsossSZGOMMcYc1ba9NQxJDcZksp9hGYlU1jRwoAuzYRrTFZYgG2OMMeaotlfUMDwr+q3HAHluJItdVdaKbKLDEmRjjDHGHNX2vTUUxChBPjTUm41kYaLDEmRjjDHGdKiuMUTJvloKshJjEr85Qd6+18ZCNtFhCbIxxhhjOrSj4iCqxKyLRW5akEEp8azYsvfoOxsTAZYgG2OMMaZD21zLbay6WPh8wtkTBvP6p6U0hppiUgczsFiCbIwxxpgONXdtiFULMsApo7PZV9vI59bNwkSBJcjGGGOM6dDn5TXEB3zkpCTErA6jc5IB2LrnQMzqYAYOS5CNMWYAEBG/iKwRkefc+igReU9ENorIn0Qk3pUnuPWNbvvIsHPc4so/FZHzw8rnurKNIrI4rLzNGKbvef/zCo4flobPJzGrw+hBXoK8xRJkEwWWIBtjzMBwI/Bx2PrPgXtUdSxQAVzjyq8BKlz5PW4/RGQS8BVgMjAX+E+XdPuB+4ELgEnAfLdvRzFMH1JZU8/a4krOGJcT03pkJMWTkRTHZkuQTRRYgmyMMf2ciOQDFwEPunUBzgGWul0eBb7kli9167jt57r9LwWeVNU6Vd0CbAROdq+NqrpZVeuBJ4FLjxLD9CEfFFfRpF4f4FgbNziFD4srY10NMwBYgmyMMf3fr4AfAs2P/2cDlaraPG9vMZDnlvOA7QBue5Xbv6W81THtlXcU4zAicr2IrBSRlWVlZV29RtNDPttdDcCEoakxrgl8cdIQ1u3Yx7Zye1DP9CxLkI0xph8TkYuBUlVdFeu6tEdVH1DVmao6Mycnth/jmyNtKttPVnI8Wcmx70L+xUlDAXh7054Y18T0d4FYV8AYY0yPOh2YJyIXAkEgDbgXyBCRgGvhzQd2uP13AAVAsYgEgHSgPKy8WfgxbZWXdxDD9CGf7d7P2JyUWFcDgBFZScQHfPagnulx1oJsjDH9mKreoqr5qjoS7yG7V1X1q8BrwOVutwXAM255mVvHbX9VVdWVf8WNcjEKGAesAN4HxrkRK+JdjGXumPZimD6iIdTER7v2MTE39t0rwJswZFR2MpvL9se6KqafswTZGGMGppuB74vIRrz+wg+58oeAbFf+fWAxgKquB5YAHwEvAgtVNeRah28AluONkrHE7dtRDNNHrN1RRU19qFc8oNdsdE6yjWRhepx1sTDGmAFCVV8HXnfLm/FGoGi9Ty1wRTvH3wHc0Ub588DzbZS3GcP0HSu27AVg1qisGNfkkFGDknn5o900hJqI81s7n+kZliAbY45JQ0MDxcXF1NbWxroqvU4wGCQ/P5+4uLhYV8WYiNhQUk1uepDsGM6g19ronBQam5TiioOMcpOHGBNpliAbY45JcXExqampjBw5Em+oWwOgqpSXl1NcXMyoUaNiXR1jImLTngOM6SUP6DVrToo3l+23BNn0GPtswhhzTGpra8nOzrbkuBURITs721rWTb+hqmwu3c/onN6VhI7JsSmnTc+LWYLspihdIyLPufVRIvKeiGwUkT+5p6FxT0z/yZW/JyIjw85xiyv/VETODyuf68o2isjiaF+bMf2dJcdts6+L6U9Kq+uormtkdC9rpc1IiiczKY6Pdu6LdVVMP9atBFlEpojId0Tk2yIy5RgPvxHviedmPwfuUdWxQAVwjSu/Bqhw5fe4/RCRSXjDCU0G5gL/6ZJuP3A/cAEwCZjv9jXGmE45++yzmTBhAlOnTmXq1KmUlpYCUFdXR2FhIWPHjmXWrFls3bo1thU1pge9v9V7QO/EgowY1+RIc4/P5dkPd7Kr6mCsq2L6qS4nyCLyI+CPeFOH5gN/EJFbOnlsPnAR8KBbF+AcYKnb5VHgS275UreO236u2/9S4ElVrVPVLcBGvKelTwY2qupmVa0HnnT7GmNMpz3xxBMUFRVRVFTE4MGDAXjooYfIzMxk48aNfO973+Pmm2+OaR1FJLMLjRPGdMo/NpWTkhDghLz0WFflCAtOG0FDSHlnU3msq2L6qe60IF8FnKSqP1LVH+Elpl/v5LG/An4INLn1bKDSjacJUIyXeOPetwO47VVu/5byVse0V34EEbleRFaKyMqysrJOVt0YE0uLFy/m/vvvb1m/7bbbuPvuu6MS+5lnnmHBAm8Ojcsvv5xXXnkFbz6M6BGR10UkTUSygNXAf4vIL6NaCTMgrNpawYwRmQR64VBqY3NSiPf7+LSkOtZVMf1Ud0ax2NXq+IAr65CIXAyUquoqETm7G/G7TVUfAB4AmDlzZnT/yhnTD/zk2fUR7wc4aVgaP75kcrvbCwsLWbRoEQsXLgRgyZIlLF++/Ij9zjjjDKqrj/zjeffddzNnzpyj1uPqq6/G7/dz2WWXceuttyIi7Nixg4ICb1blQCBAeno65eXlDBo0qLOXFwnpqrpPRK4FHlPVH4vIh9GsgOn/ahtCbCzbzxcnDYl1VdoU8PsYOziFTyxBNj2kOwnyXmC9iCwHFDgPeL+5JUNVv9/OcacD80TkQiAIpAH3AhkiEnCtxPnADrf/DqAAKBaRAJAOlIeVNws/pr1yY0wfN23aNEpLS9m5cydlZWVkZma2JK3h3nrrrS7HeOKJJ8jLy6O6uprLLruMxx9/nKuuuqo71Y6kgIjkAl8GfhTrypj+6bPd+wk1KZOHpcW6Ku2aODSVf1gXC9NDupMg/697NXu3Mwep6i3ALQCuBfkmVf2qiDwFXI7XZ3gB8Iw7ZJlbf8dtf1VVVUSW4fV7/iUwDBgHrAAEGCcio/AS468AV3bjOo0x7eiopbcnXXHFFSxdupSSkhIKCwvb3KezLcihUIgZM2YAMG/ePG6//Xby8rxeWampqVx55ZWsWLGCq666iry8PLZv305+fj6NjY1UVVWRnR31KXhvx5vW+e+q+r6IjAY+i3YlTP+2fmcV4H2i01tNGJrKn9fsoLKmnoyk+FhXx/QzXU6QVfWhSFYEuBl4UkR+BqwBms//EPC4iGzEa7X+iou/XkSWAB8BjcBCVQ0BiMgNeH9A/MDDqro+wnU1xsRQYWEh1113HXv27OGNN95oc5/OtiD7/X6Kiopa1hsbG6msrGTQoEE0NDTw3HPPtSTU8+bN49FHH+XUU09l6dKlnHPOOVEf2k1VnwKeClvfDFwW1UqYfu+jXftISQhQkJkU66q0a8LQVAA+KanmlNFR/0fV9HNdTpBFZC7wU2CEO48AqqqdnrBdVV8HXnfLm/Ee9Gu9Ty1wRTvH3wHc0Ub588Dzna2HMaZvmTx5MtXV1eTl5ZGbmxvRc9fV1XH++efT0NBAKBRizpw5XHfddQBcc801fO1rX2Ps2LFkZWXx5JNPRjR2Z4jIvwM/Aw4CLwJTgO+p6v9EvTKm3/po5z4m5abh8/Xesb0nDvVatz+1BNn0gO50sfgNXh+4tRwajcIYY6Ji7dq1PXLe5ORkVq1a1ea2YDDIU0891ea2KDpPVX8oIv8EbAX+GXgTsATZRERTk/Lxrn1cMfPIvv29yZC0BIamBXn1k1IWnDYy1tUx/Ux3xm4pBopUtUFVQ82vSFXMGGNMm5obNi4CnlLVqlhWxvQ/63ZWcaA+xNReOEFIOBFh/snDeWNDGdv31sS6Oqaf6U6C/EPgWRH5gYh8t/kVqYoZY4xp03Mi8gkwA3hFRHKA2hjXyfQjr31ShgicOT4n1lU5quZh6FZvq4hxTUx/050E+SdACMgAcsJexhhjeoiqLgZOA2aqagNQg80WaiLoH5v2cPywdLKSe//IEOOGpJAQ8LG22D5IMZHVnT7IBap6fMRqYowx5qhEJAn4DjAcuB5vmMsJwHOxrJfpH5qalHU7qrhsRn6sq9IpcX4fk4alWQuyibjutCAvF5FzIlYTY4wxnfF7oB6vFRm88d5/1tEBIhIUkRUi8oGIrBeRn7jyUSLynohsFJE/iUi8K09w6xvd9pFh57rFlX8qIueHlc91ZRtFZHFYeZsxTO+0ec9+DtSHOCEvPdZV6bRzJw5m9bZKm3baRFR3EuRvAH8Tkf0isldEKkRkb6QqZowxpk1jVPXfgQYAVa3BG2azI3XAOap6IjAVmCsipwA/B+5R1bFABXCN2/8aoMKV3+P2Q0Qm4Y1FPxmYC/yniPhFxA/cD1wATALmu33pIIbphd7b4v0Znza8dz+gF+6rs0bg9wnPfbgz1lUx/Uh3EuRBQBze1M85bt36IBtj+rSamhouuugiJk6cyOTJk1m8uKUxlEceeYScnBymTp3K1KlTefDBB2NRxXoRSQQUQETG4CXA7VLPfrca514KnAMsdeWPAl9yy5e6ddz2c8WbEeVS4ElVrVPVLcBGvPHrTwY2qupmVa3HmxH1UndMezFML/TiuhJGZicxJicl1lXptMzkeMYNTuED64dsIqjLCbIb0u0K4Ga3nIvXMmGMMX3aTTfdxCeffMKaNWt4++23eeGFF1q2FRYWUlRURFFREddee20sqvdjvAlCCkTkCeAVvFGFOuRaeouAUuBlYBNQqaqNbpdiIM8t5wHbAdz2KiA7vLzVMe2VZ3cQI7xu14vIShFZWVZWdrRLMT3kYH2IdzaVc/7koVGfIbK7puSns7a4ElWNdVVMP9HlBFlEfgPMBr7mimqA/4pEpYwxpj2LFy/m/vvvb1m/7bbbuPvuuyN2/qSkJGbPng1AfHw806dPp7i4OGLn7w7XIvsJ3uQgXwf+iDeaxetHO9aNVT8VyMdr8Z3YczU9Nqr6gKrOVNWZOTn2QWSsfFhcSWOTctLITk+I22tMyc+goqaB4oqDsa6K6Se6M4rFaao6XUTWAKjqXnv4wpgB5oXFUBLhGe2GngAX3NXu5sLCQhYtWsTChQsBWLJkCcuXLz9ivzPOOIPq6iMf2rn77ruZM2dOp6pSWVnJs88+y4033thS9vTTT/Pmm28yfvx47rnnHgoKojfbmKqqiDyvqicA/9vFc1SKyGvAqUCGiARcC28+3gN/uPcCoFhEAnhd6crDypuFH9NWeXkHMUwvs3pbJdC3+h83OzHfq/MHxZUUZCXFuDamP+hOgtwgIj4O9YPLxqacNsb0sGnTplFaWsrOnTspKysjMzOzzST1rbfe6lacxsZG5s+fz3e/+11Gjx4NwCWXXML8+fNJSEjgd7/7HQsWLODVV1/tVpwuWC0iJ6nq+509wE0m0uCS40Tgi3gPz70GXI7XZ3gB8Iw7ZJlbf8dtf9Ul58uAP4jIL/GGlxsHrMB7SHCciIzCS4C/AlzpjmkvhullVm+rYGR2EtkpCbGuyjGbMDSVeL+PD4uruHjKsFhXx/QDx5wgh7UE3A88DeS4IYO+jDd5iDFmoOigpbcnXXHFFSxdupSSkhIKCwvb3KezLcihUIgZM2YAMG/ePG6//XYArr/+esaNG8eiRYta9s3Ozm5Zvvbaa/nhD4/a9bcnzAL+RUS2AgfwklNV1SkdHJMLPOpGm/ABS1T1ORH5CHhSRH4GrAEecvs/BDwuIhuBvXgJL6q6XkSWAB8BjcBC9wwKInIDsBzwAw+r6np3rpvbiWF6EVVlzbYKzhzXN7u4xAd8HJ+Xxt8+3s0Pzp9AnL87YxAY07UW5BXAdFV9TERWAXPwbtBXqOq6iNbOGGPaUFhYyHXXXceePXt444032tynsy3Ifr+foqKiw8puvfVWqqqqjhilYteuXeTm5gKwbNkyjjvuuC7UvtvOP/ouh1PVD4FpbZRvxuuP3Lq8Fu8h7LbOdQdwRxvlzwPPdzaG6V227z3Inv31TBuRGeuqdNk3zxrDNx9fxQvrSph3orUim+7pSoLc8mirayFY38G+xhgTcZMnT6a6upq8vLyWhDVSiouLueOOO5g4cSLTp08H4IYbbuDaa6/lvvvuY9myZQQCAbKysnjkkUciGrsjIhIEvgWMBdYCD4WNDmFMt7z0UQkAp43JPsqevdec44aQmhDg3c3lliCbbutKgpwjIt9vb6Oq/rIb9THGmE5ZuzbCDwc6+fn57Q4Vdeedd3LnnXf2SNxOeBRvcpC3ODQhx40dHmFMJz37wU6Oz0vrU+Mft+b3CdNHZLJyq81ZZrqvK510/EAKkNrOyxhjTORNUtV/UdXf4T30dkasK2T6h7LqOj4oruKC4yP7aUwszByRyYbd+6mqaYh1VUwf15UW5F2qenvEa2KMMaYjLX/xVbWxr03kYHqvNzd4k7OcNb5vPqAXbqYbw3n1tgpmTxwc49qYvqwrLch2VzbGmOg7UUT2uVc1MKV5WUT2xbpypu96enUxw9KDTMpNi3VVum1qQQYBn7DCulmYbupKgnxuxGthjOlTbDrXtvXk10VV/aqa5l6pqhoIW+77mY2JiS17DvCPTeX8y6kj8Pn6fvtXYryfU8dks+T97eyvs2dYTdcdc4KsqvZvmTEDWDAYpLy83JLkVlSV8vJygsFgrKtiTKe98vFugH416sON546j/EA9y9eVxLoqpg/rzkx6xpgBKD8/n+LiYsrKymJdlV4nGAySn58f62oY0ymqygvrShg/JIX8zP4zPfP04ZlkJcfz9qY9XDbDfh9N10Q9QXZjeb4JJLj4S1X1x26K0ieBbGAV8DVVrReRBOAxYAZQDhSq6lZ3rluAa4AQ8F1VXe7K5wL34o248aCqxma6L2P6obi4OEaNGhXrahhjuumtz/aw6vMKbrtkUqyrElE+n3DqmGze+LSMypp6MpLiY10l0wfFYi7GOuAcVT0RmArMFZFTgJ8D96jqWKACL/HFvVe48nvcfojIJLzpTycDc4H/FBG/m0r1fg6NEzrf7WuMMcYYZ8nK7WQnx3PlrBGxrkrEffPM0VQdbODXr26MdVVMHxX1BFk9+91qnHspcA6w1JU/CnzJLV/q1nHbzxVvfKNLgSdVtU5VtwAb8aYzPRnYqKqbVbUer1X60h6+LGOMMabPqKlv5JWPS5l7/FDiA7FoK+tZU/IzOGPcoJY+1sYcq5j8VriW3iKgFHgZ2ARUhk2bWgzkueU8YDt4Y38CVXjdMFrKWx3TXnlb9bheRFaKyErrT2mMMWagePWTUg42hLh4Sv95OK+12RMHs7W8hi17DsS6KqYPikmCrKohVZ0K5OO1+E6MUT0eUNWZqjozJ6fvD5BujDHGHE2oSXnsnc/JSU3g5FFZsa5Ojzl7vDdRyGuflMa4JqYviunnKqpaCbwGnApkiEjzQ4P5wA63vAMoAPj/27vz8Cire4Hj318mmez7AiFsAQOIgAgIrohSKWIVra21ti61V9qqtdYu2tZ77eZ9vO211wVraytVrHu1rW1xQcQqyiLIpqxhXxOy78lkcu4f5wSGmIQkZGay/D7P8z7vO+ddznkzkzO/Oe95z+vWJ2Nv1jua3mKfttKVUkqpfu/5D/eyalcJ3581Ck8fGPu4LUPT4xiZGc9b2s1CdUHIA2QRyRSRFLccC1wMbMYGyl9wm90A/N0tv+pe49a/bewArK8C14hItBsBIw9YBXwI5IlIroh4sTfyvRr8M1NKKaV6tv2lNTz01nYmDU3h6ilDTrxDL3flGTl8sKOYNXtKw10U1cuEowU5G1gqIhuwwexiY8w/gbuAO0UkH9vH+Am3/RNAuku/E7gbwBjzCfAisAl4HbjVdd1oBG4D3sAG3i+6bZVSSql+q9HfxG3PrqXW5+fnc8dh73fv2246L5ekmEieWbEn3EVRvUzIx0E2xmwAzmglfSe2P3LL9Drgi20c6z7gvlbSFwGLTrqwSimlVB9QWFHHF3+/nD3FNdx35TjG5SSHu0ghEeeN5LOnDeT1jw/J8KB2AAAgAElEQVRT5/MTE+UJd5FUL9H3xnZRSil1HBEZIiJLRWSTiHwiIt9x6WkislhEtrt5qksXEXlYRPJFZIOITAo41g1u++0ickNA+mQR2ej2edgNx9lmHio0Ghqb+ON7O/ncI8s4UlnPb64+nWunDg13sULq0gnZVNY38t72onAXRfUiGiArpVTf1wh8zxgzFjgLuNU9QOluYIkxJg9Y4l6DfdBSnpvmAY+BDXaBe4Fp2Ct+9wYEvI8BNwfsN9ult5WHCrJ3thZyxs/f5Jf/2kxMlIc/Xj+Fz08a3C+6VgQ695QMUuKi+OeGg+EuiupFQt7FQimlVGgZYw4Bh9xypYhsxo4PPxeY4TZ7CngHez/IXGChuyF6hYikiEi223axMaYEQEQWY5+G+g6QZIxZ4dIXYh/29Fo7eaggaPQ38dbmQv7w3k7W7Cll1IAEbr3wFC4/fVC/C4ybRXkiuGRcNn9fd4CahkbivBr6qBPTT4lSSvUjIjIcex/ISmCAC54BDgMD3HJnH8SU45ZbptNOHqobGWNYt6+M3yzexnvbi0iMieTm83O5fWYeiTFR4S5e2M2dOIjnVu3lxQ/3ceO5ueEujuoFNEBWSql+QkQSgJeBO4wxFYEtisYYIyImmPm3lYeIzMN25WDo0P7VP/ZkVdc38uamwyxcvoe1e8uIjBD+63Nj+cKUwSRpYHzU1OFpnJ+Xwf2vb+HyiTmkxXvDXSTVw2kfZKWU6gdEJAobHD9jjHnFJRe4rhO4efMjxzr7IKYDbrllent5HKVPNe28Op+f37y5lQt+/Q7ffWE9B0pruWXGSJZ+f4Yb2kyD40AREcI9l46lztfEy2v2n3gH1e9pgKyUUn2cG1HiCWCzMeY3AasCH8TU8gFN17vRLM4Cyl03iTeAWSKS6m7OmwW84dZViMhZLq/raf1hT4F5qC6o8/l5evlu5s5/n4ffzmdgcjQPXTORd394IT+cPYYhaXHhLmKPNXpgIlOHp/HEsl3U+fzhLo7q4bSLhVJK9X3nAtcBG0VknUv7MXA/8KKIfB3YA1zt1i0C5gD5QA3wNQBjTImI/AL7kCeAnzffsAfcAjwJxGJvznvNpbeVh+qEqvpGnlmxh/99cys+vyErMZo/XD+Fi8dql+7OuOMzeVz7x5V8/6X1zL920ol3UP2WBshKKdXHGWOWAW0NYTCzle0NcGsbx1oALGglfTUwrpX04tbyUCdW2+Bn+c4i/rr2IK9/fAif3zBjdCbXTh3KxWMH9NtRKU7GOadk8J2ZeTy0ZDv/cX4ZE4ekhLtIqofSAFkppZTqQQor6nhjUwG/XZrPofI64r0eLh47gLkTc5ilgfFJu3n6CBYs28UTy3bxyJc/9WBfpQANkJVSSqmwq23ws2ZPKQ8s3sq6fWUYA3lZCTx0zUQuGpOlQ7V1o4ToSK6ZOoQnlu3iiomDmHmqdlNRn6YBslJKKRUmu4uqeWDxNt7ddoTyWh9ZidF87ZxcLp2QzYTByUR59F76YLh5+giWbCnk9ufW8sHdM0mO0x8g6ngaICullFIh4vM3selgBW9uOsySzYVsOVxJnNfDOSMzmD1uILNOG6BDtIVAVmIMj147iUseeo+nV+zmtovywl0k1cNogKyUUkoFkTGGfSW1LN5cwDMr97DzSDUAIzPj+cb0Edx0Xi4DkmLCXMr+59TsJGaMzuRP7+/my1OHkp4QHe4idU2THwo+gZgk8HihrhyMgdLdMGAspA4Pdwl7JQ2QlVJKqW7W6G9iV1E1K3YW89TyPeQXVgEwPD2O/75yPBeMziQnJTbMpVTf/cworv79cuY9vYa/fPPsnn0DZJMfdr0L1UVweL0NhMv2wp4PwN/Q9n5ZYyF7IuROhxEzICk7VCXu1TRAVkoppU6SMYZtBVVsOVzBxv3lvPzRfkprfACMyIznrtljmHlqFqdkJhAR0YODsH7m9CEp/GLuOH748gb+seEQl58+KNxF+rSCT2DZg5C/GGpLbZonGuLSwBsPk2+EnMngqwF/I0TFQmSMbVEu2g5bF8GWf8H6Z+2+A8fDqNmQNwsGnwk9+UdBGGmArJRSSnWSMYYjlfWs3FXC0q2F/HPDIRoam46unzkmi+mjMjl7ZDojMxPwaFDcY101eTB/+mA3P3llIwnRHi4a00NGtSjKhyU/hc3/AG8ijL0cTvkMZORBxmiI9J74GKM+C+fcZluf962Efatg62vw7q/tlDMFpv/AbqeB8nHEjgevpkyZYlavXh3uYiil+iARWWOMmRLucvQGPbkuLqyoY8P+cv669gDr9pVxoKwWgJioCOaensOEIcmMGZjIuJxkoiM9YS6t6oyPD5TzjafXUF7rY/Gd08lODmP3F18tLJ8P7/6vbSk+61sw7Ru2xbi7lB+Aba/BsoegfC8MGA/Tvw+nXg4RfXvklI7WxxogOz25UlZK9W4aIHdcT6qLjTF8uLuUVz7az/s7ithXYgPilLgopgxLY/KwVCYNTeHUQUk68kQfsLe4hlkP/psJg1OYf+0ZZCWG4cZJXx08dw3sXApjPgeXPgCJA4OXn98HG1+C9x6A4nzIGAXnfw/GfQE8fbOTgQbIndSTKmWlVN+iAXLHhbsuPlJZf3QItvfzi6hvbCLe62FcTjJnj0znzOFpTBqaSqxXW4j7omdX7uWev21kfE4yL3/rHCJDOQ51bSm89DUbHM99FM74aujybvLDpr/Buw9A4SeQmgvn3wnjr4aovjXCSkfr477580AppZQ6AWMMRVUNbNhfxqKNh/lwdwl7S2oAGJoWx6UTspk6PI25E3M0IO4nrp02lMSYSL793FoefGs735s1KjQjWxTvgGevhtI9oQ+OASI8MO4qGHul7Xrx9i/h1W/D2/fBBT+AKV/vd32UNUBWSinV5/mbDLuKqth8qJI1e0rZfKiCHUeqKKqyw2PFez2cPTKdL505hMnDUpmWm9azh/xSQXPZ6YN4c1MB85fmU1xdz39fOT64n4XqYhsc15TADa/CsHOCl9eJRETAmEth9Bzbkv3uA/Cv78HHr8B5d8IpM/tNoBzyAFlEhgALgQGAAR43xjwkImnAC8BwYDdwtTGmVOyn8iFgDlAD3GiM+cgd6wbgHnfoXxpjnnLpk4EngVhgEfAdo31JlFKqz/M3GTbsL2N3cTV7imtYtcu2Cu8vrT26TWyUh9yMeM4ZmcGY7EQmDk5h3OBk7UesjnrwSxPJTo7h8Xd3sn5fOT+9/DSm5nbjTXLNSnfD01dCxUG47m8w7Ozuz6MrRGDkRZA7A5Y/Aisfh2eusuMoX7UA4tPDXMDgC3kfZBHJBrKNMR+JSCKwBrgCuBEoMcbcLyJ3A6nGmLtEZA7wbWyAPA14yBgzzQXUq4Ep2EB7DTDZBdWrgNuBldgA+WFjzGvtlSvc/d6UUn2X9kHuuM7WxdsKKtl8qIJNByvYsL+cbQWVFFcfe2jCmIGJjMxKIDc9nmHpceRmxHPG0FQddk2dkDGG37+7k8fe2YExhtfumN69D3dpqIEnL4WSHfCVv8CQqd137O7mq4PVC+Ctn0J0Apz5H7ZFuRf2T+6xfZCNMYeAQ265UkQ2AznAXGCG2+wp4B3gLpe+0LUArxCRFBdkzwAWG2NKAERkMTBbRN4BkowxK1z6QmwA3m6ArJRSqvf5x/qDPPJ2Pl5PBCMy45kxOovpozLIy0okNyNe+w6rLhMRvnnBSC4ZN5A5D73HFY++z2+/Mokzh3dDS3JVoe1WcXAdfPHJnh0cgw2Ez77FtnC/cz/8+39g41/g8kdg+LnhLl1QhLUPsogMB87AtvQOcMEzwGFsFwywwfO+gN32u7T20ve3kq6UUqqPue7sYVx2+iCGpMZpMKyCYlh6PAu/Po3vv7SeGxas4v6rJnDZhOyu90tuqLGjVRRugWuehTFzurfAwTToDLj2BdjxNvzjDtsCPul6mPGjPvcI67CNBi0iCcDLwB3GmIrAda61OOh9P0RknoisFpHVR44cCXZ2SikVFiKyQEQKReTjgLQ0EVksItvdPNWli4g8LCL5IrJBRCYF7HOD2367uwekOX2yiGx0+zzs7h1pM4/ulJUYw6gBiRocq6CaPCyVF+adxZDUOG5/bi2/+OdmutRFtboYnpwDe96Hyx7sXcFxoJEXwS3LYeo8WP8c/P58yH8r3KXqVmEJkEUkChscP2OMecUlF7iuE839lAtd+gFgSMDug11ae+mDW0n/FGPM48aYKcaYKZmZmSd3Ukop1XM9CcxukXY3sMQYkwcsca8BLgHy3DQPeAxssAvci70XZCpwb0DA+xhwc8B+s0+Qh1K9TlZSDIu+cz43njOcBe/v4opH32efGxawQ8oPwAtfhYJNtuX49GuCV9hQ8MbDnF/BN5dBTAr8+Sp48Qb7I6APCHmA7FoWngA2G2N+E7DqVaC5ReIG4O8B6de7Vo2zgHLXFeMNYJaIpLpKehbwhltXISJnubyuDziWUkr1O8aYd4GSFslzsfd74OZXBKQvNNYKoPm+j8/i7vswxpQCzfd9ZOPu+3BX/xa2OFZreSjVK3kihHsvG8t/fW4sO4uquWz+Mu7520Yq63zt73hgDcw/087nzu+9LcetyRxtg+SL7oEt/4KHJ9ruF5UF4S7ZSQlHC/K5wHXARSKyzk1zgPuBi0VkO/AZ9xrsKBQ7gXzgD8AtAO7mvF8AH7rp58037Llt/uj22YHeoKeUUi2F4r6PtvJQqtcSEW46L5dXvnUOk4am8szKvXzxd8t585PDrXe7OLQenv8qxKbCbatgwtWhL3SwRcXA9B/AN9+z4yivewYemQzLH7WPs+6FwjGKxTKgrZ7tM1vZ3gC3tnGsBcCCVtJXA+NOophKKdVvGGOMiAT1vo/28hCRedjuHAwdOjSYxVCq2+QNSGTBjWfy721H+PazHzHv6TVcOiGbKybmMGN0JlGeCDi4Fv40xwbH1z4PqcPDXezgyjoVrvydDZZfuwve+DF8tBAu+RWMuCDcpeuUsN2kp5RSKqxCcd9HW3kcR+8HUb3ZBaMyWfOfF3P7zDwWbTzEzQtXc/n891n62l8wf74KYtPg5rdh4PhwFzV00kfCV16CLz8PvlpYeDm88g37tMBeQgNkpZTqn0Jx30dbeSjVp0R5Irjz4lGsv3cWD35pIuNrVnLeinnsrYtj4aiHKfME4Sl8PZ0IjL4Ebl1lW5Q3vgSPTIIVj0Fjw4n3D7OQP0mvp9In6SmlgiXcT9ITkeewD1fKAAqwo1H8DXgRGArsAa42xpS4IHc+diSKGuBrrtsaInIT8GN32PuMMX9y6VOwI2XEYu/5+LbrUpHeWh7tlVXrYtXr7fkA8/TnKY/P5ZbIn/HBAdsHd2x2EtefPYyZpw4gMzE6zIUMg8Mb4c17YOc7kJoLn7kXxl5hA+kQ6mh9rAGyo5WyUipYwh0g9yZaF6teq6EGlvwMVv8JUobCTa9DfAYb95ezZEsBr647yM6iamKiIrhswiCunTaUM4Z2+9DgPZsxkL8EFv8nFG6CwVNh1i9h6LSQFUED5E7SSlkpFSwaIHec1sWqV6otg799C7a+BpOugwt/AokDj9vE32TYcriCJ5btYvEnBVTWN3L64GQuHJPFVZMGMyQtLkyFD4Mmvx3p4u37oOownHqZ/ZtlnRr0rDVA7iStlJVSwaIBcsdpXax6ne1vwcs3QX0lzL4fpn3jhLtU1Tfy+L93sCy/iLX7yjAGzhyeyvicFC47PZtTs5OIieoHT4dsqLZDwS17EHzVMPhMOOM6+xCVyOB0Q9EAuZO0UlZKBYsGyB2ndbHqNSoL4LUfwKa/Q9ZYuPL3kD2h04c5UFbLXz/az6vrD7K7uIaGxiYiI4SZp2YxcUgqnz1tANnJsX37cerVRbD+eVj7ZziyGdJG2gePjJ0LEd173hogd1KnKuXKAlg+HyTCvnESAeJxy2KXW66TCIiIaPHazQOHhf5UZ/X21tHGupbbufe4I+91q3m0kW+b5elP23fXsdtIjE6042fGZ0JMcshvZlDdQwPkjtMAWfV4xtgRGRb9wA5hdv734NzbISr2pA9dWefjXxsOseVwJf/aeIgjlfWAfYLfpKEpnJ+XyficZHIz4slJjSUyQpC+9L3Q3Ef5jR9B0TZIGGiD5LNv6bYxpDVA7qROVcqFm+HxC8H4bT8a08TRIFSpYPFEQ8IASBzg5gNthdE8pQyDmKQwF1K1RgPkjtMAWfVY/kb46CnbynnwI9sdYO5vIXNU0LLcX1rD21sKOVRex7LtRWw8UH7c+oyEaKbmppKVGMOEwcmMzExgeEY8ybFRQStTSDT5Yesi2PAibHvDxltjr4Bp34QhZ57UoTVA7qSTrpSNsYFyc8Bs/C1eN7W9vr1jHnvRRnpH1klAy2N7vzRb+Sy0+floI70/bd/mv053laXJ9mmrLYWqQqgqCJgKoeIA1B1fWZKUAwMnwMBxkDEaMvIg/RSITmirsCoENEDuOA2QVY9TttcGamuegvK9to494zo48+vdfvn/hEWpaWDHkSryC6vYV1LLnpIaPj5QTkFFHTUNx+KJ9HgvkR5hYFIMIzMTGJmVQJzXQ3ZyLMPS48hJjSUpppcE0RUH4YP5sPZpqK+A874LF9xtH2/dBRogd5JWyqpXqi2F0j1QuhtKdtphcw5tgOLt7sqGk5Rjg+WMUW7Kg/Q8SBqk3TZCQAPkjtO6WIWFv9GOpuD32f6wtaX2MdH7VsCOpYCB4efDWbfYh1/0sHqz0d/ErqJqdhZVs7uoml1F1fj8hoKKOnYcqeJQed2n9slI8DJxSAoDkmIYkZlAvNfDoJRYkmKjGJYWR0pcVM/qvlFfBa/fZVvwk4fAZQ/BiAtt99VO6Gh9HNnlgiqlwi821U6DJh6f3lhvA+aibW7Kt/N1z0FD5bHtvAm2hTkwcM4YBWkjuvzrXCmlwsbfaFsZ68pty2N9hb3q1lBtXzdU2RZhXx2U77dXcysP2/mnCKTlwgU/hInXdlsf2GCI9ESQNyCRvAGJra6vaWiktsHPwbI69pbUcKCshq2Hq9h4oIxVu0qoqGv81D6JMZGkxnlp9DeRluDF64kgIyGakVk2mI6O9NBkDOkJ0QxMimFgcjQZCdHEeSPxRgbhQc3RCTD3UZjwJXj12/Dnz0N8Fnz9Tfs+dTMNkJXqiyKj7XiSLceUNMZ+GRRts63MRdvt8t7lsPHFY9tJhB3oPjXX9nVOyIK4dDvFprnAPMXeOBidCFFxIb/UqJTqI/w+8NVAXYUNaBtq7Ly6yM4b62xA21hr19WVHdu2rvzYPk0+2/LblshYG2QlZtt6a+g0iIiy93V4om1dFxFp7/GIioGcyeCND93fIYjivJHEeSNJT4hm/ODk49YZYyit8VFd38ih8jrKa33sKa5mb0kN5bU+PCKU1fqob/Szs6iaJVsK8Te13/sgMkKI9XpIi/cyKDmWpNhIhmfEMywtnvQEL8mxUcR5PXgihNQ4L2nx3o4Pa5c7Hb75Pmx73bbupwzt6p+l/XMIylGVUj2TCCRl22nEBceva6iG4h2uxdkFzmV7bFpVAfjr2z+2JxriXPDsq7FpkbH2iyZwHumFyBjweG0gf3QebdcdN2+eYu0d4lFx4I2zLd/RiXbSwFyp0PH7oKbEtsQ2VLn7JMqOLddX2mV/g922vtIGsc1Bbl2Z3V/EptWVQ9OnWy9bJR4bsMak2BuSo5NssJs5xtYLEVHuR3yKXZcwAOLTbZ0Uk6L1RRtEhLR4G6R25GEl9Y1+N28iQoTiqnoOl9dxuKKOoqoGahsaqWnwU9Pg50hVPYfKatlxpJ6lW47Q4G9q87hJMZFkJcUwICmarMQYsprnibZl2udvIjoy4mhZU067Cs/4L3Tb36ElDZCVUpY33o7h2do4nsbYALqm2E51ZfZLsa7MfgH66uwg7zXFNr15uCNfbcAXYzn4XKDd2ODm9faLtLG+jUucHRARCVHx9oswPhPiMuxyTMqxlu7mL8i4NDtsUHxmp/utKdWjNfkD/t9q3XJtwHKd/eHqc/Pm/8ujyzXHWml9LfZrqHattNX29YmIx/7w9UTZH7Mxye4HbqwdbSfbdQmLjHbr4uwP6JhkG9h6E2x9FJ9hXzfv6+klN5X1cdGRnuPmCdGRDEs/cUu7v8lwuKKO0uoGKmp91DT4aWxqoqzGR3F1AwUVdRRW1FNYWceqXSUcqaxvN6AWgdQ4L4tuP5+Byd3fJVADZKXUiYnYS5PRCZA6LDh5NPldwBwYQLt5cwDuq7Vf0s2tVc1f2PVVUFNkL8mW7oYDq22g3lard/Nl1MRse1k1PtO2PCVk2Sl5CCQPtv3bNJBWxthWzsb6Yz/mjo5OZI4f8rP5td9nL/n7G928wS77G9xr37FW1uYfiU0+l1/TseDVV+u2c9v4ffbzX1tyrCW3eZuuEM+xADUqNuBqTaxtcU0YYJdjkm3Q6k2wPzSjk2x94E1wLbaJxwLbqLgedxObCj9PhJCTEktOSsfGizbGUF7ro6CinqKqeryREdT7miiurqe0uoGS6gaKqxuCNqSdBshKqZ4hwmMvk3LiS3wd5qu1fRJry+y8pth2F6k4CJWH7FScD/tW2mCjZSt2RBQk50DSYBs4x2fa6ZSZkDOp+8qpum7PB7DnffsjqfnyfnNraFNjwHvaPNyl2D72zcNtNjUHt27eWHcs6Ay80hHyse7FBprN3YwCuyRFxdnP5MAJNiiNinHbxhwLdo8GuoHrYj8dBGurrOqhRISUOC8pcV5G0/rNh8GkAbJSqu9qDgKSBp1426YmG0RXHrJjTJfttXe5l++3rw9vgOojtqtIbIoGyD3Fzn/Dv++3fdabWzS98TaYjIi0LaQAGDcWuZuLHFsfGe2echphPy/eBBdIttJHPjLm+KegNu933JNTxQaeEVFuHum6HHjB45ab1zVvFxVj581BfGS0tsIqFUYaICulFNiuFPHpdho4ru3tGus79th2FRrnfdc+6jfSG+6SKKX6EA2QlVKqMyKjw10CFUjH61ZKBYHefaKUUkoppVQADZCVUkoFlYjMFpGtIpIvIneHuzxKKXUiGiArpZQKGhHxAI8ClwBjgS+LyNjwlkoppdqnAbJSSqlgmgrkG2N2GmMagOeBuWEuk1JKtUsDZKWUUsGUA+wLeL3fpSmlVI+lo1g4a9asKRKRPZ3cLQMoCkZ5enjemr/mr/l3Lv8gPX6wbxCRecA897JKRLZ28hC97fOg+Wv+fSX/3njuHaqPNUB2jDGZnd1HRFYbY6YEozw9OW/NX/PX/MObfy9zABgS8HqwSzvKGPM48HhXMwj3+6H5a/79Nf++fO7axUIppVQwfQjkiUiuiHiBa4BXw1wmpZRql7YgK6WUChpjTKOI3Aa8AXiABcaYT8JcLKWUapcGyCeny5cEe3nemr/mr/mrDjPGLAIWBTGLcL8fmr/m31/z77PnLsaYYB1bKaWUUkqpXkf7ICullFJKKRVAA2SllFJKKaUCaIDcBSIyW0S2iki+iNwdxHx2i8hGEVknIqtdWpqILBaR7W6e6tJFRB52ZdogIpO6kN8CESkUkY8D0jqdn4jc4LbfLiI3nGT+PxWRA+5vsE5E5gSs+5HLf6uIfDYgvdPvj4gMEZGlIrJJRD4Rke+E8vzbyT9U5x8jIqtEZL3L/2cuPVdEVrpjveBGIUBEot3rfLd++InK1cX8nxSRXQHnP9Gld/vnz+3rEZG1IvLPUJ6/6rqufN67mE+/qY/byDskdZHbT+vjfl4fS0+oi40xOnViwt6FvQMYAXiB9cDYIOW1G8hokfYr4G63fDfwP255DvAaIMBZwMou5DcdmAR83NX8gDRgp5unuuXUk8j/p8D3W9l2rPvbRwO57j3xdPX9AbKBSW45Edjm8gjJ+beTf6jOX4AEtxwFrHTn9SJwjUv/HfAtt3wL8Du3fA3wQnvlOon8nwS+0Mr23f75c/vfCTwL/NO9Dsn569S1qauf9y7mtZt+Uh+3kfdPCUFd5I6p9XE/r4/pAXWxtiB33lQg3xiz0xjTADwPzA1h/nOBp9zyU8AVAekLjbUCSBGR7M4c2BjzLlBykvl9FlhsjCkxxpQCi4HZJ5F/W+YCzxtj6o0xu4B87HvTpffHGHPIGPORW64ENmMfhxuS828n/1CdvzHGVLmXUW4ywEXAX9o4/+a/y1+AmSIi7ZSrq/m3pds/fyIyGLgU+KN7LYTo/FWXaX3cfn5drY/CVhe7/LU+7sf1cU+pizVA7rwcYF/A6/20/49zMgzwpoisEfsoVoABxphDbvkwMCDI5epsfsEox23uss2C5ktqwczfXaI5A/urOeTn3yJ/CNH5u0ta64BCbEW2AygzxjS2cqyj+bj15UB6d+ZvjGk+//vc+f+fiES3zL9FPifz938Q+CHQ5F6nE8LzV12i9XH7+XV3OUJaF4PWx/TP+rhH1MUaIPds5xljJgGXALeKyPTAlcYYQ/u/6rpVqPNzHgNGAhOBQ8ADwcxMRBKAl4E7jDEVgetCcf6t5B+y8zfG+I0xE7GPAp4KjAlWXh3JX0TGAT9y5TgTe5nurmDkLSKfAwqNMWuCcXzVJ/T3+jikdTFofdwf6+OeVBdrgNx5B4AhAa8Hu7RuZ4w54OaFwF+x/yQFzZfq3LwwyOXqbH7dWg5jTIH7R20C/sCxSyTdnr+IRGErw2eMMa+45JCdf2v5h/L8mxljyoClwNnYS2XNDxQKPNbRfNz6ZKC4m/Of7S51GmNMPfAngnf+5wKXi8hu7GXQi4CHCMP5q07R+rj9/LqtHKGui7Q+tvphfdxz6mLTyRsH+vuEffrgTmyn7+ZO96cFIZ94IDFg+QNs351fc/xNCr9yy5dyfCf5VV3MdzjH35jRqfywvyp3YTvkp7rltJPIPztg+bvYPkUAp3F8B/yd2BsiuvT+uPNYCDzYIj0k599O/ptJ3DIAAAMrSURBVKE6/0wgxS3HAu8BnwNe4vgbI25xy7dy/I0RL7ZXrpPIPzvg7/MgcH8wP3/uGDM4dmNISM5fp65NXf28dyGfflcft5J3SOoid0ytj7U+hjDXxWGv4HrjhL1jcxu2T9BPgpTHCPfmrgc+ac4H27dmCbAdeKv5w+Y+mI+6Mm0EpnQhz+ewl4182P46X+9KfsBN2A7x+cDXTjL/p93xNwCvcnwF9ROX/1bgkpN5f4DzsJfrNgDr3DQnVOffTv6hOv8JwFqXz8fAfwV8Dle5c3kJiHbpMe51vls/4kTl6mL+b7vz/xj4M8furO72z1/A/jM4VimH5Px16vrUlc97F/LoV/VxG3mHpC5y+2l9rPUxhLku1kdNK6WUUkopFUD7ICullFJKKRVAA2SllFJKKaUCaICslFJKKaVUAA2QlVJKKaWUCqABslJKKaWUUgE0QFaqBRH5iYh84h6nuU5EponIHSISF+6yKaVUf6F1sQonHeZNqQAicjbwG2CGMaZeRDKwA7x/gB3bsSisBVRKqX5A62IVbtqCrNTxsoEiYx+liauEvwAMApaKyFIAEZklIstF5CMReUlEElz6bhH5lYhsFJFVInJKuE5EKaV6Ma2LVVhpgKzU8d4EhojINhH5rYhcYIx5GDgIXGiMudC1ZNwDfMYYMwlYDdwZcIxyY8x4YD72cZxKKaU6R+tiFVaR4S6AUj2JMaZKRCYD5wMXAi+IyN0tNjsLGAu8LyJgL/stD1j/XMD8/4JbYqWU6nu0LlbhpgGyUi0YY/zAO8A7IrIRuKHFJgIsNsZ8ua1DtLGslFKqg7QuVuGkXSyUCiAio0UkLyBpIrAHqAQSXdoK4NzmPm0iEi8iowL2+VLAPLA1QymlVAdoXazCTVuQlTpeAvCIiKQAjUA+MA/4MvC6iBx0fd9uBJ4TkWi33z3ANrecKiIbgHq3n1JKqc7RuliFlQ7zplQ3EpHd6BBESikVVloXq5OlXSyUUkoppZQKoC3ISimllFJKBdAWZKWUUkoppQJogKyUUkoppVQADZCVUkoppZQKoAGyUkoppZRSATRAVkoppZRSKsD/A0+nZyNZyD0eAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 720x432 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cols = 2\n",
+ "rows = 2\n",
+ "fig, ax = plt.subplots(rows,cols,figsize=(10,6))\n",
+ "for i,c in enumerate(columns): \n",
+ " ind = (i//cols,i%cols)\n",
+ " for v in V:\n",
+ " data[ data.velocity == v ].plot(\n",
+ " ax=ax[ind],x=\"Step\",y=c, label = \"v = {:d}\".format(v))\n",
+ " ax[ind].set_ylabel(c)\n",
+ "fig.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Selective analyisis at fixed velocity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T16:20:53.427613Z",
+ "start_time": "2019-12-08T16:20:53.419938Z"
+ }
+ },
+ "source": [
+ "Similarly, we analyisistake a slice at fixed velocity v = -25 and compare thermodynamic \n",
+ "observables along the trajectroies of large and small cluster."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:05:14.908393Z",
+ "start_time": "2019-12-08T17:05:14.904311Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Compare along cluster size dimension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:08:29.389267Z",
+ "start_time": "2019-12-08T17:08:29.382357Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# query a specific \"trajectory\" file\n",
+ "query = { \n",
+ " \"metadata.project\": project_id,\n",
+ " \"metadata.material\": \"gold\",\n",
+ " \"metadata.type\": \"log\",\n",
+ " \"metadata.velocity\": -25}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:08:37.166516Z",
+ "start_time": "2019-12-08T17:08:37.150587Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(fp.filepad.count_documents(query) )\n",
+ "# that many documents matching query"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:08:45.967952Z",
+ "start_time": "2019-12-08T17:08:45.945552Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "files = fp.get_file_by_query(query)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:08:54.078393Z",
+ "start_time": "2019-12-08T17:08:54.048543Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "data = pd.DataFrame()\n",
+ "for content, metadata in files:\n",
+ " m = regex.findall(content.decode())[0] # takes the first thermo output block\n",
+ " \n",
+ " d = pd.read_csv(io.StringIO(m),sep='\\s+')\n",
+ " d[\"cluster_size\"] = metadata[\"metadata\"][\"cluster_size\"]\n",
+ "\n",
+ " data = data.append(d,ignore_index=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:09:02.460530Z",
+ "start_time": "2019-12-08T17:09:02.455161Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "N = data.cluster_size.unique() # unique cluster sizes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2019-12-08T17:09:12.367852Z",
+ "start_time": "2019-12-08T17:09:10.697613Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x432 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cols = 2\n",
+ "rows = 2\n",
+ "fig, ax = plt.subplots(rows,cols,figsize=(10,6))\n",
+ "for i,c in enumerate(columns): \n",
+ " ind = (i//cols,i%cols)\n",
+ " for n in N:\n",
+ " data[ data.cluster_size == n ].plot(\n",
+ " ax=ax[ind],x=\"Step\",y=c, label = \"N = {:d}\".format(n))\n",
+ " ax[ind].set_ylabel(c)\n",
+ "fig.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, storing data sets within FireWork's FilePad with proper metadata attached facilitates meaningful evaluation."
+ ]
+ }
+ ],
+ "metadata": {
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diff --git a/demos/7_filepad/README.md b/demos/7_filepad/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..0393efd7171dca6a3b21de426d8631709aa96a4c
--- /dev/null
+++ b/demos/7_filepad/README.md
@@ -0,0 +1,78 @@
+# FireWorks, FilePad and Metadata
+
+## Introduction
+
+The Jupyter notebook `PythonGoldCluster.ipynb` contains two
+[FireWorks](https://materialsproject.github.io/fireworks/) sample usage
+cases. They demonstrate meaningful use of metadata for management of workflow
+results within FireWork's *FilePad*.
+
+## Content
+
+* `PythonGoldCluster.ipynb`: a Jupyter notebook working through this demo
+ from setting up workflows to analyze results, i.e. from beginning to end
+* `A_*.yaml` to `D_*.yaml`: Static yaml descriptions of Fireworks used within
+ the first sample case.
+* `infiles/*`: static LAMMPS input scripts and data files used in both sample
+ cases. The third-party EAM potential file `Au-Grochola-JCP05.eam.alloy` by
+
+ *G. Grochola, S.P. Russo, and I.K. Snook (2005), "On fitting a gold embedded
+ atom method potential using the force matching method", The Journal of
+ Chemical Physics, 123(20), 204719. DOI: 10.1063/1.2124667.*
+
+ has been excluded from this repository. You will find it publicly available
+ for download at https://www.ctcms.nist.gov/potentials/system/Au
+ (see `infiles/NOTE`)
+* `fig/*`: figures and animations used for documentation purposes
+* `doc/PythonGoldCluster.html`: a static HTML-rendered version of the Jupyter
+ notebook. Does not include any interactive trajectory visualizations via
+ `nglview`.
+* `doc/FilePad_slides.pdf`: static PDF-rendered slides outlining this
+ demonstration's core concepts.
+* `doc/gold_droplet_impact.mp4`: An accompanying gold droplet impact animation
+ not embedded within the static PDF slides.
+
+## Requirements
+
+The Jupyter notebook has been tested with FireWorks version 1.9.5.
+Next to FireWorks, the Python packages
+* ase
+* matplotlib
+* nglview (indirectly via ASE)
+* numpy
+* pandas
+* scipy
+are used to facilitate demonstrative analysis of results.
+
+The samples have been designed to run on a custom OpenStack instance. An image
+of this instance is available on request from the author (Johannes Hörmann,
+johannes.hoermann@imtek.uni-freiburg.de) via the OpenStack service *bwCloud*
+(https://bwcloud.ruf.uni-freiburg.de). Without modifications, these samples will
+not run within the framework of this *grk2450-fireworks-tutorial*. Most
+importantly, every Firework's "_category" spec must point to a worker that
+offers a LAMMPS executable. Likewise, *ScriptTask* calls of LAMMPS must
+match this particular worker's setup.
+
+The LAMMPS version used for this demonstration is
+
+ LAMMPS (22 Aug 2018)
+
+and the executable has been built with
+
+ Active compile time flags:
+
+ -DLAMMPS_GZIP
+ -DLAMMPS_PNG
+ -DLAMMPS_JPEG
+ -DLAMMPS_FFMPEG
+
+ Installed packages:
+
+ COLLOID COMPRESS KOKKOS KSPACE MANYBODY MC MOLECULE MPIIO OPT PYTHON REPLICA
+ RIGID VORONOI USER-COLVARS USER-DIFFRACTION USER-MOLFILE USER-NETCDF USER-OMP
+
+
+## Contributors:
+
+* Johannes Hörmann (IMTEK, University of Freiburg, johannes.hoermann@imtek.uni-freiburg.de)
+* Prof. Lars Pastewka (IMTEK, University of Freiburg, lars.pastewka@imtek.uni-freiburg.de)
diff --git a/demos/7_filepad/doc/FilePad_slides.pdf b/demos/7_filepad/doc/FilePad_slides.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..f058b79726aff118a97c97e833bfc693df714356
Binary files /dev/null and b/demos/7_filepad/doc/FilePad_slides.pdf differ
diff --git a/demos/7_filepad/doc/PythonGoldCluster.html b/demos/7_filepad/doc/PythonGoldCluster.html
new file mode 100644
index 0000000000000000000000000000000000000000..502acbbff4a46a2fe14c1d390a5b056303bc96f6
--- /dev/null
+++ b/demos/7_filepad/doc/PythonGoldCluster.html
@@ -0,0 +1,14888 @@
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+ content: "\e157";
+}
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+ content: "\e158";
+}
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+ content: "\e159";
+}
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+ content: "\e160";
+}
+.glyphicon-log-in:before {
+ content: "\e161";
+}
+.glyphicon-flash:before {
+ content: "\e162";
+}
+.glyphicon-log-out:before {
+ content: "\e163";
+}
+.glyphicon-new-window:before {
+ content: "\e164";
+}
+.glyphicon-record:before {
+ content: "\e165";
+}
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+ content: "\e166";
+}
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+ content: "\e167";
+}
+.glyphicon-saved:before {
+ content: "\e168";
+}
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+ content: "\e169";
+}
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+ content: "\e170";
+}
+.glyphicon-send:before {
+ content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+ content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+ content: "\e173";
+}
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+ content: "\e174";
+}
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+ content: "\e175";
+}
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+ content: "\e176";
+}
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+ content: "\e177";
+}
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+ content: "\e178";
+}
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+ content: "\e179";
+}
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+ content: "\e180";
+}
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+ content: "\e181";
+}
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+ content: "\e182";
+}
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+ content: "\e183";
+}
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+ content: "\e184";
+}
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+ content: "\e185";
+}
+.glyphicon-sd-video:before {
+ content: "\e186";
+}
+.glyphicon-hd-video:before {
+ content: "\e187";
+}
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+ content: "\e188";
+}
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+ content: "\e189";
+}
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+ content: "\e190";
+}
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+ content: "\e191";
+}
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+ content: "\e192";
+}
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+ content: "\e193";
+}
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+ content: "\e194";
+}
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+ content: "\e195";
+}
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+ content: "\e197";
+}
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+ content: "\e198";
+}
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+ content: "\e199";
+}
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+ content: "\e200";
+}
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+ content: "\e201";
+}
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+ content: "\e202";
+}
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+ content: "\e203";
+}
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+ content: "\e204";
+}
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+ content: "\e205";
+}
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+ content: "\e206";
+}
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+ content: "\e209";
+}
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+ content: "\e210";
+}
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+ content: "\e211";
+}
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+ content: "\e212";
+}
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+ content: "\e213";
+}
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+ content: "\e214";
+}
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+ content: "\e215";
+}
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+ content: "\e216";
+}
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+ content: "\26fa";
+}
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+ content: "\e218";
+}
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+ content: "\e219";
+}
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+ content: "\f8ff";
+}
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+ content: "\e221";
+}
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+ content: "\231b";
+}
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+ content: "\e223";
+}
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+ content: "\e224";
+}
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+ content: "\e225";
+}
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+ content: "\e226";
+}
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+ content: "\e227";
+}
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+ content: "\e227";
+}
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+ content: "\e227";
+}
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+ content: "\00a5";
+}
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+ content: "\00a5";
+}
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+ content: "\20bd";
+}
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+ content: "\20bd";
+}
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+ content: "\e230";
+}
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+ content: "\e231";
+}
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+ content: "\e232";
+}
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+ content: "\e233";
+}
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+ content: "\e234";
+}
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+ content: "\e235";
+}
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+ content: "\e236";
+}
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+ content: "\e237";
+}
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+ content: "\e238";
+}
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+ content: "\e239";
+}
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+ content: "\e240";
+}
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+ content: "\e241";
+}
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+ content: "\e242";
+}
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+ content: "\e243";
+}
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+ content: "\e244";
+}
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+ content: "\e245";
+}
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+ content: "\e246";
+}
+.glyphicon-object-align-left:before {
+ content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+ content: "\e248";
+}
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+ content: "\e249";
+}
+.glyphicon-triangle-right:before {
+ content: "\e250";
+}
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+ content: "\e251";
+}
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+ content: "\e252";
+}
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+ content: "\e253";
+}
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+ content: "\e254";
+}
+.glyphicon-superscript:before {
+ content: "\e255";
+}
+.glyphicon-subscript:before {
+ content: "\e256";
+}
+.glyphicon-menu-left:before {
+ content: "\e257";
+}
+.glyphicon-menu-right:before {
+ content: "\e258";
+}
+.glyphicon-menu-down:before {
+ content: "\e259";
+}
+.glyphicon-menu-up:before {
+ content: "\e260";
+}
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+ -webkit-box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ box-sizing: border-box;
+}
+*:before,
+*:after {
+ -webkit-box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ box-sizing: border-box;
+}
+html {
+ font-size: 10px;
+ -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #000000;
+ background-color: #ffffff;
+}
+input,
+button,
+select,
+textarea {
+ font-family: inherit;
+ font-size: inherit;
+ line-height: inherit;
+}
+a {
+ color: #337ab7;
+ text-decoration: none;
+}
+a:hover,
+a:focus {
+ color: #23527c;
+ text-decoration: underline;
+}
+a:focus {
+ outline: 5px auto -webkit-focus-ring-color;
+ outline-offset: -2px;
+}
+figure {
+ margin: 0;
+}
+img {
+ vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+ display: block;
+ max-width: 100%;
+ height: auto;
+}
+.img-rounded {
+ border-radius: 3px;
+}
+.img-thumbnail {
+ padding: 4px;
+ line-height: 1.42857143;
+ background-color: #ffffff;
+ border: 1px solid #dddddd;
+ border-radius: 2px;
+ -webkit-transition: all 0.2s ease-in-out;
+ -o-transition: all 0.2s ease-in-out;
+ transition: all 0.2s ease-in-out;
+ display: inline-block;
+ max-width: 100%;
+ height: auto;
+}
+.img-circle {
+ border-radius: 50%;
+}
+hr {
+ margin-top: 18px;
+ margin-bottom: 18px;
+ border: 0;
+ border-top: 1px solid #eeeeee;
+}
+.sr-only {
+ position: absolute;
+ width: 1px;
+ height: 1px;
+ margin: -1px;
+ padding: 0;
+ overflow: hidden;
+ clip: rect(0, 0, 0, 0);
+ border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+ position: static;
+ width: auto;
+ height: auto;
+ margin: 0;
+ overflow: visible;
+ clip: auto;
+}
+[role="button"] {
+ cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+ font-family: inherit;
+ font-weight: 500;
+ line-height: 1.1;
+ color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
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+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+ font-weight: normal;
+ line-height: 1;
+ color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+ margin-top: 18px;
+ margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
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+h3 small,
+.h3 small,
+h1 .small,
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+h2 .small,
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+h3 .small,
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+ font-size: 65%;
+}
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+.h4,
+h5,
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+h6,
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+ margin-top: 9px;
+ margin-bottom: 9px;
+}
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+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+ font-size: 75%;
+}
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+.h1 {
+ font-size: 33px;
+}
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+.h2 {
+ font-size: 27px;
+}
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+ font-size: 23px;
+}
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+.h4 {
+ font-size: 17px;
+}
+h5,
+.h5 {
+ font-size: 13px;
+}
+h6,
+.h6 {
+ font-size: 12px;
+}
+p {
+ margin: 0 0 9px;
+}
+.lead {
+ margin-bottom: 18px;
+ font-size: 14px;
+ font-weight: 300;
+ line-height: 1.4;
+}
+@media (min-width: 768px) {
+ .lead {
+ font-size: 19.5px;
+ }
+}
+small,
+.small {
+ font-size: 92%;
+}
+mark,
+.mark {
+ background-color: #fcf8e3;
+ padding: .2em;
+}
+.text-left {
+ text-align: left;
+}
+.text-right {
+ text-align: right;
+}
+.text-center {
+ text-align: center;
+}
+.text-justify {
+ text-align: justify;
+}
+.text-nowrap {
+ white-space: nowrap;
+}
+.text-lowercase {
+ text-transform: lowercase;
+}
+.text-uppercase {
+ text-transform: uppercase;
+}
+.text-capitalize {
+ text-transform: capitalize;
+}
+.text-muted {
+ color: #777777;
+}
+.text-primary {
+ color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+ color: #286090;
+}
+.text-success {
+ color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+ color: #2b542c;
+}
+.text-info {
+ color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+ color: #245269;
+}
+.text-warning {
+ color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+ color: #66512c;
+}
+.text-danger {
+ color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+ color: #843534;
+}
+.bg-primary {
+ color: #fff;
+ background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+ background-color: #286090;
+}
+.bg-success {
+ background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+ background-color: #c1e2b3;
+}
+.bg-info {
+ background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+ background-color: #afd9ee;
+}
+.bg-warning {
+ background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+ background-color: #f7ecb5;
+}
+.bg-danger {
+ background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+ background-color: #e4b9b9;
+}
+.page-header {
+ padding-bottom: 8px;
+ margin: 36px 0 18px;
+ border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+ margin-top: 0;
+ margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+ margin-bottom: 0;
+}
+.list-unstyled {
+ padding-left: 0;
+ list-style: none;
+}
+.list-inline {
+ padding-left: 0;
+ list-style: none;
+ margin-left: -5px;
+}
+.list-inline > li {
+ display: inline-block;
+ padding-left: 5px;
+ padding-right: 5px;
+}
+dl {
+ margin-top: 0;
+ margin-bottom: 18px;
+}
+dt,
+dd {
+ line-height: 1.42857143;
+}
+dt {
+ font-weight: bold;
+}
+dd {
+ margin-left: 0;
+}
+@media (min-width: 541px) {
+ .dl-horizontal dt {
+ float: left;
+ width: 160px;
+ clear: left;
+ text-align: right;
+ overflow: hidden;
+ text-overflow: ellipsis;
+ white-space: nowrap;
+ }
+ .dl-horizontal dd {
+ margin-left: 180px;
+ }
+}
+abbr[title],
+abbr[data-original-title] {
+ cursor: help;
+ border-bottom: 1px dotted #777777;
+}
+.initialism {
+ font-size: 90%;
+ text-transform: uppercase;
+}
+blockquote {
+ padding: 9px 18px;
+ margin: 0 0 18px;
+ font-size: inherit;
+ border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+ margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+ display: block;
+ font-size: 80%;
+ line-height: 1.42857143;
+ color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+ content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+ padding-right: 15px;
+ padding-left: 0;
+ border-right: 5px solid #eeeeee;
+ border-left: 0;
+ text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+ content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+ content: '\00A0 \2014';
+}
+address {
+ margin-bottom: 18px;
+ font-style: normal;
+ line-height: 1.42857143;
+}
+code,
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+pre,
+samp {
+ font-family: monospace;
+}
+code {
+ padding: 2px 4px;
+ font-size: 90%;
+ color: #c7254e;
+ background-color: #f9f2f4;
+ border-radius: 2px;
+}
+kbd {
+ padding: 2px 4px;
+ font-size: 90%;
+ color: #888888;
+ background-color: transparent;
+ border-radius: 1px;
+ box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+ padding: 0;
+ font-size: 100%;
+ font-weight: bold;
+ box-shadow: none;
+}
+pre {
+ display: block;
+ padding: 8.5px;
+ margin: 0 0 9px;
+ font-size: 12px;
+ line-height: 1.42857143;
+ word-break: break-all;
+ word-wrap: break-word;
+ color: #333333;
+ background-color: #f5f5f5;
+ border: 1px solid #cccccc;
+ border-radius: 2px;
+}
+pre code {
+ padding: 0;
+ font-size: inherit;
+ color: inherit;
+ white-space: pre-wrap;
+ background-color: transparent;
+ border-radius: 0;
+}
+.pre-scrollable {
+ max-height: 340px;
+ overflow-y: scroll;
+}
+.container {
+ margin-right: auto;
+ margin-left: auto;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+@media (min-width: 768px) {
+ .container {
+ width: 768px;
+ }
+}
+@media (min-width: 992px) {
+ .container {
+ width: 940px;
+ }
+}
+@media (min-width: 1200px) {
+ .container {
+ width: 1140px;
+ }
+}
+.container-fluid {
+ margin-right: auto;
+ margin-left: auto;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+.row {
+ margin-left: 0px;
+ margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+ position: relative;
+ min-height: 1px;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+ float: left;
+}
+.col-xs-12 {
+ width: 100%;
+}
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+ width: 91.66666667%;
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+ width: 16.66666667%;
+}
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+ width: 8.33333333%;
+}
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+ right: 100%;
+}
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+ right: 91.66666667%;
+}
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+}
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+}
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+}
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+}
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+}
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+ right: 41.66666667%;
+}
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+}
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+}
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+ right: 16.66666667%;
+}
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+ right: 8.33333333%;
+}
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+ right: auto;
+}
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+ left: 100%;
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+ left: 25%;
+}
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+ left: 16.66666667%;
+}
+.col-xs-push-1 {
+ left: 8.33333333%;
+}
+.col-xs-push-0 {
+ left: auto;
+}
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+ margin-left: 100%;
+}
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+ margin-left: 91.66666667%;
+}
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+ margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+ margin-left: 75%;
+}
+.col-xs-offset-8 {
+ margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+ margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+ margin-left: 50%;
+}
+.col-xs-offset-5 {
+ margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+ margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+ margin-left: 25%;
+}
+.col-xs-offset-2 {
+ margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+ margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+ margin-left: 0%;
+}
+@media (min-width: 768px) {
+ .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+ float: left;
+ }
+ .col-sm-12 {
+ width: 100%;
+ }
+ .col-sm-11 {
+ width: 91.66666667%;
+ }
+ .col-sm-10 {
+ width: 83.33333333%;
+ }
+ .col-sm-9 {
+ width: 75%;
+ }
+ .col-sm-8 {
+ width: 66.66666667%;
+ }
+ .col-sm-7 {
+ width: 58.33333333%;
+ }
+ .col-sm-6 {
+ width: 50%;
+ }
+ .col-sm-5 {
+ width: 41.66666667%;
+ }
+ .col-sm-4 {
+ width: 33.33333333%;
+ }
+ .col-sm-3 {
+ width: 25%;
+ }
+ .col-sm-2 {
+ width: 16.66666667%;
+ }
+ .col-sm-1 {
+ width: 8.33333333%;
+ }
+ .col-sm-pull-12 {
+ right: 100%;
+ }
+ .col-sm-pull-11 {
+ right: 91.66666667%;
+ }
+ .col-sm-pull-10 {
+ right: 83.33333333%;
+ }
+ .col-sm-pull-9 {
+ right: 75%;
+ }
+ .col-sm-pull-8 {
+ right: 66.66666667%;
+ }
+ .col-sm-pull-7 {
+ right: 58.33333333%;
+ }
+ .col-sm-pull-6 {
+ right: 50%;
+ }
+ .col-sm-pull-5 {
+ right: 41.66666667%;
+ }
+ .col-sm-pull-4 {
+ right: 33.33333333%;
+ }
+ .col-sm-pull-3 {
+ right: 25%;
+ }
+ .col-sm-pull-2 {
+ right: 16.66666667%;
+ }
+ .col-sm-pull-1 {
+ right: 8.33333333%;
+ }
+ .col-sm-pull-0 {
+ right: auto;
+ }
+ .col-sm-push-12 {
+ left: 100%;
+ }
+ .col-sm-push-11 {
+ left: 91.66666667%;
+ }
+ .col-sm-push-10 {
+ left: 83.33333333%;
+ }
+ .col-sm-push-9 {
+ left: 75%;
+ }
+ .col-sm-push-8 {
+ left: 66.66666667%;
+ }
+ .col-sm-push-7 {
+ left: 58.33333333%;
+ }
+ .col-sm-push-6 {
+ left: 50%;
+ }
+ .col-sm-push-5 {
+ left: 41.66666667%;
+ }
+ .col-sm-push-4 {
+ left: 33.33333333%;
+ }
+ .col-sm-push-3 {
+ left: 25%;
+ }
+ .col-sm-push-2 {
+ left: 16.66666667%;
+ }
+ .col-sm-push-1 {
+ left: 8.33333333%;
+ }
+ .col-sm-push-0 {
+ left: auto;
+ }
+ .col-sm-offset-12 {
+ margin-left: 100%;
+ }
+ .col-sm-offset-11 {
+ margin-left: 91.66666667%;
+ }
+ .col-sm-offset-10 {
+ margin-left: 83.33333333%;
+ }
+ .col-sm-offset-9 {
+ margin-left: 75%;
+ }
+ .col-sm-offset-8 {
+ margin-left: 66.66666667%;
+ }
+ .col-sm-offset-7 {
+ margin-left: 58.33333333%;
+ }
+ .col-sm-offset-6 {
+ margin-left: 50%;
+ }
+ .col-sm-offset-5 {
+ margin-left: 41.66666667%;
+ }
+ .col-sm-offset-4 {
+ margin-left: 33.33333333%;
+ }
+ .col-sm-offset-3 {
+ margin-left: 25%;
+ }
+ .col-sm-offset-2 {
+ margin-left: 16.66666667%;
+ }
+ .col-sm-offset-1 {
+ margin-left: 8.33333333%;
+ }
+ .col-sm-offset-0 {
+ margin-left: 0%;
+ }
+}
+@media (min-width: 992px) {
+ .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+ float: left;
+ }
+ .col-md-12 {
+ width: 100%;
+ }
+ .col-md-11 {
+ width: 91.66666667%;
+ }
+ .col-md-10 {
+ width: 83.33333333%;
+ }
+ .col-md-9 {
+ width: 75%;
+ }
+ .col-md-8 {
+ width: 66.66666667%;
+ }
+ .col-md-7 {
+ width: 58.33333333%;
+ }
+ .col-md-6 {
+ width: 50%;
+ }
+ .col-md-5 {
+ width: 41.66666667%;
+ }
+ .col-md-4 {
+ width: 33.33333333%;
+ }
+ .col-md-3 {
+ width: 25%;
+ }
+ .col-md-2 {
+ width: 16.66666667%;
+ }
+ .col-md-1 {
+ width: 8.33333333%;
+ }
+ .col-md-pull-12 {
+ right: 100%;
+ }
+ .col-md-pull-11 {
+ right: 91.66666667%;
+ }
+ .col-md-pull-10 {
+ right: 83.33333333%;
+ }
+ .col-md-pull-9 {
+ right: 75%;
+ }
+ .col-md-pull-8 {
+ right: 66.66666667%;
+ }
+ .col-md-pull-7 {
+ right: 58.33333333%;
+ }
+ .col-md-pull-6 {
+ right: 50%;
+ }
+ .col-md-pull-5 {
+ right: 41.66666667%;
+ }
+ .col-md-pull-4 {
+ right: 33.33333333%;
+ }
+ .col-md-pull-3 {
+ right: 25%;
+ }
+ .col-md-pull-2 {
+ right: 16.66666667%;
+ }
+ .col-md-pull-1 {
+ right: 8.33333333%;
+ }
+ .col-md-pull-0 {
+ right: auto;
+ }
+ .col-md-push-12 {
+ left: 100%;
+ }
+ .col-md-push-11 {
+ left: 91.66666667%;
+ }
+ .col-md-push-10 {
+ left: 83.33333333%;
+ }
+ .col-md-push-9 {
+ left: 75%;
+ }
+ .col-md-push-8 {
+ left: 66.66666667%;
+ }
+ .col-md-push-7 {
+ left: 58.33333333%;
+ }
+ .col-md-push-6 {
+ left: 50%;
+ }
+ .col-md-push-5 {
+ left: 41.66666667%;
+ }
+ .col-md-push-4 {
+ left: 33.33333333%;
+ }
+ .col-md-push-3 {
+ left: 25%;
+ }
+ .col-md-push-2 {
+ left: 16.66666667%;
+ }
+ .col-md-push-1 {
+ left: 8.33333333%;
+ }
+ .col-md-push-0 {
+ left: auto;
+ }
+ .col-md-offset-12 {
+ margin-left: 100%;
+ }
+ .col-md-offset-11 {
+ margin-left: 91.66666667%;
+ }
+ .col-md-offset-10 {
+ margin-left: 83.33333333%;
+ }
+ .col-md-offset-9 {
+ margin-left: 75%;
+ }
+ .col-md-offset-8 {
+ margin-left: 66.66666667%;
+ }
+ .col-md-offset-7 {
+ margin-left: 58.33333333%;
+ }
+ .col-md-offset-6 {
+ margin-left: 50%;
+ }
+ .col-md-offset-5 {
+ margin-left: 41.66666667%;
+ }
+ .col-md-offset-4 {
+ margin-left: 33.33333333%;
+ }
+ .col-md-offset-3 {
+ margin-left: 25%;
+ }
+ .col-md-offset-2 {
+ margin-left: 16.66666667%;
+ }
+ .col-md-offset-1 {
+ margin-left: 8.33333333%;
+ }
+ .col-md-offset-0 {
+ margin-left: 0%;
+ }
+}
+@media (min-width: 1200px) {
+ .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+ float: left;
+ }
+ .col-lg-12 {
+ width: 100%;
+ }
+ .col-lg-11 {
+ width: 91.66666667%;
+ }
+ .col-lg-10 {
+ width: 83.33333333%;
+ }
+ .col-lg-9 {
+ width: 75%;
+ }
+ .col-lg-8 {
+ width: 66.66666667%;
+ }
+ .col-lg-7 {
+ width: 58.33333333%;
+ }
+ .col-lg-6 {
+ width: 50%;
+ }
+ .col-lg-5 {
+ width: 41.66666667%;
+ }
+ .col-lg-4 {
+ width: 33.33333333%;
+ }
+ .col-lg-3 {
+ width: 25%;
+ }
+ .col-lg-2 {
+ width: 16.66666667%;
+ }
+ .col-lg-1 {
+ width: 8.33333333%;
+ }
+ .col-lg-pull-12 {
+ right: 100%;
+ }
+ .col-lg-pull-11 {
+ right: 91.66666667%;
+ }
+ .col-lg-pull-10 {
+ right: 83.33333333%;
+ }
+ .col-lg-pull-9 {
+ right: 75%;
+ }
+ .col-lg-pull-8 {
+ right: 66.66666667%;
+ }
+ .col-lg-pull-7 {
+ right: 58.33333333%;
+ }
+ .col-lg-pull-6 {
+ right: 50%;
+ }
+ .col-lg-pull-5 {
+ right: 41.66666667%;
+ }
+ .col-lg-pull-4 {
+ right: 33.33333333%;
+ }
+ .col-lg-pull-3 {
+ right: 25%;
+ }
+ .col-lg-pull-2 {
+ right: 16.66666667%;
+ }
+ .col-lg-pull-1 {
+ right: 8.33333333%;
+ }
+ .col-lg-pull-0 {
+ right: auto;
+ }
+ .col-lg-push-12 {
+ left: 100%;
+ }
+ .col-lg-push-11 {
+ left: 91.66666667%;
+ }
+ .col-lg-push-10 {
+ left: 83.33333333%;
+ }
+ .col-lg-push-9 {
+ left: 75%;
+ }
+ .col-lg-push-8 {
+ left: 66.66666667%;
+ }
+ .col-lg-push-7 {
+ left: 58.33333333%;
+ }
+ .col-lg-push-6 {
+ left: 50%;
+ }
+ .col-lg-push-5 {
+ left: 41.66666667%;
+ }
+ .col-lg-push-4 {
+ left: 33.33333333%;
+ }
+ .col-lg-push-3 {
+ left: 25%;
+ }
+ .col-lg-push-2 {
+ left: 16.66666667%;
+ }
+ .col-lg-push-1 {
+ left: 8.33333333%;
+ }
+ .col-lg-push-0 {
+ left: auto;
+ }
+ .col-lg-offset-12 {
+ margin-left: 100%;
+ }
+ .col-lg-offset-11 {
+ margin-left: 91.66666667%;
+ }
+ .col-lg-offset-10 {
+ margin-left: 83.33333333%;
+ }
+ .col-lg-offset-9 {
+ margin-left: 75%;
+ }
+ .col-lg-offset-8 {
+ margin-left: 66.66666667%;
+ }
+ .col-lg-offset-7 {
+ margin-left: 58.33333333%;
+ }
+ .col-lg-offset-6 {
+ margin-left: 50%;
+ }
+ .col-lg-offset-5 {
+ margin-left: 41.66666667%;
+ }
+ .col-lg-offset-4 {
+ margin-left: 33.33333333%;
+ }
+ .col-lg-offset-3 {
+ margin-left: 25%;
+ }
+ .col-lg-offset-2 {
+ margin-left: 16.66666667%;
+ }
+ .col-lg-offset-1 {
+ margin-left: 8.33333333%;
+ }
+ .col-lg-offset-0 {
+ margin-left: 0%;
+ }
+}
+table {
+ background-color: transparent;
+}
+caption {
+ padding-top: 8px;
+ padding-bottom: 8px;
+ color: #777777;
+ text-align: left;
+}
+th {
+ text-align: left;
+}
+.table {
+ width: 100%;
+ max-width: 100%;
+ margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+ padding: 8px;
+ line-height: 1.42857143;
+ vertical-align: top;
+ border-top: 1px solid #dddddd;
+}
+.table > thead > tr > th {
+ vertical-align: bottom;
+ border-bottom: 2px solid #dddddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+ border-top: 0;
+}
+.table > tbody + tbody {
+ border-top: 2px solid #dddddd;
+}
+.table .table {
+ background-color: #ffffff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+ padding: 5px;
+}
+.table-bordered {
+ border: 1px solid #dddddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+ border: 1px solid #dddddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+ border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+ background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+ background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+ position: static;
+ float: none;
+ display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+ position: static;
+ float: none;
+ display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+ background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+ background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+ background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+ background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+ background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+ background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+ background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+ background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+ background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+ background-color: #ebcccc;
+}
+.table-responsive {
+ overflow-x: auto;
+ min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+ .table-responsive {
+ width: 100%;
+ margin-bottom: 13.5px;
+ overflow-y: hidden;
+ -ms-overflow-style: -ms-autohiding-scrollbar;
+ border: 1px solid #dddddd;
+ }
+ .table-responsive > .table {
+ margin-bottom: 0;
+ }
+ .table-responsive > .table > thead > tr > th,
+ .table-responsive > .table > tbody > tr > th,
+ .table-responsive > .table > tfoot > tr > th,
+ .table-responsive > .table > thead > tr > td,
+ .table-responsive > .table > tbody > tr > td,
+ .table-responsive > .table > tfoot > tr > td {
+ white-space: nowrap;
+ }
+ .table-responsive > .table-bordered {
+ border: 0;
+ }
+ .table-responsive > .table-bordered > thead > tr > th:first-child,
+ .table-responsive > .table-bordered > tbody > tr > th:first-child,
+ .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+ .table-responsive > .table-bordered > thead > tr > td:first-child,
+ .table-responsive > .table-bordered > tbody > tr > td:first-child,
+ .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+ border-left: 0;
+ }
+ .table-responsive > .table-bordered > thead > tr > th:last-child,
+ .table-responsive > .table-bordered > tbody > tr > th:last-child,
+ .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+ .table-responsive > .table-bordered > thead > tr > td:last-child,
+ .table-responsive > .table-bordered > tbody > tr > td:last-child,
+ .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+ border-right: 0;
+ }
+ .table-responsive > .table-bordered > tbody > tr:last-child > th,
+ .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+ .table-responsive > .table-bordered > tbody > tr:last-child > td,
+ .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+ border-bottom: 0;
+ }
+}
+fieldset {
+ padding: 0;
+ margin: 0;
+ border: 0;
+ min-width: 0;
+}
+legend {
+ display: block;
+ width: 100%;
+ padding: 0;
+ margin-bottom: 18px;
+ font-size: 19.5px;
+ line-height: inherit;
+ color: #333333;
+ border: 0;
+ border-bottom: 1px solid #e5e5e5;
+}
+label {
+ display: inline-block;
+ max-width: 100%;
+ margin-bottom: 5px;
+ font-weight: bold;
+}
+input[type="search"] {
+ -webkit-box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+ margin: 4px 0 0;
+ margin-top: 1px \9;
+ line-height: normal;
+}
+input[type="file"] {
+ display: block;
+}
+input[type="range"] {
+ display: block;
+ width: 100%;
+}
+select[multiple],
+select[size] {
+ height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+ outline: 5px auto -webkit-focus-ring-color;
+ outline-offset: -2px;
+}
+output {
+ display: block;
+ padding-top: 7px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #555555;
+}
+.form-control {
+ display: block;
+ width: 100%;
+ height: 32px;
+ padding: 6px 12px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #555555;
+ background-color: #ffffff;
+ background-image: none;
+ border: 1px solid #cccccc;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+ border-color: #66afe9;
+ outline: 0;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+ box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+ color: #999999;
+ opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+ color: #999999;
+}
+.form-control::-webkit-input-placeholder {
+ color: #999999;
+}
+.form-control::-ms-expand {
+ border: 0;
+ background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+ background-color: #eeeeee;
+ opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+ cursor: not-allowed;
+}
+textarea.form-control {
+ height: auto;
+}
+input[type="search"] {
+ -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+ input[type="date"].form-control,
+ input[type="time"].form-control,
+ input[type="datetime-local"].form-control,
+ input[type="month"].form-control {
+ line-height: 32px;
+ }
+ input[type="date"].input-sm,
+ input[type="time"].input-sm,
+ input[type="datetime-local"].input-sm,
+ input[type="month"].input-sm,
+ .input-group-sm input[type="date"],
+ .input-group-sm input[type="time"],
+ .input-group-sm input[type="datetime-local"],
+ .input-group-sm input[type="month"] {
+ line-height: 30px;
+ }
+ input[type="date"].input-lg,
+ input[type="time"].input-lg,
+ input[type="datetime-local"].input-lg,
+ input[type="month"].input-lg,
+ .input-group-lg input[type="date"],
+ .input-group-lg input[type="time"],
+ .input-group-lg input[type="datetime-local"],
+ .input-group-lg input[type="month"] {
+ line-height: 45px;
+ }
+}
+.form-group {
+ margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+ position: relative;
+ display: block;
+ margin-top: 10px;
+ margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+ min-height: 18px;
+ padding-left: 20px;
+ margin-bottom: 0;
+ font-weight: normal;
+ cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+ position: absolute;
+ margin-left: -20px;
+ margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+ margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+ position: relative;
+ display: inline-block;
+ padding-left: 20px;
+ margin-bottom: 0;
+ vertical-align: middle;
+ font-weight: normal;
+ cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+ margin-top: 0;
+ margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+ cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+ cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+ cursor: not-allowed;
+}
+.form-control-static {
+ padding-top: 7px;
+ padding-bottom: 7px;
+ margin-bottom: 0;
+ min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+ padding-left: 0;
+ padding-right: 0;
+}
+.input-sm {
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+select.input-sm {
+ height: 30px;
+ line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+ height: auto;
+}
+.form-group-sm .form-control {
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+.form-group-sm select.form-control {
+ height: 30px;
+ line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+ height: auto;
+}
+.form-group-sm .form-control-static {
+ height: 30px;
+ min-height: 30px;
+ padding: 6px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+}
+.input-lg {
+ height: 45px;
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+select.input-lg {
+ height: 45px;
+ line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+ height: auto;
+}
+.form-group-lg .form-control {
+ height: 45px;
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+.form-group-lg select.form-control {
+ height: 45px;
+ line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+ height: auto;
+}
+.form-group-lg .form-control-static {
+ height: 45px;
+ min-height: 35px;
+ padding: 11px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+}
+.has-feedback {
+ position: relative;
+}
+.has-feedback .form-control {
+ padding-right: 40px;
+}
+.form-control-feedback {
+ position: absolute;
+ top: 0;
+ right: 0;
+ z-index: 2;
+ display: block;
+ width: 32px;
+ height: 32px;
+ line-height: 32px;
+ text-align: center;
+ pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+ width: 45px;
+ height: 45px;
+ line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+ width: 30px;
+ height: 30px;
+ line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+ color: #3c763d;
+}
+.has-success .form-control {
+ border-color: #3c763d;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+ border-color: #2b542c;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+ color: #3c763d;
+ border-color: #3c763d;
+ background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+ color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+ color: #8a6d3b;
+}
+.has-warning .form-control {
+ border-color: #8a6d3b;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+ border-color: #66512c;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+ color: #8a6d3b;
+ border-color: #8a6d3b;
+ background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+ color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+ color: #a94442;
+}
+.has-error .form-control {
+ border-color: #a94442;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+ border-color: #843534;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+ color: #a94442;
+ border-color: #a94442;
+ background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+ color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+ top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+ top: 0;
+}
+.help-block {
+ display: block;
+ margin-top: 5px;
+ margin-bottom: 10px;
+ color: #404040;
+}
+@media (min-width: 768px) {
+ .form-inline .form-group {
+ display: inline-block;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .form-inline .form-control {
+ display: inline-block;
+ width: auto;
+ vertical-align: middle;
+ }
+ .form-inline .form-control-static {
+ display: inline-block;
+ }
+ .form-inline .input-group {
+ display: inline-table;
+ vertical-align: middle;
+ }
+ .form-inline .input-group .input-group-addon,
+ .form-inline .input-group .input-group-btn,
+ .form-inline .input-group .form-control {
+ width: auto;
+ }
+ .form-inline .input-group > .form-control {
+ width: 100%;
+ }
+ .form-inline .control-label {
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .form-inline .radio,
+ .form-inline .checkbox {
+ display: inline-block;
+ margin-top: 0;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .form-inline .radio label,
+ .form-inline .checkbox label {
+ padding-left: 0;
+ }
+ .form-inline .radio input[type="radio"],
+ .form-inline .checkbox input[type="checkbox"] {
+ position: relative;
+ margin-left: 0;
+ }
+ .form-inline .has-feedback .form-control-feedback {
+ top: 0;
+ }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+ margin-top: 0;
+ margin-bottom: 0;
+ padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+ min-height: 25px;
+}
+.form-horizontal .form-group {
+ margin-left: 0px;
+ margin-right: 0px;
+}
+@media (min-width: 768px) {
+ .form-horizontal .control-label {
+ text-align: right;
+ margin-bottom: 0;
+ padding-top: 7px;
+ }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+ right: 0px;
+}
+@media (min-width: 768px) {
+ .form-horizontal .form-group-lg .control-label {
+ padding-top: 11px;
+ font-size: 17px;
+ }
+}
+@media (min-width: 768px) {
+ .form-horizontal .form-group-sm .control-label {
+ padding-top: 6px;
+ font-size: 12px;
+ }
+}
+.btn {
+ display: inline-block;
+ margin-bottom: 0;
+ font-weight: normal;
+ text-align: center;
+ vertical-align: middle;
+ touch-action: manipulation;
+ cursor: pointer;
+ background-image: none;
+ border: 1px solid transparent;
+ white-space: nowrap;
+ padding: 6px 12px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ border-radius: 2px;
+ -webkit-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+ outline: 5px auto -webkit-focus-ring-color;
+ outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+ color: #333333;
+ text-decoration: none;
+}
+.btn:active,
+.btn.active {
+ outline: 0;
+ background-image: none;
+ -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+ box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+ cursor: not-allowed;
+ opacity: 0.65;
+ filter: alpha(opacity=65);
+ -webkit-box-shadow: none;
+ box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+ pointer-events: none;
+}
+.btn-default {
+ color: #333333;
+ background-color: #ffffff;
+ border-color: #cccccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+ color: #333333;
+ background-color: #e6e6e6;
+ border-color: #8c8c8c;
+}
+.btn-default:hover {
+ color: #333333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+ color: #333333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+ color: #333333;
+ background-color: #d4d4d4;
+ border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+ background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+ background-color: #ffffff;
+ border-color: #cccccc;
+}
+.btn-default .badge {
+ color: #ffffff;
+ background-color: #333333;
+}
+.btn-primary {
+ color: #ffffff;
+ background-color: #337ab7;
+ border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+ color: #ffffff;
+ background-color: #286090;
+ border-color: #122b40;
+}
+.btn-primary:hover {
+ color: #ffffff;
+ background-color: #286090;
+ border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+ color: #ffffff;
+ background-color: #286090;
+ border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+ color: #ffffff;
+ background-color: #204d74;
+ border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+ background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+ background-color: #337ab7;
+ border-color: #2e6da4;
+}
+.btn-primary .badge {
+ color: #337ab7;
+ background-color: #ffffff;
+}
+.btn-success {
+ color: #ffffff;
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+ color: #ffffff;
+ background-color: #449d44;
+ border-color: #255625;
+}
+.btn-success:hover {
+ color: #ffffff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+ color: #ffffff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+ color: #ffffff;
+ background-color: #398439;
+ border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+ background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.btn-success .badge {
+ color: #5cb85c;
+ background-color: #ffffff;
+}
+.btn-info {
+ color: #ffffff;
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+ color: #ffffff;
+ background-color: #31b0d5;
+ border-color: #1b6d85;
+}
+.btn-info:hover {
+ color: #ffffff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+ color: #ffffff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+ color: #ffffff;
+ background-color: #269abc;
+ border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+ background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.btn-info .badge {
+ color: #5bc0de;
+ background-color: #ffffff;
+}
+.btn-warning {
+ color: #ffffff;
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+ color: #ffffff;
+ background-color: #ec971f;
+ border-color: #985f0d;
+}
+.btn-warning:hover {
+ color: #ffffff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+ color: #ffffff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+ color: #ffffff;
+ background-color: #d58512;
+ border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+ background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.btn-warning .badge {
+ color: #f0ad4e;
+ background-color: #ffffff;
+}
+.btn-danger {
+ color: #ffffff;
+ background-color: #d9534f;
+ border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+ color: #ffffff;
+ background-color: #c9302c;
+ border-color: #761c19;
+}
+.btn-danger:hover {
+ color: #ffffff;
+ background-color: #c9302c;
+ border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+ color: #ffffff;
+ background-color: #c9302c;
+ border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+ color: #ffffff;
+ background-color: #ac2925;
+ border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+ background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+ background-color: #d9534f;
+ border-color: #d43f3a;
+}
+.btn-danger .badge {
+ color: #d9534f;
+ background-color: #ffffff;
+}
+.btn-link {
+ color: #337ab7;
+ font-weight: normal;
+ border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+ background-color: transparent;
+ -webkit-box-shadow: none;
+ box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+ border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+ color: #23527c;
+ text-decoration: underline;
+ background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+ color: #777777;
+ text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+ padding: 1px 5px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+.btn-block {
+ display: block;
+ width: 100%;
+}
+.btn-block + .btn-block {
+ margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+ width: 100%;
+}
+.fade {
+ opacity: 0;
+ -webkit-transition: opacity 0.15s linear;
+ -o-transition: opacity 0.15s linear;
+ transition: opacity 0.15s linear;
+}
+.fade.in {
+ opacity: 1;
+}
+.collapse {
+ display: none;
+}
+.collapse.in {
+ display: block;
+}
+tr.collapse.in {
+ display: table-row;
+}
+tbody.collapse.in {
+ display: table-row-group;
+}
+.collapsing {
+ position: relative;
+ height: 0;
+ overflow: hidden;
+ -webkit-transition-property: height, visibility;
+ transition-property: height, visibility;
+ -webkit-transition-duration: 0.35s;
+ transition-duration: 0.35s;
+ -webkit-transition-timing-function: ease;
+ transition-timing-function: ease;
+}
+.caret {
+ display: inline-block;
+ width: 0;
+ height: 0;
+ margin-left: 2px;
+ vertical-align: middle;
+ border-top: 4px dashed;
+ border-top: 4px solid \9;
+ border-right: 4px solid transparent;
+ border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+ position: relative;
+}
+.dropdown-toggle:focus {
+ outline: 0;
+}
+.dropdown-menu {
+ position: absolute;
+ top: 100%;
+ left: 0;
+ z-index: 1000;
+ display: none;
+ float: left;
+ min-width: 160px;
+ padding: 5px 0;
+ margin: 2px 0 0;
+ list-style: none;
+ font-size: 13px;
+ text-align: left;
+ background-color: #ffffff;
+ border: 1px solid #cccccc;
+ border: 1px solid rgba(0, 0, 0, 0.15);
+ border-radius: 2px;
+ -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+ box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+ background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+ right: 0;
+ left: auto;
+}
+.dropdown-menu .divider {
+ height: 1px;
+ margin: 8px 0;
+ overflow: hidden;
+ background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+ display: block;
+ padding: 3px 20px;
+ clear: both;
+ font-weight: normal;
+ line-height: 1.42857143;
+ color: #333333;
+ white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+ text-decoration: none;
+ color: #262626;
+ background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+ color: #ffffff;
+ text-decoration: none;
+ outline: 0;
+ background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+ color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+ text-decoration: none;
+ background-color: transparent;
+ background-image: none;
+ filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+ cursor: not-allowed;
+}
+.open > .dropdown-menu {
+ display: block;
+}
+.open > a {
+ outline: 0;
+}
+.dropdown-menu-right {
+ left: auto;
+ right: 0;
+}
+.dropdown-menu-left {
+ left: 0;
+ right: auto;
+}
+.dropdown-header {
+ display: block;
+ padding: 3px 20px;
+ font-size: 12px;
+ line-height: 1.42857143;
+ color: #777777;
+ white-space: nowrap;
+}
+.dropdown-backdrop {
+ position: fixed;
+ left: 0;
+ right: 0;
+ bottom: 0;
+ top: 0;
+ z-index: 990;
+}
+.pull-right > .dropdown-menu {
+ right: 0;
+ left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+ border-top: 0;
+ border-bottom: 4px dashed;
+ border-bottom: 4px solid \9;
+ content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+ top: auto;
+ bottom: 100%;
+ margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+ .navbar-right .dropdown-menu {
+ left: auto;
+ right: 0;
+ }
+ .navbar-right .dropdown-menu-left {
+ left: 0;
+ right: auto;
+ }
+}
+.btn-group,
+.btn-group-vertical {
+ position: relative;
+ display: inline-block;
+ vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+ position: relative;
+ float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+ z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+ margin-left: -1px;
+}
+.btn-toolbar {
+ margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+ float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+ margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+ border-radius: 0;
+}
+.btn-group > .btn:first-child {
+ margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+ border-bottom-right-radius: 0;
+ border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+ border-bottom-left-radius: 0;
+ border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+ float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+ border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+ border-bottom-right-radius: 0;
+ border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+ border-bottom-left-radius: 0;
+ border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+ outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+ padding-left: 8px;
+ padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+ padding-left: 12px;
+ padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+ -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+ box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+ -webkit-box-shadow: none;
+ box-shadow: none;
+}
+.btn .caret {
+ margin-left: 0;
+}
+.btn-lg .caret {
+ border-width: 5px 5px 0;
+ border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+ border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+ display: block;
+ float: none;
+ width: 100%;
+ max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+ float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+ margin-top: -1px;
+ margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+ border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+ border-top-right-radius: 2px;
+ border-top-left-radius: 2px;
+ border-bottom-right-radius: 0;
+ border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+ border-bottom-right-radius: 2px;
+ border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+ border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+ border-bottom-right-radius: 0;
+ border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.btn-group-justified {
+ display: table;
+ width: 100%;
+ table-layout: fixed;
+ border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+ float: none;
+ display: table-cell;
+ width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+ width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+ left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+ position: absolute;
+ clip: rect(0, 0, 0, 0);
+ pointer-events: none;
+}
+.input-group {
+ position: relative;
+ display: table;
+ border-collapse: separate;
+}
+.input-group[class*="col-"] {
+ float: none;
+ padding-left: 0;
+ padding-right: 0;
+}
+.input-group .form-control {
+ position: relative;
+ z-index: 2;
+ float: left;
+ width: 100%;
+ margin-bottom: 0;
+}
+.input-group .form-control:focus {
+ z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+ height: 45px;
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+ height: 45px;
+ line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+ height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+ height: 30px;
+ line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+ height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+ display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+ border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+ width: 1%;
+ white-space: nowrap;
+ vertical-align: middle;
+}
+.input-group-addon {
+ padding: 6px 12px;
+ font-size: 13px;
+ font-weight: normal;
+ line-height: 1;
+ color: #555555;
+ text-align: center;
+ background-color: #eeeeee;
+ border: 1px solid #cccccc;
+ border-radius: 2px;
+}
+.input-group-addon.input-sm {
+ padding: 5px 10px;
+ font-size: 12px;
+ border-radius: 1px;
+}
+.input-group-addon.input-lg {
+ padding: 10px 16px;
+ font-size: 17px;
+ border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+ margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+ border-bottom-right-radius: 0;
+ border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+ border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+ border-bottom-left-radius: 0;
+ border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+ border-left: 0;
+}
+.input-group-btn {
+ position: relative;
+ font-size: 0;
+ white-space: nowrap;
+}
+.input-group-btn > .btn {
+ position: relative;
+}
+.input-group-btn > .btn + .btn {
+ margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+ z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+ margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+ z-index: 2;
+ margin-left: -1px;
+}
+.nav {
+ margin-bottom: 0;
+ padding-left: 0;
+ list-style: none;
+}
+.nav > li {
+ position: relative;
+ display: block;
+}
+.nav > li > a {
+ position: relative;
+ display: block;
+ padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+ text-decoration: none;
+ background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+ color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+ color: #777777;
+ text-decoration: none;
+ background-color: transparent;
+ cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+ background-color: #eeeeee;
+ border-color: #337ab7;
+}
+.nav .nav-divider {
+ height: 1px;
+ margin: 8px 0;
+ overflow: hidden;
+ background-color: #e5e5e5;
+}
+.nav > li > a > img {
+ max-width: none;
+}
+.nav-tabs {
+ border-bottom: 1px solid #dddddd;
+}
+.nav-tabs > li {
+ float: left;
+ margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+ margin-right: 2px;
+ line-height: 1.42857143;
+ border: 1px solid transparent;
+ border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+ border-color: #eeeeee #eeeeee #dddddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+ color: #555555;
+ background-color: #ffffff;
+ border: 1px solid #dddddd;
+ border-bottom-color: transparent;
+ cursor: default;
+}
+.nav-tabs.nav-justified {
+ width: 100%;
+ border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+ float: none;
+}
+.nav-tabs.nav-justified > li > a {
+ text-align: center;
+ margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+ top: auto;
+ left: auto;
+}
+@media (min-width: 768px) {
+ .nav-tabs.nav-justified > li {
+ display: table-cell;
+ width: 1%;
+ }
+ .nav-tabs.nav-justified > li > a {
+ margin-bottom: 0;
+ }
+}
+.nav-tabs.nav-justified > li > a {
+ margin-right: 0;
+ border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+ border: 1px solid #dddddd;
+}
+@media (min-width: 768px) {
+ .nav-tabs.nav-justified > li > a {
+ border-bottom: 1px solid #dddddd;
+ border-radius: 2px 2px 0 0;
+ }
+ .nav-tabs.nav-justified > .active > a,
+ .nav-tabs.nav-justified > .active > a:hover,
+ .nav-tabs.nav-justified > .active > a:focus {
+ border-bottom-color: #ffffff;
+ }
+}
+.nav-pills > li {
+ float: left;
+}
+.nav-pills > li > a {
+ border-radius: 2px;
+}
+.nav-pills > li + li {
+ margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+ color: #ffffff;
+ background-color: #337ab7;
+}
+.nav-stacked > li {
+ float: none;
+}
+.nav-stacked > li + li {
+ margin-top: 2px;
+ margin-left: 0;
+}
+.nav-justified {
+ width: 100%;
+}
+.nav-justified > li {
+ float: none;
+}
+.nav-justified > li > a {
+ text-align: center;
+ margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+ top: auto;
+ left: auto;
+}
+@media (min-width: 768px) {
+ .nav-justified > li {
+ display: table-cell;
+ width: 1%;
+ }
+ .nav-justified > li > a {
+ margin-bottom: 0;
+ }
+}
+.nav-tabs-justified {
+ border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+ margin-right: 0;
+ border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+ border: 1px solid #dddddd;
+}
+@media (min-width: 768px) {
+ .nav-tabs-justified > li > a {
+ border-bottom: 1px solid #dddddd;
+ border-radius: 2px 2px 0 0;
+ }
+ .nav-tabs-justified > .active > a,
+ .nav-tabs-justified > .active > a:hover,
+ .nav-tabs-justified > .active > a:focus {
+ border-bottom-color: #ffffff;
+ }
+}
+.tab-content > .tab-pane {
+ display: none;
+}
+.tab-content > .active {
+ display: block;
+}
+.nav-tabs .dropdown-menu {
+ margin-top: -1px;
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.navbar {
+ position: relative;
+ min-height: 30px;
+ margin-bottom: 18px;
+ border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+ .navbar {
+ border-radius: 2px;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-header {
+ float: left;
+ }
+}
+.navbar-collapse {
+ overflow-x: visible;
+ padding-right: 0px;
+ padding-left: 0px;
+ border-top: 1px solid transparent;
+ box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+ -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+ overflow-y: auto;
+}
+@media (min-width: 541px) {
+ .navbar-collapse {
+ width: auto;
+ border-top: 0;
+ box-shadow: none;
+ }
+ .navbar-collapse.collapse {
+ display: block !important;
+ height: auto !important;
+ padding-bottom: 0;
+ overflow: visible !important;
+ }
+ .navbar-collapse.in {
+ overflow-y: visible;
+ }
+ .navbar-fixed-top .navbar-collapse,
+ .navbar-static-top .navbar-collapse,
+ .navbar-fixed-bottom .navbar-collapse {
+ padding-left: 0;
+ padding-right: 0;
+ }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+ max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+ .navbar-fixed-top .navbar-collapse,
+ .navbar-fixed-bottom .navbar-collapse {
+ max-height: 200px;
+ }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+ margin-right: 0px;
+ margin-left: 0px;
+}
+@media (min-width: 541px) {
+ .container > .navbar-header,
+ .container-fluid > .navbar-header,
+ .container > .navbar-collapse,
+ .container-fluid > .navbar-collapse {
+ margin-right: 0;
+ margin-left: 0;
+ }
+}
+.navbar-static-top {
+ z-index: 1000;
+ border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+ .navbar-static-top {
+ border-radius: 0;
+ }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+ position: fixed;
+ right: 0;
+ left: 0;
+ z-index: 1030;
+}
+@media (min-width: 541px) {
+ .navbar-fixed-top,
+ .navbar-fixed-bottom {
+ border-radius: 0;
+ }
+}
+.navbar-fixed-top {
+ top: 0;
+ border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+ bottom: 0;
+ margin-bottom: 0;
+ border-width: 1px 0 0;
+}
+.navbar-brand {
+ float: left;
+ padding: 6px 0px;
+ font-size: 17px;
+ line-height: 18px;
+ height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+ text-decoration: none;
+}
+.navbar-brand > img {
+ display: block;
+}
+@media (min-width: 541px) {
+ .navbar > .container .navbar-brand,
+ .navbar > .container-fluid .navbar-brand {
+ margin-left: 0px;
+ }
+}
+.navbar-toggle {
+ position: relative;
+ float: right;
+ margin-right: 0px;
+ padding: 9px 10px;
+ margin-top: -2px;
+ margin-bottom: -2px;
+ background-color: transparent;
+ background-image: none;
+ border: 1px solid transparent;
+ border-radius: 2px;
+}
+.navbar-toggle:focus {
+ outline: 0;
+}
+.navbar-toggle .icon-bar {
+ display: block;
+ width: 22px;
+ height: 2px;
+ border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+ margin-top: 4px;
+}
+@media (min-width: 541px) {
+ .navbar-toggle {
+ display: none;
+ }
+}
+.navbar-nav {
+ margin: 3px 0px;
+}
+.navbar-nav > li > a {
+ padding-top: 10px;
+ padding-bottom: 10px;
+ line-height: 18px;
+}
+@media (max-width: 540px) {
+ .navbar-nav .open .dropdown-menu {
+ position: static;
+ float: none;
+ width: auto;
+ margin-top: 0;
+ background-color: transparent;
+ border: 0;
+ box-shadow: none;
+ }
+ .navbar-nav .open .dropdown-menu > li > a,
+ .navbar-nav .open .dropdown-menu .dropdown-header {
+ padding: 5px 15px 5px 25px;
+ }
+ .navbar-nav .open .dropdown-menu > li > a {
+ line-height: 18px;
+ }
+ .navbar-nav .open .dropdown-menu > li > a:hover,
+ .navbar-nav .open .dropdown-menu > li > a:focus {
+ background-image: none;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-nav {
+ float: left;
+ margin: 0;
+ }
+ .navbar-nav > li {
+ float: left;
+ }
+ .navbar-nav > li > a {
+ padding-top: 6px;
+ padding-bottom: 6px;
+ }
+}
+.navbar-form {
+ margin-left: 0px;
+ margin-right: 0px;
+ padding: 10px 0px;
+ border-top: 1px solid transparent;
+ border-bottom: 1px solid transparent;
+ -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+ box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+ margin-top: -1px;
+ margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+ .navbar-form .form-group {
+ display: inline-block;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .navbar-form .form-control {
+ display: inline-block;
+ width: auto;
+ vertical-align: middle;
+ }
+ .navbar-form .form-control-static {
+ display: inline-block;
+ }
+ .navbar-form .input-group {
+ display: inline-table;
+ vertical-align: middle;
+ }
+ .navbar-form .input-group .input-group-addon,
+ .navbar-form .input-group .input-group-btn,
+ .navbar-form .input-group .form-control {
+ width: auto;
+ }
+ .navbar-form .input-group > .form-control {
+ width: 100%;
+ }
+ .navbar-form .control-label {
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .navbar-form .radio,
+ .navbar-form .checkbox {
+ display: inline-block;
+ margin-top: 0;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .navbar-form .radio label,
+ .navbar-form .checkbox label {
+ padding-left: 0;
+ }
+ .navbar-form .radio input[type="radio"],
+ .navbar-form .checkbox input[type="checkbox"] {
+ position: relative;
+ margin-left: 0;
+ }
+ .navbar-form .has-feedback .form-control-feedback {
+ top: 0;
+ }
+}
+@media (max-width: 540px) {
+ .navbar-form .form-group {
+ margin-bottom: 5px;
+ }
+ .navbar-form .form-group:last-child {
+ margin-bottom: 0;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-form {
+ width: auto;
+ border: 0;
+ margin-left: 0;
+ margin-right: 0;
+ padding-top: 0;
+ padding-bottom: 0;
+ -webkit-box-shadow: none;
+ box-shadow: none;
+ }
+}
+.navbar-nav > li > .dropdown-menu {
+ margin-top: 0;
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+ margin-bottom: 0;
+ border-top-right-radius: 2px;
+ border-top-left-radius: 2px;
+ border-bottom-right-radius: 0;
+ border-bottom-left-radius: 0;
+}
+.navbar-btn {
+ margin-top: -1px;
+ margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+ margin-top: 0px;
+ margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+ margin-top: 4px;
+ margin-bottom: 4px;
+}
+.navbar-text {
+ margin-top: 6px;
+ margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+ .navbar-text {
+ float: left;
+ margin-left: 0px;
+ margin-right: 0px;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-left {
+ float: left !important;
+ float: left;
+ }
+ .navbar-right {
+ float: right !important;
+ float: right;
+ margin-right: 0px;
+ }
+ .navbar-right ~ .navbar-right {
+ margin-right: 0;
+ }
+}
+.navbar-default {
+ background-color: #f8f8f8;
+ border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+ color: #777777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+ color: #5e5e5e;
+ background-color: transparent;
+}
+.navbar-default .navbar-text {
+ color: #777777;
+}
+.navbar-default .navbar-nav > li > a {
+ color: #777777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+ color: #333333;
+ background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+ color: #555555;
+ background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+ color: #cccccc;
+ background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+ border-color: #dddddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+ background-color: #dddddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+ background-color: #888888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+ border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+ background-color: #e7e7e7;
+ color: #555555;
+}
+@media (max-width: 540px) {
+ .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+ color: #777777;
+ }
+ .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+ .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+ color: #333333;
+ background-color: transparent;
+ }
+ .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+ .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+ .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+ color: #555555;
+ background-color: #e7e7e7;
+ }
+ .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+ .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+ .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+ color: #cccccc;
+ background-color: transparent;
+ }
+}
+.navbar-default .navbar-link {
+ color: #777777;
+}
+.navbar-default .navbar-link:hover {
+ color: #333333;
+}
+.navbar-default .btn-link {
+ color: #777777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+ color: #333333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+ color: #cccccc;
+}
+.navbar-inverse {
+ background-color: #222222;
+ border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+ color: #ffffff;
+ background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+ color: #ffffff;
+ background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+ color: #ffffff;
+ background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+ color: #444444;
+ background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+ border-color: #333333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+ background-color: #333333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+ background-color: #ffffff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+ border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+ background-color: #080808;
+ color: #ffffff;
+}
+@media (max-width: 540px) {
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+ border-color: #080808;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+ background-color: #080808;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+ color: #9d9d9d;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+ color: #ffffff;
+ background-color: transparent;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+ color: #ffffff;
+ background-color: #080808;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+ color: #444444;
+ background-color: transparent;
+ }
+}
+.navbar-inverse .navbar-link {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+ color: #ffffff;
+}
+.navbar-inverse .btn-link {
+ color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+ color: #ffffff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+ color: #444444;
+}
+.breadcrumb {
+ padding: 8px 15px;
+ margin-bottom: 18px;
+ list-style: none;
+ background-color: #f5f5f5;
+ border-radius: 2px;
+}
+.breadcrumb > li {
+ display: inline-block;
+}
+.breadcrumb > li + li:before {
+ content: "/\00a0";
+ padding: 0 5px;
+ color: #5e5e5e;
+}
+.breadcrumb > .active {
+ color: #777777;
+}
+.pagination {
+ display: inline-block;
+ padding-left: 0;
+ margin: 18px 0;
+ border-radius: 2px;
+}
+.pagination > li {
+ display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+ position: relative;
+ float: left;
+ padding: 6px 12px;
+ line-height: 1.42857143;
+ text-decoration: none;
+ color: #337ab7;
+ background-color: #ffffff;
+ border: 1px solid #dddddd;
+ margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+ margin-left: 0;
+ border-bottom-left-radius: 2px;
+ border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+ border-bottom-right-radius: 2px;
+ border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+ z-index: 2;
+ color: #23527c;
+ background-color: #eeeeee;
+ border-color: #dddddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+ z-index: 3;
+ color: #ffffff;
+ background-color: #337ab7;
+ border-color: #337ab7;
+ cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+ color: #777777;
+ background-color: #ffffff;
+ border-color: #dddddd;
+ cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+ border-bottom-left-radius: 3px;
+ border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+ border-bottom-right-radius: 3px;
+ border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+ border-bottom-left-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+ border-bottom-right-radius: 1px;
+ border-top-right-radius: 1px;
+}
+.pager {
+ padding-left: 0;
+ margin: 18px 0;
+ list-style: none;
+ text-align: center;
+}
+.pager li {
+ display: inline;
+}
+.pager li > a,
+.pager li > span {
+ display: inline-block;
+ padding: 5px 14px;
+ background-color: #ffffff;
+ border: 1px solid #dddddd;
+ border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+ text-decoration: none;
+ background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+ float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+ float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+ color: #777777;
+ background-color: #ffffff;
+ cursor: not-allowed;
+}
+.label {
+ display: inline;
+ padding: .2em .6em .3em;
+ font-size: 75%;
+ font-weight: bold;
+ line-height: 1;
+ color: #ffffff;
+ text-align: center;
+ white-space: nowrap;
+ vertical-align: baseline;
+ border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+ color: #ffffff;
+ text-decoration: none;
+ cursor: pointer;
+}
+.label:empty {
+ display: none;
+}
+.btn .label {
+ position: relative;
+ top: -1px;
+}
+.label-default {
+ background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+ background-color: #5e5e5e;
+}
+.label-primary {
+ background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+ background-color: #286090;
+}
+.label-success {
+ background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+ background-color: #449d44;
+}
+.label-info {
+ background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+ background-color: #31b0d5;
+}
+.label-warning {
+ background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+ background-color: #ec971f;
+}
+.label-danger {
+ background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+ background-color: #c9302c;
+}
+.badge {
+ display: inline-block;
+ min-width: 10px;
+ padding: 3px 7px;
+ font-size: 12px;
+ font-weight: bold;
+ color: #ffffff;
+ line-height: 1;
+ vertical-align: middle;
+ white-space: nowrap;
+ text-align: center;
+ background-color: #777777;
+ border-radius: 10px;
+}
+.badge:empty {
+ display: none;
+}
+.btn .badge {
+ position: relative;
+ top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+ top: 0;
+ padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+ color: #ffffff;
+ text-decoration: none;
+ cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+ color: #337ab7;
+ background-color: #ffffff;
+}
+.list-group-item > .badge {
+ float: right;
+}
+.list-group-item > .badge + .badge {
+ margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+ margin-left: 3px;
+}
+.jumbotron {
+ padding-top: 30px;
+ padding-bottom: 30px;
+ margin-bottom: 30px;
+ color: inherit;
+ background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+ color: inherit;
+}
+.jumbotron p {
+ margin-bottom: 15px;
+ font-size: 20px;
+ font-weight: 200;
+}
+.jumbotron > hr {
+ border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+ border-radius: 3px;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+.jumbotron .container {
+ max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+ .jumbotron {
+ padding-top: 48px;
+ padding-bottom: 48px;
+ }
+ .container .jumbotron,
+ .container-fluid .jumbotron {
+ padding-left: 60px;
+ padding-right: 60px;
+ }
+ .jumbotron h1,
+ .jumbotron .h1 {
+ font-size: 59px;
+ }
+}
+.thumbnail {
+ display: block;
+ padding: 4px;
+ margin-bottom: 18px;
+ line-height: 1.42857143;
+ background-color: #ffffff;
+ border: 1px solid #dddddd;
+ border-radius: 2px;
+ -webkit-transition: border 0.2s ease-in-out;
+ -o-transition: border 0.2s ease-in-out;
+ transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+ margin-left: auto;
+ margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+ border-color: #337ab7;
+}
+.thumbnail .caption {
+ padding: 9px;
+ color: #000000;
+}
+.alert {
+ padding: 15px;
+ margin-bottom: 18px;
+ border: 1px solid transparent;
+ border-radius: 2px;
+}
+.alert h4 {
+ margin-top: 0;
+ color: inherit;
+}
+.alert .alert-link {
+ font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+ margin-bottom: 0;
+}
+.alert > p + p {
+ margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+ padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+ position: relative;
+ top: -2px;
+ right: -21px;
+ color: inherit;
+}
+.alert-success {
+ background-color: #dff0d8;
+ border-color: #d6e9c6;
+ color: #3c763d;
+}
+.alert-success hr {
+ border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+ color: #2b542c;
+}
+.alert-info {
+ background-color: #d9edf7;
+ border-color: #bce8f1;
+ color: #31708f;
+}
+.alert-info hr {
+ border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+ color: #245269;
+}
+.alert-warning {
+ background-color: #fcf8e3;
+ border-color: #faebcc;
+ color: #8a6d3b;
+}
+.alert-warning hr {
+ border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+ color: #66512c;
+}
+.alert-danger {
+ background-color: #f2dede;
+ border-color: #ebccd1;
+ color: #a94442;
+}
+.alert-danger hr {
+ border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+ color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+ from {
+ background-position: 40px 0;
+ }
+ to {
+ background-position: 0 0;
+ }
+}
+@keyframes progress-bar-stripes {
+ from {
+ background-position: 40px 0;
+ }
+ to {
+ background-position: 0 0;
+ }
+}
+.progress {
+ overflow: hidden;
+ height: 18px;
+ margin-bottom: 18px;
+ background-color: #f5f5f5;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+ box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+ float: left;
+ width: 0%;
+ height: 100%;
+ font-size: 12px;
+ line-height: 18px;
+ color: #ffffff;
+ text-align: center;
+ background-color: #337ab7;
+ -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+ box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+ -webkit-transition: width 0.6s ease;
+ -o-transition: width 0.6s ease;
+ transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+ -webkit-animation: progress-bar-stripes 2s linear infinite;
+ -o-animation: progress-bar-stripes 2s linear infinite;
+ animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+ background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+ background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+ background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+ background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+ margin-top: 15px;
+}
+.media:first-child {
+ margin-top: 0;
+}
+.media,
+.media-body {
+ zoom: 1;
+ overflow: hidden;
+}
+.media-body {
+ width: 10000px;
+}
+.media-object {
+ display: block;
+}
+.media-object.img-thumbnail {
+ max-width: none;
+}
+.media-right,
+.media > .pull-right {
+ padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+ padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+ display: table-cell;
+ vertical-align: top;
+}
+.media-middle {
+ vertical-align: middle;
+}
+.media-bottom {
+ vertical-align: bottom;
+}
+.media-heading {
+ margin-top: 0;
+ margin-bottom: 5px;
+}
+.media-list {
+ padding-left: 0;
+ list-style: none;
+}
+.list-group {
+ margin-bottom: 20px;
+ padding-left: 0;
+}
+.list-group-item {
+ position: relative;
+ display: block;
+ padding: 10px 15px;
+ margin-bottom: -1px;
+ background-color: #ffffff;
+ border: 1px solid #dddddd;
+}
+.list-group-item:first-child {
+ border-top-right-radius: 2px;
+ border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+ margin-bottom: 0;
+ border-bottom-right-radius: 2px;
+ border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+ color: #555555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+ color: #333333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+ text-decoration: none;
+ color: #555555;
+ background-color: #f5f5f5;
+}
+button.list-group-item {
+ width: 100%;
+ text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+ background-color: #eeeeee;
+ color: #777777;
+ cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+ color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+ color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+ z-index: 2;
+ color: #ffffff;
+ background-color: #337ab7;
+ border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+ color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+ color: #c7ddef;
+}
+.list-group-item-success {
+ color: #3c763d;
+ background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+ color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+ color: #3c763d;
+ background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+ color: #fff;
+ background-color: #3c763d;
+ border-color: #3c763d;
+}
+.list-group-item-info {
+ color: #31708f;
+ background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+ color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+ color: #31708f;
+ background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+ color: #fff;
+ background-color: #31708f;
+ border-color: #31708f;
+}
+.list-group-item-warning {
+ color: #8a6d3b;
+ background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+ color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+ color: #8a6d3b;
+ background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+ color: #fff;
+ background-color: #8a6d3b;
+ border-color: #8a6d3b;
+}
+.list-group-item-danger {
+ color: #a94442;
+ background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+ color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+ color: #a94442;
+ background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+ color: #fff;
+ background-color: #a94442;
+ border-color: #a94442;
+}
+.list-group-item-heading {
+ margin-top: 0;
+ margin-bottom: 5px;
+}
+.list-group-item-text {
+ margin-bottom: 0;
+ line-height: 1.3;
+}
+.panel {
+ margin-bottom: 18px;
+ background-color: #ffffff;
+ border: 1px solid transparent;
+ border-radius: 2px;
+ -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+ box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+ padding: 15px;
+}
+.panel-heading {
+ padding: 10px 15px;
+ border-bottom: 1px solid transparent;
+ border-top-right-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+ color: inherit;
+}
+.panel-title {
+ margin-top: 0;
+ margin-bottom: 0;
+ font-size: 15px;
+ color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+ color: inherit;
+}
+.panel-footer {
+ padding: 10px 15px;
+ background-color: #f5f5f5;
+ border-top: 1px solid #dddddd;
+ border-bottom-right-radius: 1px;
+ border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+ margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+ border-width: 1px 0;
+ border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+ border-top: 0;
+ border-top-right-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+ border-bottom: 0;
+ border-bottom-right-radius: 1px;
+ border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+ border-top-width: 0;
+}
+.list-group + .panel-footer {
+ border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+ margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+ padding-left: 15px;
+ padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+ border-top-right-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+ border-top-left-radius: 1px;
+ border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+ border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+ border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+ border-bottom-right-radius: 1px;
+ border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+ border-bottom-left-radius: 1px;
+ border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+ border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+ border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+ border-top: 1px solid #dddddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+ border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+ border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+ border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+ border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+ border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+ border-bottom: 0;
+}
+.panel > .table-responsive {
+ border: 0;
+ margin-bottom: 0;
+}
+.panel-group {
+ margin-bottom: 18px;
+}
+.panel-group .panel {
+ margin-bottom: 0;
+ border-radius: 2px;
+}
+.panel-group .panel + .panel {
+ margin-top: 5px;
+}
+.panel-group .panel-heading {
+ border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+ border-top: 1px solid #dddddd;
+}
+.panel-group .panel-footer {
+ border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+ border-bottom: 1px solid #dddddd;
+}
+.panel-default {
+ border-color: #dddddd;
+}
+.panel-default > .panel-heading {
+ color: #333333;
+ background-color: #f5f5f5;
+ border-color: #dddddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #dddddd;
+}
+.panel-default > .panel-heading .badge {
+ color: #f5f5f5;
+ background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #dddddd;
+}
+.panel-primary {
+ border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+ color: #ffffff;
+ background-color: #337ab7;
+ border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+ color: #337ab7;
+ background-color: #ffffff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #337ab7;
+}
+.panel-success {
+ border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+ color: #3c763d;
+ background-color: #dff0d8;
+ border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+ color: #dff0d8;
+ background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #d6e9c6;
+}
+.panel-info {
+ border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+ color: #31708f;
+ background-color: #d9edf7;
+ border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+ color: #d9edf7;
+ background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #bce8f1;
+}
+.panel-warning {
+ border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+ color: #8a6d3b;
+ background-color: #fcf8e3;
+ border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+ color: #fcf8e3;
+ background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #faebcc;
+}
+.panel-danger {
+ border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+ color: #a94442;
+ background-color: #f2dede;
+ border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+ color: #f2dede;
+ background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+ position: relative;
+ display: block;
+ height: 0;
+ padding: 0;
+ overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+ position: absolute;
+ top: 0;
+ left: 0;
+ bottom: 0;
+ height: 100%;
+ width: 100%;
+ border: 0;
+}
+.embed-responsive-16by9 {
+ padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+ padding-bottom: 75%;
+}
+.well {
+ min-height: 20px;
+ padding: 19px;
+ margin-bottom: 20px;
+ background-color: #f5f5f5;
+ border: 1px solid #e3e3e3;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+ border-color: #ddd;
+ border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+ padding: 24px;
+ border-radius: 3px;
+}
+.well-sm {
+ padding: 9px;
+ border-radius: 1px;
+}
+.close {
+ float: right;
+ font-size: 19.5px;
+ font-weight: bold;
+ line-height: 1;
+ color: #000000;
+ text-shadow: 0 1px 0 #ffffff;
+ opacity: 0.2;
+ filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+ color: #000000;
+ text-decoration: none;
+ cursor: pointer;
+ opacity: 0.5;
+ filter: alpha(opacity=50);
+}
+button.close {
+ padding: 0;
+ cursor: pointer;
+ background: transparent;
+ border: 0;
+ -webkit-appearance: none;
+}
+.modal-open {
+ overflow: hidden;
+}
+.modal {
+ display: none;
+ overflow: hidden;
+ position: fixed;
+ top: 0;
+ right: 0;
+ bottom: 0;
+ left: 0;
+ z-index: 1050;
+ -webkit-overflow-scrolling: touch;
+ outline: 0;
+}
+.modal.fade .modal-dialog {
+ -webkit-transform: translate(0, -25%);
+ -ms-transform: translate(0, -25%);
+ -o-transform: translate(0, -25%);
+ transform: translate(0, -25%);
+ -webkit-transition: -webkit-transform 0.3s ease-out;
+ -moz-transition: -moz-transform 0.3s ease-out;
+ -o-transition: -o-transform 0.3s ease-out;
+ transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+ -webkit-transform: translate(0, 0);
+ -ms-transform: translate(0, 0);
+ -o-transform: translate(0, 0);
+ transform: translate(0, 0);
+}
+.modal-open .modal {
+ overflow-x: hidden;
+ overflow-y: auto;
+}
+.modal-dialog {
+ position: relative;
+ width: auto;
+ margin: 10px;
+}
+.modal-content {
+ position: relative;
+ background-color: #ffffff;
+ border: 1px solid #999999;
+ border: 1px solid rgba(0, 0, 0, 0.2);
+ border-radius: 3px;
+ -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+ box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+ background-clip: padding-box;
+ outline: 0;
+}
+.modal-backdrop {
+ position: fixed;
+ top: 0;
+ right: 0;
+ bottom: 0;
+ left: 0;
+ z-index: 1040;
+ background-color: #000000;
+}
+.modal-backdrop.fade {
+ opacity: 0;
+ filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+ opacity: 0.5;
+ filter: alpha(opacity=50);
+}
+.modal-header {
+ padding: 15px;
+ border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+ margin-top: -2px;
+}
+.modal-title {
+ margin: 0;
+ line-height: 1.42857143;
+}
+.modal-body {
+ position: relative;
+ padding: 15px;
+}
+.modal-footer {
+ padding: 15px;
+ text-align: right;
+ border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+ margin-left: 5px;
+ margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+ margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+ margin-left: 0;
+}
+.modal-scrollbar-measure {
+ position: absolute;
+ top: -9999px;
+ width: 50px;
+ height: 50px;
+ overflow: scroll;
+}
+@media (min-width: 768px) {
+ .modal-dialog {
+ width: 600px;
+ margin: 30px auto;
+ }
+ .modal-content {
+ -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+ box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+ }
+ .modal-sm {
+ width: 300px;
+ }
+}
+@media (min-width: 992px) {
+ .modal-lg {
+ width: 900px;
+ }
+}
+.tooltip {
+ position: absolute;
+ z-index: 1070;
+ display: block;
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+ font-style: normal;
+ font-weight: normal;
+ letter-spacing: normal;
+ line-break: auto;
+ line-height: 1.42857143;
+ text-align: left;
+ text-align: start;
+ text-decoration: none;
+ text-shadow: none;
+ text-transform: none;
+ white-space: normal;
+ word-break: normal;
+ word-spacing: normal;
+ word-wrap: normal;
+ font-size: 12px;
+ opacity: 0;
+ filter: alpha(opacity=0);
+}
+.tooltip.in {
+ opacity: 0.9;
+ filter: alpha(opacity=90);
+}
+.tooltip.top {
+ margin-top: -3px;
+ padding: 5px 0;
+}
+.tooltip.right {
+ margin-left: 3px;
+ padding: 0 5px;
+}
+.tooltip.bottom {
+ margin-top: 3px;
+ padding: 5px 0;
+}
+.tooltip.left {
+ margin-left: -3px;
+ padding: 0 5px;
+}
+.tooltip-inner {
+ max-width: 200px;
+ padding: 3px 8px;
+ color: #ffffff;
+ text-align: center;
+ background-color: #000000;
+ border-radius: 2px;
+}
+.tooltip-arrow {
+ position: absolute;
+ width: 0;
+ height: 0;
+ border-color: transparent;
+ border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+ bottom: 0;
+ left: 50%;
+ margin-left: -5px;
+ border-width: 5px 5px 0;
+ border-top-color: #000000;
+}
+.tooltip.top-left .tooltip-arrow {
+ bottom: 0;
+ right: 5px;
+ margin-bottom: -5px;
+ border-width: 5px 5px 0;
+ border-top-color: #000000;
+}
+.tooltip.top-right .tooltip-arrow {
+ bottom: 0;
+ left: 5px;
+ margin-bottom: -5px;
+ border-width: 5px 5px 0;
+ border-top-color: #000000;
+}
+.tooltip.right .tooltip-arrow {
+ top: 50%;
+ left: 0;
+ margin-top: -5px;
+ border-width: 5px 5px 5px 0;
+ border-right-color: #000000;
+}
+.tooltip.left .tooltip-arrow {
+ top: 50%;
+ right: 0;
+ margin-top: -5px;
+ border-width: 5px 0 5px 5px;
+ border-left-color: #000000;
+}
+.tooltip.bottom .tooltip-arrow {
+ top: 0;
+ left: 50%;
+ margin-left: -5px;
+ border-width: 0 5px 5px;
+ border-bottom-color: #000000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+ top: 0;
+ right: 5px;
+ margin-top: -5px;
+ border-width: 0 5px 5px;
+ border-bottom-color: #000000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+ top: 0;
+ left: 5px;
+ margin-top: -5px;
+ border-width: 0 5px 5px;
+ border-bottom-color: #000000;
+}
+.popover {
+ position: absolute;
+ top: 0;
+ left: 0;
+ z-index: 1060;
+ display: none;
+ max-width: 276px;
+ padding: 1px;
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+ font-style: normal;
+ font-weight: normal;
+ letter-spacing: normal;
+ line-break: auto;
+ line-height: 1.42857143;
+ text-align: left;
+ text-align: start;
+ text-decoration: none;
+ text-shadow: none;
+ text-transform: none;
+ white-space: normal;
+ word-break: normal;
+ word-spacing: normal;
+ word-wrap: normal;
+ font-size: 13px;
+ background-color: #ffffff;
+ background-clip: padding-box;
+ border: 1px solid #cccccc;
+ border: 1px solid rgba(0, 0, 0, 0.2);
+ border-radius: 3px;
+ -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+ box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+ margin-top: -10px;
+}
+.popover.right {
+ margin-left: 10px;
+}
+.popover.bottom {
+ margin-top: 10px;
+}
+.popover.left {
+ margin-left: -10px;
+}
+.popover-title {
+ margin: 0;
+ padding: 8px 14px;
+ font-size: 13px;
+ background-color: #f7f7f7;
+ border-bottom: 1px solid #ebebeb;
+ border-radius: 2px 2px 0 0;
+}
+.popover-content {
+ padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+ position: absolute;
+ display: block;
+ width: 0;
+ height: 0;
+ border-color: transparent;
+ border-style: solid;
+}
+.popover > .arrow {
+ border-width: 11px;
+}
+.popover > .arrow:after {
+ border-width: 10px;
+ content: "";
+}
+.popover.top > .arrow {
+ left: 50%;
+ margin-left: -11px;
+ border-bottom-width: 0;
+ border-top-color: #999999;
+ border-top-color: rgba(0, 0, 0, 0.25);
+ bottom: -11px;
+}
+.popover.top > .arrow:after {
+ content: " ";
+ bottom: 1px;
+ margin-left: -10px;
+ border-bottom-width: 0;
+ border-top-color: #ffffff;
+}
+.popover.right > .arrow {
+ top: 50%;
+ left: -11px;
+ margin-top: -11px;
+ border-left-width: 0;
+ border-right-color: #999999;
+ border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+ content: " ";
+ left: 1px;
+ bottom: -10px;
+ border-left-width: 0;
+ border-right-color: #ffffff;
+}
+.popover.bottom > .arrow {
+ left: 50%;
+ margin-left: -11px;
+ border-top-width: 0;
+ border-bottom-color: #999999;
+ border-bottom-color: rgba(0, 0, 0, 0.25);
+ top: -11px;
+}
+.popover.bottom > .arrow:after {
+ content: " ";
+ top: 1px;
+ margin-left: -10px;
+ border-top-width: 0;
+ border-bottom-color: #ffffff;
+}
+.popover.left > .arrow {
+ top: 50%;
+ right: -11px;
+ margin-top: -11px;
+ border-right-width: 0;
+ border-left-color: #999999;
+ border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+ content: " ";
+ right: 1px;
+ border-right-width: 0;
+ border-left-color: #ffffff;
+ bottom: -10px;
+}
+.carousel {
+ position: relative;
+}
+.carousel-inner {
+ position: relative;
+ overflow: hidden;
+ width: 100%;
+}
+.carousel-inner > .item {
+ display: none;
+ position: relative;
+ -webkit-transition: 0.6s ease-in-out left;
+ -o-transition: 0.6s ease-in-out left;
+ transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+ line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+ .carousel-inner > .item {
+ -webkit-transition: -webkit-transform 0.6s ease-in-out;
+ -moz-transition: -moz-transform 0.6s ease-in-out;
+ -o-transition: -o-transform 0.6s ease-in-out;
+ transition: transform 0.6s ease-in-out;
+ -webkit-backface-visibility: hidden;
+ -moz-backface-visibility: hidden;
+ backface-visibility: hidden;
+ -webkit-perspective: 1000px;
+ -moz-perspective: 1000px;
+ perspective: 1000px;
+ }
+ .carousel-inner > .item.next,
+ .carousel-inner > .item.active.right {
+ -webkit-transform: translate3d(100%, 0, 0);
+ transform: translate3d(100%, 0, 0);
+ left: 0;
+ }
+ .carousel-inner > .item.prev,
+ .carousel-inner > .item.active.left {
+ -webkit-transform: translate3d(-100%, 0, 0);
+ transform: translate3d(-100%, 0, 0);
+ left: 0;
+ }
+ .carousel-inner > .item.next.left,
+ .carousel-inner > .item.prev.right,
+ .carousel-inner > .item.active {
+ -webkit-transform: translate3d(0, 0, 0);
+ transform: translate3d(0, 0, 0);
+ left: 0;
+ }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+ display: block;
+}
+.carousel-inner > .active {
+ left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+ position: absolute;
+ top: 0;
+ width: 100%;
+}
+.carousel-inner > .next {
+ left: 100%;
+}
+.carousel-inner > .prev {
+ left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+ left: 0;
+}
+.carousel-inner > .active.left {
+ left: -100%;
+}
+.carousel-inner > .active.right {
+ left: 100%;
+}
+.carousel-control {
+ position: absolute;
+ top: 0;
+ left: 0;
+ bottom: 0;
+ width: 15%;
+ opacity: 0.5;
+ filter: alpha(opacity=50);
+ font-size: 20px;
+ color: #ffffff;
+ text-align: center;
+ text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+ background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+ background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+ background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+ background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+ background-repeat: repeat-x;
+ filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+ left: auto;
+ right: 0;
+ background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+ background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+ background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+ background-repeat: repeat-x;
+ filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+ outline: 0;
+ color: #ffffff;
+ text-decoration: none;
+ opacity: 0.9;
+ filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+ position: absolute;
+ top: 50%;
+ margin-top: -10px;
+ z-index: 5;
+ display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+ left: 50%;
+ margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+ right: 50%;
+ margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+ width: 20px;
+ height: 20px;
+ line-height: 1;
+ font-family: serif;
+}
+.carousel-control .icon-prev:before {
+ content: '\2039';
+}
+.carousel-control .icon-next:before {
+ content: '\203a';
+}
+.carousel-indicators {
+ position: absolute;
+ bottom: 10px;
+ left: 50%;
+ z-index: 15;
+ width: 60%;
+ margin-left: -30%;
+ padding-left: 0;
+ list-style: none;
+ text-align: center;
+}
+.carousel-indicators li {
+ display: inline-block;
+ width: 10px;
+ height: 10px;
+ margin: 1px;
+ text-indent: -999px;
+ border: 1px solid #ffffff;
+ border-radius: 10px;
+ cursor: pointer;
+ background-color: #000 \9;
+ background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+ margin: 0;
+ width: 12px;
+ height: 12px;
+ background-color: #ffffff;
+}
+.carousel-caption {
+ position: absolute;
+ left: 15%;
+ right: 15%;
+ bottom: 20px;
+ z-index: 10;
+ padding-top: 20px;
+ padding-bottom: 20px;
+ color: #ffffff;
+ text-align: center;
+ text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+ text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+ .carousel-control .glyphicon-chevron-left,
+ .carousel-control .glyphicon-chevron-right,
+ .carousel-control .icon-prev,
+ .carousel-control .icon-next {
+ width: 30px;
+ height: 30px;
+ margin-top: -10px;
+ font-size: 30px;
+ }
+ .carousel-control .glyphicon-chevron-left,
+ .carousel-control .icon-prev {
+ margin-left: -10px;
+ }
+ .carousel-control .glyphicon-chevron-right,
+ .carousel-control .icon-next {
+ margin-right: -10px;
+ }
+ .carousel-caption {
+ left: 20%;
+ right: 20%;
+ padding-bottom: 30px;
+ }
+ .carousel-indicators {
+ bottom: 20px;
+ }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+ content: " ";
+ display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+ clear: both;
+}
+.center-block {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+.pull-right {
+ float: right !important;
+}
+.pull-left {
+ float: left !important;
+}
+.hide {
+ display: none !important;
+}
+.show {
+ display: block !important;
+}
+.invisible {
+ visibility: hidden;
+}
+.text-hide {
+ font: 0/0 a;
+ color: transparent;
+ text-shadow: none;
+ background-color: transparent;
+ border: 0;
+}
+.hidden {
+ display: none !important;
+}
+.affix {
+ position: fixed;
+}
+@-ms-viewport {
+ width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+ display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+ display: none !important;
+}
+@media (max-width: 767px) {
+ .visible-xs {
+ display: block !important;
+ }
+ table.visible-xs {
+ display: table !important;
+ }
+ tr.visible-xs {
+ display: table-row !important;
+ }
+ th.visible-xs,
+ td.visible-xs {
+ display: table-cell !important;
+ }
+}
+@media (max-width: 767px) {
+ .visible-xs-block {
+ display: block !important;
+ }
+}
+@media (max-width: 767px) {
+ .visible-xs-inline {
+ display: inline !important;
+ }
+}
+@media (max-width: 767px) {
+ .visible-xs-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm {
+ display: block !important;
+ }
+ table.visible-sm {
+ display: table !important;
+ }
+ tr.visible-sm {
+ display: table-row !important;
+ }
+ th.visible-sm,
+ td.visible-sm {
+ display: table-cell !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm-block {
+ display: block !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm-inline {
+ display: inline !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md {
+ display: block !important;
+ }
+ table.visible-md {
+ display: table !important;
+ }
+ tr.visible-md {
+ display: table-row !important;
+ }
+ th.visible-md,
+ td.visible-md {
+ display: table-cell !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md-block {
+ display: block !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md-inline {
+ display: inline !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg {
+ display: block !important;
+ }
+ table.visible-lg {
+ display: table !important;
+ }
+ tr.visible-lg {
+ display: table-row !important;
+ }
+ th.visible-lg,
+ td.visible-lg {
+ display: table-cell !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg-block {
+ display: block !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg-inline {
+ display: inline !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (max-width: 767px) {
+ .hidden-xs {
+ display: none !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .hidden-sm {
+ display: none !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .hidden-md {
+ display: none !important;
+ }
+}
+@media (min-width: 1200px) {
+ .hidden-lg {
+ display: none !important;
+ }
+}
+.visible-print {
+ display: none !important;
+}
+@media print {
+ .visible-print {
+ display: block !important;
+ }
+ table.visible-print {
+ display: table !important;
+ }
+ tr.visible-print {
+ display: table-row !important;
+ }
+ th.visible-print,
+ td.visible-print {
+ display: table-cell !important;
+ }
+}
+.visible-print-block {
+ display: none !important;
+}
+@media print {
+ .visible-print-block {
+ display: block !important;
+ }
+}
+.visible-print-inline {
+ display: none !important;
+}
+@media print {
+ .visible-print-inline {
+ display: inline !important;
+ }
+}
+.visible-print-inline-block {
+ display: none !important;
+}
+@media print {
+ .visible-print-inline-block {
+ display: inline-block !important;
+ }
+}
+@media print {
+ .hidden-print {
+ display: none !important;
+ }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ * Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
+ * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+ font-family: 'FontAwesome';
+ src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
+ src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
+ font-weight: normal;
+ font-style: normal;
+}
+.fa {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+ font-size: 1.33333333em;
+ line-height: 0.75em;
+ vertical-align: -15%;
+}
+.fa-2x {
+ font-size: 2em;
+}
+.fa-3x {
+ font-size: 3em;
+}
+.fa-4x {
+ font-size: 4em;
+}
+.fa-5x {
+ font-size: 5em;
+}
+.fa-fw {
+ width: 1.28571429em;
+ text-align: center;
+}
+.fa-ul {
+ padding-left: 0;
+ margin-left: 2.14285714em;
+ list-style-type: none;
+}
+.fa-ul > li {
+ position: relative;
+}
+.fa-li {
+ position: absolute;
+ left: -2.14285714em;
+ width: 2.14285714em;
+ top: 0.14285714em;
+ text-align: center;
+}
+.fa-li.fa-lg {
+ left: -1.85714286em;
+}
+.fa-border {
+ padding: .2em .25em .15em;
+ border: solid 0.08em #eeeeee;
+ border-radius: .1em;
+}
+.fa-pull-left {
+ float: left;
+}
+.fa-pull-right {
+ float: right;
+}
+.fa.fa-pull-left {
+ margin-right: .3em;
+}
+.fa.fa-pull-right {
+ margin-left: .3em;
+}
+/* Deprecated as of 4.4.0 */
+.pull-right {
+ float: right;
+}
+.pull-left {
+ float: left;
+}
+.fa.pull-left {
+ margin-right: .3em;
+}
+.fa.pull-right {
+ margin-left: .3em;
+}
+.fa-spin {
+ -webkit-animation: fa-spin 2s infinite linear;
+ animation: fa-spin 2s infinite linear;
+}
+.fa-pulse {
+ -webkit-animation: fa-spin 1s infinite steps(8);
+ animation: fa-spin 1s infinite steps(8);
+}
+@-webkit-keyframes fa-spin {
+ 0% {
+ -webkit-transform: rotate(0deg);
+ transform: rotate(0deg);
+ }
+ 100% {
+ -webkit-transform: rotate(359deg);
+ transform: rotate(359deg);
+ }
+}
+@keyframes fa-spin {
+ 0% {
+ -webkit-transform: rotate(0deg);
+ transform: rotate(0deg);
+ }
+ 100% {
+ -webkit-transform: rotate(359deg);
+ transform: rotate(359deg);
+ }
+}
+.fa-rotate-90 {
+ -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
+ -webkit-transform: rotate(90deg);
+ -ms-transform: rotate(90deg);
+ transform: rotate(90deg);
+}
+.fa-rotate-180 {
+ -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
+ -webkit-transform: rotate(180deg);
+ -ms-transform: rotate(180deg);
+ transform: rotate(180deg);
+}
+.fa-rotate-270 {
+ -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
+ -webkit-transform: rotate(270deg);
+ -ms-transform: rotate(270deg);
+ transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+ -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
+ -webkit-transform: scale(-1, 1);
+ -ms-transform: scale(-1, 1);
+ transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+ -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
+ -webkit-transform: scale(1, -1);
+ -ms-transform: scale(1, -1);
+ transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+ filter: none;
+}
+.fa-stack {
+ position: relative;
+ display: inline-block;
+ width: 2em;
+ height: 2em;
+ line-height: 2em;
+ vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+ position: absolute;
+ left: 0;
+ width: 100%;
+ text-align: center;
+}
+.fa-stack-1x {
+ line-height: inherit;
+}
+.fa-stack-2x {
+ font-size: 2em;
+}
+.fa-inverse {
+ color: #ffffff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+ readers do not read off random characters that represent icons */
+.fa-glass:before {
+ content: "\f000";
+}
+.fa-music:before {
+ content: "\f001";
+}
+.fa-search:before {
+ content: "\f002";
+}
+.fa-envelope-o:before {
+ content: "\f003";
+}
+.fa-heart:before {
+ content: "\f004";
+}
+.fa-star:before {
+ content: "\f005";
+}
+.fa-star-o:before {
+ content: "\f006";
+}
+.fa-user:before {
+ content: "\f007";
+}
+.fa-film:before {
+ content: "\f008";
+}
+.fa-th-large:before {
+ content: "\f009";
+}
+.fa-th:before {
+ content: "\f00a";
+}
+.fa-th-list:before {
+ content: "\f00b";
+}
+.fa-check:before {
+ content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+ content: "\f00d";
+}
+.fa-search-plus:before {
+ content: "\f00e";
+}
+.fa-search-minus:before {
+ content: "\f010";
+}
+.fa-power-off:before {
+ content: "\f011";
+}
+.fa-signal:before {
+ content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+ content: "\f013";
+}
+.fa-trash-o:before {
+ content: "\f014";
+}
+.fa-home:before {
+ content: "\f015";
+}
+.fa-file-o:before {
+ content: "\f016";
+}
+.fa-clock-o:before {
+ content: "\f017";
+}
+.fa-road:before {
+ content: "\f018";
+}
+.fa-download:before {
+ content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+ content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+ content: "\f01b";
+}
+.fa-inbox:before {
+ content: "\f01c";
+}
+.fa-play-circle-o:before {
+ content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+ content: "\f01e";
+}
+.fa-refresh:before {
+ content: "\f021";
+}
+.fa-list-alt:before {
+ content: "\f022";
+}
+.fa-lock:before {
+ content: "\f023";
+}
+.fa-flag:before {
+ content: "\f024";
+}
+.fa-headphones:before {
+ content: "\f025";
+}
+.fa-volume-off:before {
+ content: "\f026";
+}
+.fa-volume-down:before {
+ content: "\f027";
+}
+.fa-volume-up:before {
+ content: "\f028";
+}
+.fa-qrcode:before {
+ content: "\f029";
+}
+.fa-barcode:before {
+ content: "\f02a";
+}
+.fa-tag:before {
+ content: "\f02b";
+}
+.fa-tags:before {
+ content: "\f02c";
+}
+.fa-book:before {
+ content: "\f02d";
+}
+.fa-bookmark:before {
+ content: "\f02e";
+}
+.fa-print:before {
+ content: "\f02f";
+}
+.fa-camera:before {
+ content: "\f030";
+}
+.fa-font:before {
+ content: "\f031";
+}
+.fa-bold:before {
+ content: "\f032";
+}
+.fa-italic:before {
+ content: "\f033";
+}
+.fa-text-height:before {
+ content: "\f034";
+}
+.fa-text-width:before {
+ content: "\f035";
+}
+.fa-align-left:before {
+ content: "\f036";
+}
+.fa-align-center:before {
+ content: "\f037";
+}
+.fa-align-right:before {
+ content: "\f038";
+}
+.fa-align-justify:before {
+ content: "\f039";
+}
+.fa-list:before {
+ content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+ content: "\f03b";
+}
+.fa-indent:before {
+ content: "\f03c";
+}
+.fa-video-camera:before {
+ content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+ content: "\f03e";
+}
+.fa-pencil:before {
+ content: "\f040";
+}
+.fa-map-marker:before {
+ content: "\f041";
+}
+.fa-adjust:before {
+ content: "\f042";
+}
+.fa-tint:before {
+ content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+ content: "\f044";
+}
+.fa-share-square-o:before {
+ content: "\f045";
+}
+.fa-check-square-o:before {
+ content: "\f046";
+}
+.fa-arrows:before {
+ content: "\f047";
+}
+.fa-step-backward:before {
+ content: "\f048";
+}
+.fa-fast-backward:before {
+ content: "\f049";
+}
+.fa-backward:before {
+ content: "\f04a";
+}
+.fa-play:before {
+ content: "\f04b";
+}
+.fa-pause:before {
+ content: "\f04c";
+}
+.fa-stop:before {
+ content: "\f04d";
+}
+.fa-forward:before {
+ content: "\f04e";
+}
+.fa-fast-forward:before {
+ content: "\f050";
+}
+.fa-step-forward:before {
+ content: "\f051";
+}
+.fa-eject:before {
+ content: "\f052";
+}
+.fa-chevron-left:before {
+ content: "\f053";
+}
+.fa-chevron-right:before {
+ content: "\f054";
+}
+.fa-plus-circle:before {
+ content: "\f055";
+}
+.fa-minus-circle:before {
+ content: "\f056";
+}
+.fa-times-circle:before {
+ content: "\f057";
+}
+.fa-check-circle:before {
+ content: "\f058";
+}
+.fa-question-circle:before {
+ content: "\f059";
+}
+.fa-info-circle:before {
+ content: "\f05a";
+}
+.fa-crosshairs:before {
+ content: "\f05b";
+}
+.fa-times-circle-o:before {
+ content: "\f05c";
+}
+.fa-check-circle-o:before {
+ content: "\f05d";
+}
+.fa-ban:before {
+ content: "\f05e";
+}
+.fa-arrow-left:before {
+ content: "\f060";
+}
+.fa-arrow-right:before {
+ content: "\f061";
+}
+.fa-arrow-up:before {
+ content: "\f062";
+}
+.fa-arrow-down:before {
+ content: "\f063";
+}
+.fa-mail-forward:before,
+.fa-share:before {
+ content: "\f064";
+}
+.fa-expand:before {
+ content: "\f065";
+}
+.fa-compress:before {
+ content: "\f066";
+}
+.fa-plus:before {
+ content: "\f067";
+}
+.fa-minus:before {
+ content: "\f068";
+}
+.fa-asterisk:before {
+ content: "\f069";
+}
+.fa-exclamation-circle:before {
+ content: "\f06a";
+}
+.fa-gift:before {
+ content: "\f06b";
+}
+.fa-leaf:before {
+ content: "\f06c";
+}
+.fa-fire:before {
+ content: "\f06d";
+}
+.fa-eye:before {
+ content: "\f06e";
+}
+.fa-eye-slash:before {
+ content: "\f070";
+}
+.fa-warning:before,
+.fa-exclamation-triangle:before {
+ content: "\f071";
+}
+.fa-plane:before {
+ content: "\f072";
+}
+.fa-calendar:before {
+ content: "\f073";
+}
+.fa-random:before {
+ content: "\f074";
+}
+.fa-comment:before {
+ content: "\f075";
+}
+.fa-magnet:before {
+ content: "\f076";
+}
+.fa-chevron-up:before {
+ content: "\f077";
+}
+.fa-chevron-down:before {
+ content: "\f078";
+}
+.fa-retweet:before {
+ content: "\f079";
+}
+.fa-shopping-cart:before {
+ content: "\f07a";
+}
+.fa-folder:before {
+ content: "\f07b";
+}
+.fa-folder-open:before {
+ content: "\f07c";
+}
+.fa-arrows-v:before {
+ content: "\f07d";
+}
+.fa-arrows-h:before {
+ content: "\f07e";
+}
+.fa-bar-chart-o:before,
+.fa-bar-chart:before {
+ content: "\f080";
+}
+.fa-twitter-square:before {
+ content: "\f081";
+}
+.fa-facebook-square:before {
+ content: "\f082";
+}
+.fa-camera-retro:before {
+ content: "\f083";
+}
+.fa-key:before {
+ content: "\f084";
+}
+.fa-gears:before,
+.fa-cogs:before {
+ content: "\f085";
+}
+.fa-comments:before {
+ content: "\f086";
+}
+.fa-thumbs-o-up:before {
+ content: "\f087";
+}
+.fa-thumbs-o-down:before {
+ content: "\f088";
+}
+.fa-star-half:before {
+ content: "\f089";
+}
+.fa-heart-o:before {
+ content: "\f08a";
+}
+.fa-sign-out:before {
+ content: "\f08b";
+}
+.fa-linkedin-square:before {
+ content: "\f08c";
+}
+.fa-thumb-tack:before {
+ content: "\f08d";
+}
+.fa-external-link:before {
+ content: "\f08e";
+}
+.fa-sign-in:before {
+ content: "\f090";
+}
+.fa-trophy:before {
+ content: "\f091";
+}
+.fa-github-square:before {
+ content: "\f092";
+}
+.fa-upload:before {
+ content: "\f093";
+}
+.fa-lemon-o:before {
+ content: "\f094";
+}
+.fa-phone:before {
+ content: "\f095";
+}
+.fa-square-o:before {
+ content: "\f096";
+}
+.fa-bookmark-o:before {
+ content: "\f097";
+}
+.fa-phone-square:before {
+ content: "\f098";
+}
+.fa-twitter:before {
+ content: "\f099";
+}
+.fa-facebook-f:before,
+.fa-facebook:before {
+ content: "\f09a";
+}
+.fa-github:before {
+ content: "\f09b";
+}
+.fa-unlock:before {
+ content: "\f09c";
+}
+.fa-credit-card:before {
+ content: "\f09d";
+}
+.fa-feed:before,
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+ content: "\f1fb";
+}
+.fa-paint-brush:before {
+ content: "\f1fc";
+}
+.fa-birthday-cake:before {
+ content: "\f1fd";
+}
+.fa-area-chart:before {
+ content: "\f1fe";
+}
+.fa-pie-chart:before {
+ content: "\f200";
+}
+.fa-line-chart:before {
+ content: "\f201";
+}
+.fa-lastfm:before {
+ content: "\f202";
+}
+.fa-lastfm-square:before {
+ content: "\f203";
+}
+.fa-toggle-off:before {
+ content: "\f204";
+}
+.fa-toggle-on:before {
+ content: "\f205";
+}
+.fa-bicycle:before {
+ content: "\f206";
+}
+.fa-bus:before {
+ content: "\f207";
+}
+.fa-ioxhost:before {
+ content: "\f208";
+}
+.fa-angellist:before {
+ content: "\f209";
+}
+.fa-cc:before {
+ content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+ content: "\f20b";
+}
+.fa-meanpath:before {
+ content: "\f20c";
+}
+.fa-buysellads:before {
+ content: "\f20d";
+}
+.fa-connectdevelop:before {
+ content: "\f20e";
+}
+.fa-dashcube:before {
+ content: "\f210";
+}
+.fa-forumbee:before {
+ content: "\f211";
+}
+.fa-leanpub:before {
+ content: "\f212";
+}
+.fa-sellsy:before {
+ content: "\f213";
+}
+.fa-shirtsinbulk:before {
+ content: "\f214";
+}
+.fa-simplybuilt:before {
+ content: "\f215";
+}
+.fa-skyatlas:before {
+ content: "\f216";
+}
+.fa-cart-plus:before {
+ content: "\f217";
+}
+.fa-cart-arrow-down:before {
+ content: "\f218";
+}
+.fa-diamond:before {
+ content: "\f219";
+}
+.fa-ship:before {
+ content: "\f21a";
+}
+.fa-user-secret:before {
+ content: "\f21b";
+}
+.fa-motorcycle:before {
+ content: "\f21c";
+}
+.fa-street-view:before {
+ content: "\f21d";
+}
+.fa-heartbeat:before {
+ content: "\f21e";
+}
+.fa-venus:before {
+ content: "\f221";
+}
+.fa-mars:before {
+ content: "\f222";
+}
+.fa-mercury:before {
+ content: "\f223";
+}
+.fa-intersex:before,
+.fa-transgender:before {
+ content: "\f224";
+}
+.fa-transgender-alt:before {
+ content: "\f225";
+}
+.fa-venus-double:before {
+ content: "\f226";
+}
+.fa-mars-double:before {
+ content: "\f227";
+}
+.fa-venus-mars:before {
+ content: "\f228";
+}
+.fa-mars-stroke:before {
+ content: "\f229";
+}
+.fa-mars-stroke-v:before {
+ content: "\f22a";
+}
+.fa-mars-stroke-h:before {
+ content: "\f22b";
+}
+.fa-neuter:before {
+ content: "\f22c";
+}
+.fa-genderless:before {
+ content: "\f22d";
+}
+.fa-facebook-official:before {
+ content: "\f230";
+}
+.fa-pinterest-p:before {
+ content: "\f231";
+}
+.fa-whatsapp:before {
+ content: "\f232";
+}
+.fa-server:before {
+ content: "\f233";
+}
+.fa-user-plus:before {
+ content: "\f234";
+}
+.fa-user-times:before {
+ content: "\f235";
+}
+.fa-hotel:before,
+.fa-bed:before {
+ content: "\f236";
+}
+.fa-viacoin:before {
+ content: "\f237";
+}
+.fa-train:before {
+ content: "\f238";
+}
+.fa-subway:before {
+ content: "\f239";
+}
+.fa-medium:before {
+ content: "\f23a";
+}
+.fa-yc:before,
+.fa-y-combinator:before {
+ content: "\f23b";
+}
+.fa-optin-monster:before {
+ content: "\f23c";
+}
+.fa-opencart:before {
+ content: "\f23d";
+}
+.fa-expeditedssl:before {
+ content: "\f23e";
+}
+.fa-battery-4:before,
+.fa-battery:before,
+.fa-battery-full:before {
+ content: "\f240";
+}
+.fa-battery-3:before,
+.fa-battery-three-quarters:before {
+ content: "\f241";
+}
+.fa-battery-2:before,
+.fa-battery-half:before {
+ content: "\f242";
+}
+.fa-battery-1:before,
+.fa-battery-quarter:before {
+ content: "\f243";
+}
+.fa-battery-0:before,
+.fa-battery-empty:before {
+ content: "\f244";
+}
+.fa-mouse-pointer:before {
+ content: "\f245";
+}
+.fa-i-cursor:before {
+ content: "\f246";
+}
+.fa-object-group:before {
+ content: "\f247";
+}
+.fa-object-ungroup:before {
+ content: "\f248";
+}
+.fa-sticky-note:before {
+ content: "\f249";
+}
+.fa-sticky-note-o:before {
+ content: "\f24a";
+}
+.fa-cc-jcb:before {
+ content: "\f24b";
+}
+.fa-cc-diners-club:before {
+ content: "\f24c";
+}
+.fa-clone:before {
+ content: "\f24d";
+}
+.fa-balance-scale:before {
+ content: "\f24e";
+}
+.fa-hourglass-o:before {
+ content: "\f250";
+}
+.fa-hourglass-1:before,
+.fa-hourglass-start:before {
+ content: "\f251";
+}
+.fa-hourglass-2:before,
+.fa-hourglass-half:before {
+ content: "\f252";
+}
+.fa-hourglass-3:before,
+.fa-hourglass-end:before {
+ content: "\f253";
+}
+.fa-hourglass:before {
+ content: "\f254";
+}
+.fa-hand-grab-o:before,
+.fa-hand-rock-o:before {
+ content: "\f255";
+}
+.fa-hand-stop-o:before,
+.fa-hand-paper-o:before {
+ content: "\f256";
+}
+.fa-hand-scissors-o:before {
+ content: "\f257";
+}
+.fa-hand-lizard-o:before {
+ content: "\f258";
+}
+.fa-hand-spock-o:before {
+ content: "\f259";
+}
+.fa-hand-pointer-o:before {
+ content: "\f25a";
+}
+.fa-hand-peace-o:before {
+ content: "\f25b";
+}
+.fa-trademark:before {
+ content: "\f25c";
+}
+.fa-registered:before {
+ content: "\f25d";
+}
+.fa-creative-commons:before {
+ content: "\f25e";
+}
+.fa-gg:before {
+ content: "\f260";
+}
+.fa-gg-circle:before {
+ content: "\f261";
+}
+.fa-tripadvisor:before {
+ content: "\f262";
+}
+.fa-odnoklassniki:before {
+ content: "\f263";
+}
+.fa-odnoklassniki-square:before {
+ content: "\f264";
+}
+.fa-get-pocket:before {
+ content: "\f265";
+}
+.fa-wikipedia-w:before {
+ content: "\f266";
+}
+.fa-safari:before {
+ content: "\f267";
+}
+.fa-chrome:before {
+ content: "\f268";
+}
+.fa-firefox:before {
+ content: "\f269";
+}
+.fa-opera:before {
+ content: "\f26a";
+}
+.fa-internet-explorer:before {
+ content: "\f26b";
+}
+.fa-tv:before,
+.fa-television:before {
+ content: "\f26c";
+}
+.fa-contao:before {
+ content: "\f26d";
+}
+.fa-500px:before {
+ content: "\f26e";
+}
+.fa-amazon:before {
+ content: "\f270";
+}
+.fa-calendar-plus-o:before {
+ content: "\f271";
+}
+.fa-calendar-minus-o:before {
+ content: "\f272";
+}
+.fa-calendar-times-o:before {
+ content: "\f273";
+}
+.fa-calendar-check-o:before {
+ content: "\f274";
+}
+.fa-industry:before {
+ content: "\f275";
+}
+.fa-map-pin:before {
+ content: "\f276";
+}
+.fa-map-signs:before {
+ content: "\f277";
+}
+.fa-map-o:before {
+ content: "\f278";
+}
+.fa-map:before {
+ content: "\f279";
+}
+.fa-commenting:before {
+ content: "\f27a";
+}
+.fa-commenting-o:before {
+ content: "\f27b";
+}
+.fa-houzz:before {
+ content: "\f27c";
+}
+.fa-vimeo:before {
+ content: "\f27d";
+}
+.fa-black-tie:before {
+ content: "\f27e";
+}
+.fa-fonticons:before {
+ content: "\f280";
+}
+.fa-reddit-alien:before {
+ content: "\f281";
+}
+.fa-edge:before {
+ content: "\f282";
+}
+.fa-credit-card-alt:before {
+ content: "\f283";
+}
+.fa-codiepie:before {
+ content: "\f284";
+}
+.fa-modx:before {
+ content: "\f285";
+}
+.fa-fort-awesome:before {
+ content: "\f286";
+}
+.fa-usb:before {
+ content: "\f287";
+}
+.fa-product-hunt:before {
+ content: "\f288";
+}
+.fa-mixcloud:before {
+ content: "\f289";
+}
+.fa-scribd:before {
+ content: "\f28a";
+}
+.fa-pause-circle:before {
+ content: "\f28b";
+}
+.fa-pause-circle-o:before {
+ content: "\f28c";
+}
+.fa-stop-circle:before {
+ content: "\f28d";
+}
+.fa-stop-circle-o:before {
+ content: "\f28e";
+}
+.fa-shopping-bag:before {
+ content: "\f290";
+}
+.fa-shopping-basket:before {
+ content: "\f291";
+}
+.fa-hashtag:before {
+ content: "\f292";
+}
+.fa-bluetooth:before {
+ content: "\f293";
+}
+.fa-bluetooth-b:before {
+ content: "\f294";
+}
+.fa-percent:before {
+ content: "\f295";
+}
+.fa-gitlab:before {
+ content: "\f296";
+}
+.fa-wpbeginner:before {
+ content: "\f297";
+}
+.fa-wpforms:before {
+ content: "\f298";
+}
+.fa-envira:before {
+ content: "\f299";
+}
+.fa-universal-access:before {
+ content: "\f29a";
+}
+.fa-wheelchair-alt:before {
+ content: "\f29b";
+}
+.fa-question-circle-o:before {
+ content: "\f29c";
+}
+.fa-blind:before {
+ content: "\f29d";
+}
+.fa-audio-description:before {
+ content: "\f29e";
+}
+.fa-volume-control-phone:before {
+ content: "\f2a0";
+}
+.fa-braille:before {
+ content: "\f2a1";
+}
+.fa-assistive-listening-systems:before {
+ content: "\f2a2";
+}
+.fa-asl-interpreting:before,
+.fa-american-sign-language-interpreting:before {
+ content: "\f2a3";
+}
+.fa-deafness:before,
+.fa-hard-of-hearing:before,
+.fa-deaf:before {
+ content: "\f2a4";
+}
+.fa-glide:before {
+ content: "\f2a5";
+}
+.fa-glide-g:before {
+ content: "\f2a6";
+}
+.fa-signing:before,
+.fa-sign-language:before {
+ content: "\f2a7";
+}
+.fa-low-vision:before {
+ content: "\f2a8";
+}
+.fa-viadeo:before {
+ content: "\f2a9";
+}
+.fa-viadeo-square:before {
+ content: "\f2aa";
+}
+.fa-snapchat:before {
+ content: "\f2ab";
+}
+.fa-snapchat-ghost:before {
+ content: "\f2ac";
+}
+.fa-snapchat-square:before {
+ content: "\f2ad";
+}
+.fa-pied-piper:before {
+ content: "\f2ae";
+}
+.fa-first-order:before {
+ content: "\f2b0";
+}
+.fa-yoast:before {
+ content: "\f2b1";
+}
+.fa-themeisle:before {
+ content: "\f2b2";
+}
+.fa-google-plus-circle:before,
+.fa-google-plus-official:before {
+ content: "\f2b3";
+}
+.fa-fa:before,
+.fa-font-awesome:before {
+ content: "\f2b4";
+}
+.fa-handshake-o:before {
+ content: "\f2b5";
+}
+.fa-envelope-open:before {
+ content: "\f2b6";
+}
+.fa-envelope-open-o:before {
+ content: "\f2b7";
+}
+.fa-linode:before {
+ content: "\f2b8";
+}
+.fa-address-book:before {
+ content: "\f2b9";
+}
+.fa-address-book-o:before {
+ content: "\f2ba";
+}
+.fa-vcard:before,
+.fa-address-card:before {
+ content: "\f2bb";
+}
+.fa-vcard-o:before,
+.fa-address-card-o:before {
+ content: "\f2bc";
+}
+.fa-user-circle:before {
+ content: "\f2bd";
+}
+.fa-user-circle-o:before {
+ content: "\f2be";
+}
+.fa-user-o:before {
+ content: "\f2c0";
+}
+.fa-id-badge:before {
+ content: "\f2c1";
+}
+.fa-drivers-license:before,
+.fa-id-card:before {
+ content: "\f2c2";
+}
+.fa-drivers-license-o:before,
+.fa-id-card-o:before {
+ content: "\f2c3";
+}
+.fa-quora:before {
+ content: "\f2c4";
+}
+.fa-free-code-camp:before {
+ content: "\f2c5";
+}
+.fa-telegram:before {
+ content: "\f2c6";
+}
+.fa-thermometer-4:before,
+.fa-thermometer:before,
+.fa-thermometer-full:before {
+ content: "\f2c7";
+}
+.fa-thermometer-3:before,
+.fa-thermometer-three-quarters:before {
+ content: "\f2c8";
+}
+.fa-thermometer-2:before,
+.fa-thermometer-half:before {
+ content: "\f2c9";
+}
+.fa-thermometer-1:before,
+.fa-thermometer-quarter:before {
+ content: "\f2ca";
+}
+.fa-thermometer-0:before,
+.fa-thermometer-empty:before {
+ content: "\f2cb";
+}
+.fa-shower:before {
+ content: "\f2cc";
+}
+.fa-bathtub:before,
+.fa-s15:before,
+.fa-bath:before {
+ content: "\f2cd";
+}
+.fa-podcast:before {
+ content: "\f2ce";
+}
+.fa-window-maximize:before {
+ content: "\f2d0";
+}
+.fa-window-minimize:before {
+ content: "\f2d1";
+}
+.fa-window-restore:before {
+ content: "\f2d2";
+}
+.fa-times-rectangle:before,
+.fa-window-close:before {
+ content: "\f2d3";
+}
+.fa-times-rectangle-o:before,
+.fa-window-close-o:before {
+ content: "\f2d4";
+}
+.fa-bandcamp:before {
+ content: "\f2d5";
+}
+.fa-grav:before {
+ content: "\f2d6";
+}
+.fa-etsy:before {
+ content: "\f2d7";
+}
+.fa-imdb:before {
+ content: "\f2d8";
+}
+.fa-ravelry:before {
+ content: "\f2d9";
+}
+.fa-eercast:before {
+ content: "\f2da";
+}
+.fa-microchip:before {
+ content: "\f2db";
+}
+.fa-snowflake-o:before {
+ content: "\f2dc";
+}
+.fa-superpowers:before {
+ content: "\f2dd";
+}
+.fa-wpexplorer:before {
+ content: "\f2de";
+}
+.fa-meetup:before {
+ content: "\f2e0";
+}
+.sr-only {
+ position: absolute;
+ width: 1px;
+ height: 1px;
+ padding: 0;
+ margin: -1px;
+ overflow: hidden;
+ clip: rect(0, 0, 0, 0);
+ border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+ position: static;
+ width: auto;
+ height: auto;
+ margin: 0;
+ overflow: visible;
+ clip: auto;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+ position: static;
+ width: auto;
+ height: auto;
+ margin: 0;
+ overflow: visible;
+ clip: auto;
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+ -webkit-transform: translate(0, 0);
+ -ms-transform: translate(0, 0);
+ -o-transform: translate(0, 0);
+ transform: translate(0, 0);
+}
+code {
+ color: #000000;
+}
+pre {
+ font-size: inherit;
+ line-height: inherit;
+}
+label {
+ font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+.corner-all {
+ border-radius: 2px;
+}
+.no-padding {
+ padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer. It allows the usage of flexible box
+model layouts accross multiple browsers, including older browsers. The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below). Browsers that are known to implement this
+new spec completely include:
+
+ Firefox 28.0+
+ Chrome 29.0+
+ Internet Explorer 11+
+ Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+}
+.hbox > * {
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+.vbox {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+}
+.vbox > * {
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+ /* Old browsers */
+ -webkit-box-direction: reverse;
+ -moz-box-direction: reverse;
+ box-direction: reverse;
+ /* Modern browsers */
+ flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+ width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+ /* Old browsers */
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+ /* Old browsers */
+ -webkit-box-flex: 2;
+ -moz-box-flex: 2;
+ box-flex: 2;
+ /* Modern browsers */
+ flex: 2;
+}
+.box-group1 {
+ /* Deprecated */
+ -webkit-box-flex-group: 1;
+ -moz-box-flex-group: 1;
+ box-flex-group: 1;
+}
+.box-group2 {
+ /* Deprecated */
+ -webkit-box-flex-group: 2;
+ -moz-box-flex-group: 2;
+ box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+ /* Old browsers */
+ -webkit-box-pack: start;
+ -moz-box-pack: start;
+ box-pack: start;
+ /* Modern browsers */
+ justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+ /* Old browsers */
+ -webkit-box-pack: end;
+ -moz-box-pack: end;
+ box-pack: end;
+ /* Modern browsers */
+ justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+ /* Old browsers */
+ -webkit-box-pack: center;
+ -moz-box-pack: center;
+ box-pack: center;
+ /* Modern browsers */
+ justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+ /* Old browsers */
+ -webkit-box-pack: baseline;
+ -moz-box-pack: baseline;
+ box-pack: baseline;
+ /* Modern browsers */
+ justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+ /* Old browsers */
+ -webkit-box-pack: stretch;
+ -moz-box-pack: stretch;
+ box-pack: stretch;
+ /* Modern browsers */
+ justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+ /* Old browsers */
+ -webkit-box-align: start;
+ -moz-box-align: start;
+ box-align: start;
+ /* Modern browsers */
+ align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+ /* Old browsers */
+ -webkit-box-align: end;
+ -moz-box-align: end;
+ box-align: end;
+ /* Modern browsers */
+ align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+ /* Old browsers */
+ -webkit-box-align: center;
+ -moz-box-align: center;
+ box-align: center;
+ /* Modern browsers */
+ align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+ /* Old browsers */
+ -webkit-box-align: baseline;
+ -moz-box-align: baseline;
+ box-align: baseline;
+ /* Modern browsers */
+ align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+ /* Old browsers */
+ -webkit-box-align: stretch;
+ -moz-box-align: stretch;
+ box-align: stretch;
+ /* Modern browsers */
+ align-items: stretch;
+}
+div.error {
+ margin: 2em;
+ text-align: center;
+}
+div.error > h1 {
+ font-size: 500%;
+ line-height: normal;
+}
+div.error > p {
+ font-size: 200%;
+ line-height: normal;
+}
+div.traceback-wrapper {
+ text-align: left;
+ max-width: 800px;
+ margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+ background-color: #ffffff;
+ /* This makes sure that the body covers the entire window and needs to
+ be in a different element than the display: box in wrapper below */
+ position: absolute;
+ left: 0px;
+ right: 0px;
+ top: 0px;
+ bottom: 0px;
+ overflow: visible;
+}
+body > #header {
+ /* Initially hidden to prevent FLOUC */
+ display: none;
+ background-color: #ffffff;
+ /* Display over codemirror */
+ position: relative;
+ z-index: 100;
+}
+body > #header #header-container {
+ padding-bottom: 5px;
+ padding-top: 5px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+ width: 100%;
+ height: 1px;
+ background: #e7e7e7;
+ margin-bottom: -1px;
+}
+@media print {
+ body > #header {
+ display: none !important;
+ }
+}
+#header-spacer {
+ width: 100%;
+ visibility: hidden;
+}
+@media print {
+ #header-spacer {
+ display: none;
+ }
+}
+#ipython_notebook {
+ padding-left: 0px;
+ padding-top: 1px;
+ padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+ #ipython_notebook {
+ margin-left: 10px;
+ }
+}
+#noscript {
+ width: auto;
+ padding-top: 16px;
+ padding-bottom: 16px;
+ text-align: center;
+ font-size: 22px;
+ color: red;
+ font-weight: bold;
+}
+#ipython_notebook img {
+ height: 28px;
+}
+#site {
+ width: 100%;
+ display: none;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ overflow: auto;
+}
+@media print {
+ #site {
+ height: auto !important;
+ }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+ padding: 0.2em 0.8em;
+ font-size: 77%;
+}
+input.ui-button {
+ padding: 0.3em 0.9em;
+}
+span#login_widget {
+ float: right;
+}
+span#login_widget > .button,
+#logout {
+ color: #333333;
+ background-color: #ffffff;
+ border-color: #cccccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+ color: #333333;
+ background-color: #e6e6e6;
+ border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+ color: #333333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+ color: #333333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+ color: #333333;
+ background-color: #d4d4d4;
+ border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+ background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+ background-color: #ffffff;
+ border-color: #cccccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+ color: #ffffff;
+ background-color: #333333;
+}
+.nav-header {
+ text-transform: none;
+}
+#header > span {
+ margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+ min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+ max-height: calc(100vh - 200px);
+ overflow: auto;
+ flex: 1;
+}
+@media (min-width: 768px) {
+ .modal .modal-dialog {
+ width: 700px;
+ }
+}
+@media (min-width: 768px) {
+ select.form-control {
+ margin-left: 12px;
+ margin-right: 12px;
+ }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+ display: inline-block;
+ margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+ background-color: none;
+ display: inline;
+}
+.alternate_upload.form {
+ padding: 0;
+ margin: 0;
+}
+.alternate_upload input.fileinput {
+ text-align: center;
+ vertical-align: middle;
+ display: inline;
+ opacity: 0;
+ z-index: 2;
+ width: 12ex;
+ margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+ height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+ul#tabs {
+ margin-bottom: 4px;
+}
+ul#tabs a {
+ padding-top: 6px;
+ padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+ text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+ font-size: 16px;
+ margin-right: 4px;
+}
+ul.breadcrumb span {
+ color: #5e5e5e;
+}
+.list_toolbar {
+ padding: 4px 0 4px 0;
+ vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+ padding-top: 1px;
+}
+.dynamic-buttons {
+ padding-top: 3px;
+ display: inline-block;
+}
+.list_toolbar [class*="span"] {
+ min-height: 24px;
+}
+.list_header {
+ font-weight: bold;
+ background-color: #eeeeee;
+}
+.list_placeholder {
+ font-weight: bold;
+ padding-top: 4px;
+ padding-bottom: 4px;
+ padding-left: 7px;
+ padding-right: 7px;
+}
+.list_container {
+ margin-top: 4px;
+ margin-bottom: 20px;
+ border: 1px solid #dddddd;
+ border-radius: 2px;
+}
+.list_container > div {
+ border-bottom: 1px solid #dddddd;
+}
+.list_container > div:hover .list-item {
+ background-color: red;
+}
+.list_container > div:last-child {
+ border: none;
+}
+.list_item:hover .list_item {
+ background-color: #dddddd;
+}
+.list_item a {
+ text-decoration: none;
+}
+.list_item:hover {
+ background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+ padding-top: 4px;
+ padding-bottom: 4px;
+ padding-left: 7px;
+ padding-right: 7px;
+ line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+ margin-right: 7px;
+ margin-left: 14px;
+ vertical-align: baseline;
+ line-height: 22px;
+ position: relative;
+ top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+ margin-left: -1px;
+ vertical-align: baseline;
+ line-height: 22px;
+}
+.new-file input[type=checkbox] {
+ visibility: hidden;
+}
+.item_name {
+ line-height: 22px;
+ height: 24px;
+}
+.item_icon {
+ font-size: 14px;
+ color: #5e5e5e;
+ margin-right: 7px;
+ margin-left: 7px;
+ line-height: 22px;
+ vertical-align: baseline;
+}
+.item_buttons {
+ line-height: 1em;
+ margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+ float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+ margin-left: 5px;
+}
+.item_buttons .btn {
+ min-width: 13ex;
+}
+.item_buttons .running-indicator {
+ padding-top: 4px;
+ color: #5cb85c;
+}
+.item_buttons .kernel-name {
+ padding-top: 4px;
+ color: #5bc0de;
+ margin-right: 7px;
+ float: left;
+}
+.toolbar_info {
+ height: 24px;
+ line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+ padding-top: 3px;
+ padding-bottom: 3px;
+ height: 22px;
+ line-height: 14px;
+ margin: 0px;
+}
+.highlight_text {
+ color: blue;
+}
+#project_name {
+ display: inline-block;
+ padding-left: 7px;
+ margin-left: -2px;
+}
+#project_name > .breadcrumb {
+ padding: 0px;
+ margin-bottom: 0px;
+ background-color: transparent;
+ font-weight: bold;
+}
+#tree-selector {
+ padding-right: 0px;
+}
+#button-select-all {
+ min-width: 50px;
+}
+#select-all {
+ margin-left: 7px;
+ margin-right: 2px;
+}
+.menu_icon {
+ margin-right: 2px;
+}
+.tab-content .row {
+ margin-left: 0px;
+ margin-right: 0px;
+}
+.folder_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f114";
+}
+.folder_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.folder_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.folder_icon:before.pull-left {
+ margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+ margin-left: .3em;
+}
+.notebook_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f02d";
+ position: relative;
+ top: -1px;
+}
+.notebook_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.notebook_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.notebook_icon:before.pull-left {
+ margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+ margin-left: .3em;
+}
+.running_notebook_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f02d";
+ position: relative;
+ top: -1px;
+ color: #5cb85c;
+}
+.running_notebook_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.running_notebook_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.running_notebook_icon:before.pull-left {
+ margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+ margin-left: .3em;
+}
+.file_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f016";
+ position: relative;
+ top: -2px;
+}
+.file_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.file_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.file_icon:before.pull-left {
+ margin-right: .3em;
+}
+.file_icon:before.pull-right {
+ margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+ padding-top: 0px;
+ margin-right: -1px;
+}
+ul#new-menu {
+ left: auto;
+ right: 0;
+}
+.kernel-menu-icon {
+ padding-right: 12px;
+ width: 24px;
+ content: "\f096";
+}
+.kernel-menu-icon:before {
+ content: "\f096";
+}
+.kernel-menu-icon-current:before {
+ content: "\f00c";
+}
+#tab_content {
+ padding-top: 20px;
+}
+#running .panel-group .panel {
+ margin-top: 3px;
+ margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+ background-color: #eeeeee;
+ padding-top: 4px;
+ padding-bottom: 4px;
+ padding-left: 7px;
+ padding-right: 7px;
+ line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+ text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+ padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+ margin-top: 0px;
+ margin-bottom: 0px;
+ border: 0px;
+ border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+ border-bottom: 1px solid #dddddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+ border-bottom: 0px;
+}
+.delete-button {
+ display: none;
+}
+.duplicate-button {
+ display: none;
+}
+.rename-button {
+ display: none;
+}
+.shutdown-button {
+ display: none;
+}
+.dynamic-instructions {
+ display: inline-block;
+ padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+ padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+ content: "\f00c";
+}
+#mode-menu {
+ overflow: auto;
+ max-height: 20em;
+}
+.edit_app #header {
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+ /* Use a negative 1 bottom margin, so the border overlaps the border of the
+ header */
+ margin-bottom: -1px;
+}
+.dirty-indicator {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ width: 20px;
+}
+.dirty-indicator.fa-pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator.fa-pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-dirty {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ width: 20px;
+}
+.dirty-indicator-dirty.fa-pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-dirty.fa-pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-dirty.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-clean {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ width: 20px;
+}
+.dirty-indicator-clean.fa-pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-clean.fa-pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-clean.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f00c";
+}
+.dirty-indicator-clean:before.fa-pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-clean:before.fa-pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-clean:before.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+ margin-left: .3em;
+}
+#filename {
+ font-size: 16pt;
+ display: table;
+ padding: 0px 5px;
+}
+#current-mode {
+ padding-left: 5px;
+ padding-right: 5px;
+}
+#texteditor-backdrop {
+ padding-top: 20px;
+ padding-bottom: 20px;
+}
+@media not print {
+ #texteditor-backdrop {
+ background-color: #eeeeee;
+ }
+}
+@media print {
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+ background-color: #ffffff;
+ }
+}
+@media not print {
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+ background-color: #ffffff;
+ }
+}
+@media not print {
+ #texteditor-backdrop #texteditor-container {
+ padding: 0px;
+ background-color: #ffffff;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+ font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+ color: black;
+}
+.ansired {
+ color: darkred;
+}
+.ansigreen {
+ color: darkgreen;
+}
+.ansiyellow {
+ color: #c4a000;
+}
+.ansiblue {
+ color: darkblue;
+}
+.ansipurple {
+ color: darkviolet;
+}
+.ansicyan {
+ color: steelblue;
+}
+.ansigray {
+ color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+ background-color: black;
+}
+.ansibgred {
+ background-color: red;
+}
+.ansibggreen {
+ background-color: green;
+}
+.ansibgyellow {
+ background-color: yellow;
+}
+.ansibgblue {
+ background-color: blue;
+}
+.ansibgpurple {
+ background-color: magenta;
+}
+.ansibgcyan {
+ background-color: cyan;
+}
+.ansibggray {
+ background-color: gray;
+}
+div.cell {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+ border-radius: 2px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ border-width: 1px;
+ border-style: solid;
+ border-color: transparent;
+ width: 100%;
+ padding: 5px;
+ /* This acts as a spacer between cells, that is outside the border */
+ margin: 0px;
+ outline: none;
+ border-left-width: 1px;
+ padding-left: 5px;
+ background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+ border-left-color: #90caf9;
+ border-left-color: #e3f2fd;
+ border-left-width: 1px;
+ padding-left: 5px;
+ border-right-color: #e3f2fd;
+ border-right-width: 1px;
+ background: #e3f2fd;
+}
+@media print {
+ div.cell.jupyter-soft-selected {
+ border-color: transparent;
+ }
+}
+div.cell.selected {
+ border-color: #ababab;
+ border-left-width: 0px;
+ padding-left: 6px;
+ background: linear-gradient(to right, #42a5f5 -40px, #42a5f5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+ div.cell.selected {
+ border-color: transparent;
+ }
+}
+div.cell.selected.jupyter-soft-selected {
+ border-left-width: 0;
+ padding-left: 6px;
+ background: linear-gradient(to right, #42a5f5 -40px, #42a5f5 7px, #e3f2fd 7px, #e3f2fd 100%);
+}
+.edit_mode div.cell.selected {
+ border-color: #66bb6a;
+ border-left-width: 0px;
+ padding-left: 6px;
+ background: linear-gradient(to right, #66bb6a -40px, #66bb6a 5px, transparent 5px, transparent 100%);
+}
+@media print {
+ .edit_mode div.cell.selected {
+ border-color: transparent;
+ }
+}
+.prompt {
+ /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+ min-width: 14ex;
+ /* This padding is tuned to match the padding on the CodeMirror editor. */
+ padding: 0.4em;
+ margin: 0px;
+ font-family: monospace;
+ text-align: right;
+ /* This has to match that of the the CodeMirror class line-height below */
+ line-height: 1.21429em;
+ /* Don't highlight prompt number selection */
+ -webkit-touch-callout: none;
+ -webkit-user-select: none;
+ -khtml-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+ /* Use default cursor */
+ cursor: default;
+}
+@media (max-width: 540px) {
+ .prompt {
+ text-align: left;
+ }
+}
+div.inner_cell {
+ min-width: 0;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+ border: 1px solid #cfcfcf;
+ border-radius: 2px;
+ background: #f7f7f7;
+ line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+ is no content in the output_subarea and the prompt. The main purpose of this is
+ to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+ padding-top: 0;
+ padding-bottom: 0;
+}
+div.unrecognized_cell {
+ padding: 5px 5px 5px 0px;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+div.unrecognized_cell .inner_cell {
+ border-radius: 2px;
+ padding: 5px;
+ font-weight: bold;
+ color: red;
+ border: 1px solid #cfcfcf;
+ background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+ color: inherit;
+ text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+ color: inherit;
+ text-decoration: none;
+}
+@media (max-width: 540px) {
+ div.unrecognized_cell > div.prompt {
+ display: none;
+ }
+}
+div.code_cell {
+ /* avoid page breaking on code cells when printing */
+}
+@media print {
+ div.code_cell {
+ page-break-inside: avoid;
+ }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+ page-break-inside: avoid;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+@media (max-width: 540px) {
+ div.input {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+ }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+ color: #303f9f;
+ border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+ margin: 0.4em;
+ border: none;
+ padding: 0px;
+ background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+ margin: 0px;
+ border: none;
+ padding: 0px;
+ background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height. See
+ * notebookmain.js. Such fonts will have keywords vertically offset with
+ * respect to the rest of the text. The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ * vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+ line-height: 1.21429em;
+ /* Changed from 1em to our global default */
+ font-size: 14px;
+ height: auto;
+ /* Changed to auto to autogrow */
+ background: none;
+ /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+ /* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+ /* We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+ overflow-y: hidden;
+ overflow-x: auto;
+}
+.CodeMirror-lines {
+ /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+ /* we have set a different line-height and want this to scale with that. */
+ padding: 0.4em;
+}
+.CodeMirror-linenumber {
+ padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+ border-bottom-left-radius: 2px;
+ border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+ /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+ /* .CodeMirror-lines */
+ padding: 0;
+ border: 0;
+ border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+ color: #000000;
+}
+.highlight-variable {
+ color: #000000;
+}
+.highlight-variable-2 {
+ color: #1a1a1a;
+}
+.highlight-variable-3 {
+ color: #333333;
+}
+.highlight-string {
+ color: #BA2121;
+}
+.highlight-comment {
+ color: #408080;
+ font-style: italic;
+}
+.highlight-number {
+ color: #080;
+}
+.highlight-atom {
+ color: #88F;
+}
+.highlight-keyword {
+ color: #008000;
+ font-weight: bold;
+}
+.highlight-builtin {
+ color: #008000;
+}
+.highlight-error {
+ color: #f00;
+}
+.highlight-operator {
+ color: #AA22FF;
+ font-weight: bold;
+}
+.highlight-meta {
+ color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+ color: #00f;
+}
+.highlight-string-2 {
+ color: #f50;
+}
+.highlight-qualifier {
+ color: #555;
+}
+.highlight-bracket {
+ color: #997;
+}
+.highlight-tag {
+ color: #170;
+}
+.highlight-attribute {
+ color: #00c;
+}
+.highlight-header {
+ color: blue;
+}
+.highlight-quote {
+ color: #090;
+}
+.highlight-link {
+ color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+ color: #008000;
+ font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+ color: #88F;
+}
+.cm-s-ipython span.cm-number {
+ color: #080;
+}
+.cm-s-ipython span.cm-def {
+ color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+ color: #000000;
+}
+.cm-s-ipython span.cm-operator {
+ color: #AA22FF;
+ font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+ color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+ color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+ color: #408080;
+ font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+ color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+ color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+ color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+ color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+ color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+ color: #997;
+}
+.cm-s-ipython span.cm-tag {
+ color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+ color: #00c;
+}
+.cm-s-ipython span.cm-header {
+ color: blue;
+}
+.cm-s-ipython span.cm-quote {
+ color: #090;
+}
+.cm-s-ipython span.cm-link {
+ color: #00c;
+}
+.cm-s-ipython span.cm-error {
+ color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+ background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+ background-position: right;
+ background-repeat: no-repeat;
+}
+div.output_wrapper {
+ /* this position must be relative to enable descendents to be absolute within it */
+ position: relative;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+ z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+ /* ideally, this would be max-height, but FF barfs all over that */
+ height: 24em;
+ /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+ width: 100%;
+ overflow: auto;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+ box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+ display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+ margin: 0px;
+ padding: 0px;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+div.out_prompt_overlay {
+ height: 100%;
+ padding: 0px 0.4em;
+ position: absolute;
+ border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+ /* use inner shadow to get border that is computed the same on WebKit/FF */
+ -webkit-box-shadow: inset 0 0 1px #000000;
+ box-shadow: inset 0 0 1px #000000;
+ background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+ color: #d84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+ padding: 0px;
+ page-break-inside: avoid;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+div.output_area .MathJax_Display {
+ text-align: left !important;
+}
+div.output_area .rendered_html table {
+ margin-left: 0;
+ margin-right: 0;
+}
+div.output_area .rendered_html img {
+ margin-left: 0;
+ margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+ max-width: 100%;
+ height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+ max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+ as that of bootstrap */
+.output {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+@media (max-width: 540px) {
+ div.output_area {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+ }
+}
+div.output_area pre {
+ margin: 0;
+ padding: 0;
+ border: 0;
+ vertical-align: baseline;
+ color: #000000;
+ background-color: transparent;
+ border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+ the prompt div. */
+div.output_subarea {
+ overflow-x: auto;
+ padding: 0.4em;
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+ max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+ overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+ output types */
+/* all text output has this class: */
+div.output_text {
+ text-align: left;
+ color: #000000;
+ /* This has to match that of the the CodeMirror class line-height below */
+ line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+ background: #fdd;
+ /* very light red background for stderr */
+}
+div.output_latex {
+ text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+ padding: 0;
+}
+.js-error {
+ color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+ line-height: 1.21429em;
+ padding-top: 5px;
+}
+pre.raw_input_prompt {
+ /* nothing needed here. */
+}
+input.raw_input {
+ font-family: monospace;
+ font-size: inherit;
+ color: inherit;
+ width: auto;
+ /* make sure input baseline aligns with prompt */
+ vertical-align: baseline;
+ /* padding + margin = 0.5em between prompt and cursor */
+ padding: 0em 0.25em;
+ margin: 0em 0.25em;
+}
+input.raw_input:focus {
+ box-shadow: none;
+}
+p.p-space {
+ margin-bottom: 10px;
+}
+div.output_unrecognized {
+ padding: 5px;
+ font-weight: bold;
+ color: red;
+}
+div.output_unrecognized a {
+ color: inherit;
+ text-decoration: none;
+}
+div.output_unrecognized a:hover {
+ color: inherit;
+ text-decoration: none;
+}
+.rendered_html {
+ color: #000000;
+ /* any extras will just be numbers: */
+}
+.rendered_html em {
+ font-style: italic;
+}
+.rendered_html strong {
+ font-weight: bold;
+}
+.rendered_html u {
+ text-decoration: underline;
+}
+.rendered_html :link {
+ text-decoration: underline;
+}
+.rendered_html :visited {
+ text-decoration: underline;
+}
+.rendered_html h1 {
+ font-size: 185.7%;
+ margin: 1.08em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h2 {
+ font-size: 157.1%;
+ margin: 1.27em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h3 {
+ font-size: 128.6%;
+ margin: 1.55em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h4 {
+ font-size: 100%;
+ margin: 2em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h5 {
+ font-size: 100%;
+ margin: 2em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+ font-style: italic;
+}
+.rendered_html h6 {
+ font-size: 100%;
+ margin: 2em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+ font-style: italic;
+}
+.rendered_html h1:first-child {
+ margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+ margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+ margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+ margin-top: 1em;
+}
+.rendered_html h5:first-child {
+ margin-top: 1em;
+}
+.rendered_html h6:first-child {
+ margin-top: 1em;
+}
+.rendered_html ul {
+ list-style: disc;
+ margin: 0em 2em;
+ padding-left: 0px;
+}
+.rendered_html ul ul {
+ list-style: square;
+ margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+ list-style: circle;
+ margin: 0em 2em;
+}
+.rendered_html ol {
+ list-style: decimal;
+ margin: 0em 2em;
+ padding-left: 0px;
+}
+.rendered_html ol ol {
+ list-style: upper-alpha;
+ margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+ list-style: lower-alpha;
+ margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+ list-style: lower-roman;
+ margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+ list-style: decimal;
+ margin: 0em 2em;
+}
+.rendered_html * + ul {
+ margin-top: 1em;
+}
+.rendered_html * + ol {
+ margin-top: 1em;
+}
+.rendered_html hr {
+ color: #000000;
+ background-color: #000000;
+}
+.rendered_html pre {
+ margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+ border: 0;
+ background-color: #ffffff;
+ color: #000000;
+ font-size: 100%;
+ padding: 0px;
+}
+.rendered_html blockquote {
+ margin: 1em 2em;
+}
+.rendered_html table {
+ margin-left: auto;
+ margin-right: auto;
+ border: 1px solid #000000;
+ border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+ border: 1px solid #000000;
+ border-collapse: collapse;
+ margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+ text-align: left;
+ vertical-align: middle;
+ padding: 4px;
+}
+.rendered_html th {
+ font-weight: bold;
+}
+.rendered_html * + table {
+ margin-top: 1em;
+}
+.rendered_html p {
+ text-align: left;
+}
+.rendered_html * + p {
+ margin-top: 1em;
+}
+.rendered_html img {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+.rendered_html * + img {
+ margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+ max-width: 100%;
+ height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+ max-width: none;
+}
+div.text_cell {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+@media (max-width: 540px) {
+ div.text_cell > div.prompt {
+ display: none;
+ }
+}
+div.text_cell_render {
+ /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+ outline: none;
+ resize: none;
+ width: inherit;
+ border-style: none;
+ padding: 0.5em 0.5em 0.5em 0.4em;
+ color: #000000;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+ text-decoration: none;
+ padding: 0px 20px;
+ visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+ visibility: visible;
+}
+.text_cell.rendered .input_area {
+ display: none;
+}
+.text_cell.rendered .rendered_html {
+ overflow-x: auto;
+ overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+ display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+ font-weight: bold;
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+ font-size: 185.7%;
+}
+.cm-header-2 {
+ font-size: 157.1%;
+}
+.cm-header-3 {
+ font-size: 128.6%;
+}
+.cm-header-4 {
+ font-size: 110%;
+}
+.cm-header-5 {
+ font-size: 100%;
+ font-style: italic;
+}
+.cm-header-6 {
+ font-size: 100%;
+ font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+ .notebook_app {
+ padding-left: 0px;
+ padding-right: 0px;
+ }
+}
+#ipython-main-app {
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ height: 100%;
+}
+div#notebook_panel {
+ margin: 0px;
+ padding: 0px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ height: 100%;
+}
+div#notebook {
+ font-size: 14px;
+ line-height: 20px;
+ overflow-y: hidden;
+ overflow-x: auto;
+ width: 100%;
+ /* This spaces the page away from the edge of the notebook area */
+ padding-top: 20px;
+ margin: 0px;
+ outline: none;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ min-height: 100%;
+}
+@media not print {
+ #notebook-container {
+ padding: 15px;
+ background-color: #ffffff;
+ min-height: 0;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ }
+}
+@media print {
+ #notebook-container {
+ width: 100%;
+ }
+}
+div.ui-widget-content {
+ border: 1px solid #ababab;
+ outline: none;
+}
+pre.dialog {
+ background-color: #f7f7f7;
+ border: 1px solid #ddd;
+ border-radius: 2px;
+ padding: 0.4em;
+ padding-left: 2em;
+}
+p.dialog {
+ padding: 0.2em;
+}
+/* Word-wrap output correctly. This is the CSS3 spelling, though Firefox seems
+ to not honor it correctly. Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+ white-space: pre-wrap;
+}
+#fonttest {
+ font-family: monospace;
+}
+p {
+ margin-bottom: 0;
+}
+.end_space {
+ min-height: 100px;
+ transition: height .2s ease;
+}
+.notebook_app > #header {
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+ .notebook_app {
+ background-color: #eeeeee;
+ }
+}
+kbd {
+ border-style: solid;
+ border-width: 1px;
+ box-shadow: none;
+ margin: 2px;
+ padding-left: 2px;
+ padding-right: 2px;
+ padding-top: 1px;
+ padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+ border: thin solid #CFCFCF;
+ border-bottom: none;
+ background: #EEE;
+ border-radius: 2px 2px 0px 0px;
+ width: 100%;
+ height: 29px;
+ padding-right: 4px;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+ /* Old browsers */
+ -webkit-box-pack: end;
+ -moz-box-pack: end;
+ box-pack: end;
+ /* Modern browsers */
+ justify-content: flex-end;
+ display: -webkit-flex;
+}
+@media print {
+ .celltoolbar {
+ display: none;
+ }
+}
+.ctb_hideshow {
+ display: none;
+ vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+ Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+ display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+ border-top-right-radius: 0px;
+ border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+ border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+ font-size: 87%;
+ padding-top: 3px;
+}
+.celltoolbar select {
+ display: block;
+ width: 100%;
+ height: 32px;
+ padding: 6px 12px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #555555;
+ background-color: #ffffff;
+ background-image: none;
+ border: 1px solid #cccccc;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+ width: inherit;
+ font-size: inherit;
+ height: 22px;
+ padding: 0px;
+ display: inline-block;
+}
+.celltoolbar select:focus {
+ border-color: #66afe9;
+ outline: 0;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+ box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+ color: #999999;
+ opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+ color: #999999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+ color: #999999;
+}
+.celltoolbar select::-ms-expand {
+ border: 0;
+ background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+ background-color: #eeeeee;
+ opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+ cursor: not-allowed;
+}
+textarea.celltoolbar select {
+ height: auto;
+}
+select.celltoolbar select {
+ height: 30px;
+ line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+ height: auto;
+}
+.celltoolbar label {
+ margin-left: 5px;
+ margin-right: 5px;
+}
+.completions {
+ position: absolute;
+ z-index: 110;
+ overflow: hidden;
+ border: 1px solid #ababab;
+ border-radius: 2px;
+ -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+ box-shadow: 0px 6px 10px -1px #adadad;
+ line-height: 1;
+}
+.completions select {
+ background: white;
+ outline: none;
+ border: none;
+ padding: 0px;
+ margin: 0px;
+ overflow: auto;
+ font-family: monospace;
+ font-size: 110%;
+ color: #000000;
+ width: auto;
+}
+.completions select option.context {
+ color: #286090;
+}
+#kernel_logo_widget {
+ float: right !important;
+ float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+ display: none;
+ margin-top: -1px;
+ margin-bottom: -1px;
+ width: 32px;
+ height: 32px;
+}
+#menubar {
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ margin-top: 1px;
+}
+#menubar .navbar {
+ border-top: 1px;
+ border-radius: 0px 0px 2px 2px;
+ margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+ float: left;
+ padding-top: 7px;
+ padding-bottom: 7px;
+ border: none;
+}
+#menubar .navbar-collapse {
+ clear: left;
+}
+.nav-wrapper {
+ border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+ padding-top: 4px;
+}
+ul#help_menu li a {
+ overflow: hidden;
+ padding-right: 2.2em;
+}
+ul#help_menu li a i {
+ margin-right: -1.2em;
+}
+.dropdown-submenu {
+ position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+ top: 0;
+ left: 100%;
+ margin-top: -6px;
+ margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+ display: block;
+}
+.dropdown-submenu > a:after {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ display: block;
+ content: "\f0da";
+ float: right;
+ color: #333333;
+ margin-top: 2px;
+ margin-right: -10px;
+}
+.dropdown-submenu > a:after.fa-pull-left {
+ margin-right: .3em;
+}
+.dropdown-submenu > a:after.fa-pull-right {
+ margin-left: .3em;
+}
+.dropdown-submenu > a:after.pull-left {
+ margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+ margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+ color: #262626;
+}
+.dropdown-submenu.pull-left {
+ float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+ left: -100%;
+ margin-left: 10px;
+}
+#notification_area {
+ float: right !important;
+ float: right;
+ z-index: 10;
+}
+.indicator_area {
+ float: right !important;
+ float: right;
+ color: #777777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+}
+#kernel_indicator {
+ float: right !important;
+ float: right;
+ color: #777777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+ border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+ padding-left: 5px;
+ padding-right: 5px;
+}
+#modal_indicator {
+ float: right !important;
+ float: right;
+ color: #777777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+}
+#readonly-indicator {
+ float: right !important;
+ float: right;
+ color: #777777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+ margin-top: 2px;
+ margin-bottom: 0px;
+ margin-left: 0px;
+ margin-right: 0px;
+ display: none;
+}
+.modal_indicator:before {
+ width: 1.28571429em;
+ text-align: center;
+}
+.edit_mode .modal_indicator:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f040";
+}
+.edit_mode .modal_indicator:before.fa-pull-left {
+ margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.fa-pull-right {
+ margin-left: .3em;
+}
+.edit_mode .modal_indicator:before.pull-left {
+ margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+ margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: ' ';
+}
+.command_mode .modal_indicator:before.fa-pull-left {
+ margin-right: .3em;
+}
+.command_mode .modal_indicator:before.fa-pull-right {
+ margin-left: .3em;
+}
+.command_mode .modal_indicator:before.pull-left {
+ margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_idle_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f10c";
+}
+.kernel_idle_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.kernel_idle_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.kernel_idle_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_busy_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f111";
+}
+.kernel_busy_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.kernel_busy_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.kernel_busy_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_dead_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f1e2";
+}
+.kernel_dead_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.kernel_dead_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.kernel_dead_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f127";
+}
+.kernel_disconnected_icon:before.fa-pull-left {
+ margin-right: .3em;
+}
+.kernel_disconnected_icon:before.fa-pull-right {
+ margin-left: .3em;
+}
+.kernel_disconnected_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+ margin-left: .3em;
+}
+.notification_widget {
+ color: #777777;
+ z-index: 10;
+ background: rgba(240, 240, 240, 0.5);
+ margin-right: 4px;
+ color: #333333;
+ background-color: #ffffff;
+ border-color: #cccccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+ color: #333333;
+ background-color: #e6e6e6;
+ border-color: #8c8c8c;
+}
+.notification_widget:hover {
+ color: #333333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+ color: #333333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+ color: #333333;
+ background-color: #d4d4d4;
+ border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+ background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+ background-color: #ffffff;
+ border-color: #cccccc;
+}
+.notification_widget .badge {
+ color: #ffffff;
+ background-color: #333333;
+}
+.notification_widget.warning {
+ color: #ffffff;
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+ color: #ffffff;
+ background-color: #ec971f;
+ border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+ color: #ffffff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+ color: #ffffff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+ color: #ffffff;
+ background-color: #d58512;
+ border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+ background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.notification_widget.warning .badge {
+ color: #f0ad4e;
+ background-color: #ffffff;
+}
+.notification_widget.success {
+ color: #ffffff;
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+ color: #ffffff;
+ background-color: #449d44;
+ border-color: #255625;
+}
+.notification_widget.success:hover {
+ color: #ffffff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+ color: #ffffff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+ color: #ffffff;
+ background-color: #398439;
+ border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+ background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+ color: #5cb85c;
+ background-color: #ffffff;
+}
+.notification_widget.info {
+ color: #ffffff;
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+ color: #ffffff;
+ background-color: #31b0d5;
+ border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+ color: #ffffff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+ color: #ffffff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+ color: #ffffff;
+ background-color: #269abc;
+ border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+ background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.notification_widget.info .badge {
+ color: #5bc0de;
+ background-color: #ffffff;
+}
+.notification_widget.danger {
+ color: #ffffff;
+ background-color: #d9534f;
+ border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+ color: #ffffff;
+ background-color: #c9302c;
+ border-color: #761c19;
+}
+.notification_widget.danger:hover {
+ color: #ffffff;
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+<body>
+ <div tabindex="-1" id="notebook" class="border-box-sizing">
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+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Gold-droplet-hits-substrate">Gold droplet hits substrate<a class="anchor-link" href="#Gold-droplet-hits-substrate">¶</a></h1>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Here, we will shoot a gold cluster onto a gold substrate and systematically study the effect of cluster size and velocity. A workflow involves</p>
+<ul>
+<li>Energy minimization of a substrate</li>
+<li>Equilibration of substrate (here in NVE ensemble)</li>
+<li>Energy minimization of a cluster</li>
+<li>Equilibration of cluster (here in NVE ensemble)</li>
+<li>Melting cluster at high temperature</li>
+<li>Shooting cluster onto substrate</li>
+</ul>
+<p><img 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" alt="Gold droplet hits substrate workflow"></p>
+<p>The subfolder <code>infiles</code> contains LAMMPS input scripts for each of these steps.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Initialization">Initialization<a class="anchor-link" href="#Initialization">¶</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [1]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># to display plots directly inline within this notebook</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [2]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we might need these for</span>
+<span class="c1"># * enforced memory clean-up (gc)</span>
+<span class="c1"># * bash-like globbing of files on the local file system (glob)</span>
+<span class="c1"># * basic stream-from-string input (io)</span>
+<span class="c1"># * extracting interseting information from log files by regular expressions (re)</span>
+<span class="c1"># * os, sys ...</span>
+<span class="kn">import</span> <span class="nn">gc</span><span class="o">,</span> <span class="nn">glob</span><span class="o">,</span> <span class="nn">io</span><span class="o">,</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">re</span><span class="o">,</span> <span class="nn">sys</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [3]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">ase</span><span class="o">,</span> <span class="nn">ase.io</span><span class="o">,</span> <span class="nn">ase.visualize</span> <span class="c1"># read and visualize LAMMPS trajectories</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">scp</span> <span class="c1"># here for handling rotations as matrices and quaternions</span>
+<span class="kn">import</span> <span class="nn">scipy.spatial</span>
+<span class="kn">from</span> <span class="nn">pprint</span> <span class="k">import</span> <span class="n">pprint</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [4]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># FireWorks functionality </span>
+<span class="kn">from</span> <span class="nn">fireworks</span> <span class="k">import</span> <span class="n">Firework</span><span class="p">,</span> <span class="n">LaunchPad</span><span class="p">,</span> <span class="n">ScriptTask</span><span class="p">,</span> <span class="n">Workflow</span>
+
+<span class="c1"># new here: tasks using FireWorks FilePad</span>
+<span class="kn">from</span> <span class="nn">fireworks.user_objects.firetasks.filepad_tasks</span> <span class="k">import</span> <span class="n">AddFilesTask</span><span class="p">,</span> <span class="n">GetFilesTask</span>
+
+<span class="c1"># direct FilePad access, similar to the familiar LaunchPad:</span>
+<span class="kn">from</span> <span class="nn">fireworks.utilities.filepad</span> <span class="k">import</span> <span class="n">FilePad</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [5]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">prefix</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [6]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># to start with</span>
+<span class="n">infile_prefix</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span><span class="s1">'infiles'</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [7]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the FireWorks LaunchPad</span>
+<span class="n">lp</span> <span class="o">=</span> <span class="n">LaunchPad</span><span class="o">.</span><span class="n">auto_load</span><span class="p">()</span> <span class="c1">#Define the server and database</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [8]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># FilePad behaves analogous to LaunchPad</span>
+<span class="n">fp</span> <span class="o">=</span> <span class="n">FilePad</span><span class="o">.</span><span class="n">auto_load</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [9]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># use this little snippet to identify large objects in memory</span>
+<span class="c1"># delete them and, if necessary, call gc.collect() manually to free up memory</span>
+<span class="k">def</span> <span class="nf">memuse</span><span class="p">():</span>
+ <span class="sd">"""Quick overview on memory usage of objects in Jupyter notebook"""</span>
+ <span class="c1"># https://stackoverflow.com/questions/40993626/list-memory-usage-in-ipython-and-jupyter</span>
+ <span class="c1"># These are the usual ipython objects, including this one you are creating</span>
+ <span class="n">ipython_vars</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'In'</span><span class="p">,</span> <span class="s1">'Out'</span><span class="p">,</span> <span class="s1">'exit'</span><span class="p">,</span> <span class="s1">'quit'</span><span class="p">,</span> <span class="s1">'get_ipython'</span><span class="p">,</span> <span class="s1">'ipython_vars'</span><span class="p">]</span>
+
+ <span class="c1"># Get a sorted list of the objects and their sizes</span>
+ <span class="k">return</span> <span class="nb">sorted</span><span class="p">([(</span><span class="n">x</span><span class="p">,</span> <span class="n">sys</span><span class="o">.</span><span class="n">getsizeof</span><span class="p">(</span><span class="nb">globals</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="s1">'__main__'</span><span class="p">])</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">x</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'_'</span><span class="p">)</span> <span class="ow">and</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span> <span class="ow">and</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ipython_vars</span><span class="p">],</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Static-workflow">Static workflow<a class="anchor-link" href="#Static-workflow">¶</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Within this demo, we arrange our input and data files in a data base
+and attach metadata for easy querying. FireWorks readily offers the FilePad
+(based on an underlying MongoDB "GridFS") for such purposes.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [10]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># some unique identifier for our study</span>
+<span class="n">project_id</span> <span class="o">=</span> <span class="s1">'fireworks-hands-on-2019-12-09'</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Files">Files<a class="anchor-link" href="#Files">¶</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [11]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># input and data files for the current parametric study</span>
+<span class="n">files</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="c1"># one file describing the interatomic potential</span>
+ <span class="s1">'potential'</span><span class="p">:</span> <span class="s1">'Au-Grochola-JCP05.eam.alloy'</span><span class="p">,</span>
+ <span class="c1"># two files for different cluster sizes</span>
+ <span class="s1">'large_cluster'</span><span class="p">:</span> <span class="s1">'cluster_3871.lammps'</span><span class="p">,</span>
+ <span class="s1">'small_cluster'</span><span class="p">:</span> <span class="s1">'cluster_923.lammps'</span><span class="p">,</span>
+ <span class="c1"># several LAMMPS input files for the above steps</span>
+ <span class="s1">'minimize_substrate'</span><span class="p">:</span> <span class="s1">'A_minimize_substrate.in'</span><span class="p">,</span>
+ <span class="s1">'equilibrate_substrate'</span><span class="p">:</span> <span class="s1">'B_equilibrate_substrate.in'</span><span class="p">,</span>
+ <span class="s1">'minimize_cluster'</span><span class="p">:</span> <span class="s1">'A_minimize_cluster.in'</span><span class="p">,</span>
+ <span class="s1">'equilibrate_cluster'</span><span class="p">:</span> <span class="s1">'B_equilibrate_cluster.in'</span><span class="p">,</span>
+ <span class="s1">'melt_cluster'</span><span class="p">:</span> <span class="s1">'C_melt_cluster.in'</span><span class="p">,</span>
+ <span class="s1">'deposit'</span><span class="p">:</span> <span class="s1">'D_deposit.in'</span>
+<span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [18]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># metadata common to all these files </span>
+<span class="n">metadata</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'project'</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'input'</span>
+<span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [19]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fp_files</span> <span class="o">=</span> <span class="p">[]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [20]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># insert these input files into data base</span>
+<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">files</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+ <span class="n">file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">infile_prefix</span><span class="p">,</span><span class="n">file</span><span class="p">)</span>
+ <span class="n">identifier</span> <span class="o">=</span> <span class="s1">'/'</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">project_id</span><span class="p">,</span><span class="n">name</span><span class="p">))</span> <span class="c1"># identifier is like a path on a file system</span>
+ <span class="n">metadata</span><span class="p">[</span><span class="s2">"name"</span><span class="p">]</span> <span class="o">=</span> <span class="n">name</span>
+ <span class="c1">#fp.delete_file(identifier=identifier)</span>
+ <span class="n">fp_files</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">fp</span><span class="o">.</span><span class="n">add_file</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span><span class="n">identifier</span><span class="o">=</span><span class="n">identifier</span><span class="p">,</span><span class="n">metadata</span> <span class="o">=</span> <span class="n">metadata</span><span class="p">)</span> <span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [21]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># queries to the data base are simple dictionaries</span>
+<span class="n">query</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'metadata.project'</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s1">'metadata.type'</span><span class="p">:</span> <span class="s1">'input'</span>
+<span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [22]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># use underlying MongoDB functionality to check total number of documents matching query</span>
+<span class="n">fp</span><span class="o">.</span><span class="n">filepad</span><span class="o">.</span><span class="n">count_documents</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt output_prompt">Out[22]:</div>
+
+
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>9</pre>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [23]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we fixed simple identifiers of this form</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">identifier</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>fireworks-hands-on-2019-12-09/deposit
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [24]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># on a lower level, each object has a unique "GridFS id":</span>
+<span class="n">pprint</span><span class="p">(</span><span class="n">fp_files</span><span class="p">)</span> <span class="c1"># underlying GridFS id and readable identifiers</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>[('5ded279c11940f1ec729018a', 'fireworks-hands-on-2019-12-09/potential'),
+ ('5ded279c11940f1ec729018d', 'fireworks-hands-on-2019-12-09/large_cluster'),
+ ('5ded279c11940f1ec7290190', 'fireworks-hands-on-2019-12-09/small_cluster'),
+ ('5ded279c11940f1ec7290193',
+ 'fireworks-hands-on-2019-12-09/minimize_substrate'),
+ ('5ded279c11940f1ec7290196',
+ 'fireworks-hands-on-2019-12-09/equilibrate_substrate'),
+ ('5ded279c11940f1ec7290199', 'fireworks-hands-on-2019-12-09/minimize_cluster'),
+ ('5ded279c11940f1ec729019c',
+ 'fireworks-hands-on-2019-12-09/equilibrate_cluster'),
+ ('5ded279c11940f1ec729019f', 'fireworks-hands-on-2019-12-09/melt_cluster'),
+ ('5ded279c11940f1ec72901a2', 'fireworks-hands-on-2019-12-09/deposit')]
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [25]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># if necessary, remove all files matching query</span>
+<span class="c1"># fp.delete_file_by_query(query)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Build-workflow">Build workflow<a class="anchor-link" href="#Build-workflow">¶</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>We have each FireWork prepared in a static yaml file within this folder,
+such as</p>
+<div class="highlight"><pre><span></span><span class="l l-Scalar l-Scalar-Plain">fw_id</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">-10</span>
+<span class="l l-Scalar l-Scalar-Plain">name</span><span class="p p-Indicator">:</span> <span class="s">"Minimize</span><span class="nv"> </span><span class="s">substrate"</span>
+<span class="l l-Scalar l-Scalar-Plain">spec</span><span class="p p-Indicator">:</span>
+ <span class="l l-Scalar l-Scalar-Plain">_category</span><span class="p p-Indicator">:</span> <span class="s">"bwcloud_noqueue"</span>
+ <span class="l l-Scalar l-Scalar-Plain">_files_out</span><span class="p p-Indicator">:</span>
+ <span class="l l-Scalar l-Scalar-Plain">data_file</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">final.lammps</span>
+ <span class="l l-Scalar l-Scalar-Plain">_tasks</span><span class="p p-Indicator">:</span>
+ <span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">_fw_name</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">GetFilesTask</span>
+ <span class="l l-Scalar l-Scalar-Plain">identifiers</span><span class="p p-Indicator">:</span>
+ <span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">fireworks-hands-on-2019-12-09/potential</span>
+ <span class="l l-Scalar l-Scalar-Plain">new_file_names</span><span class="p p-Indicator">:</span>
+ <span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">Au-Grochola-JCP05.eam.alloy</span>
+ <span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">_fw_name</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">GetFilesTask</span>
+ <span class="l l-Scalar l-Scalar-Plain">identifiers</span><span class="p p-Indicator">:</span>
+ <span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">fireworks-hands-on-2019-12-09/minimize_substrate</span>
+ <span class="l l-Scalar l-Scalar-Plain">new_file_names</span><span class="p p-Indicator">:</span>
+ <span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">lammps.in</span>
+ <span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">_fw_name</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">ScriptTask</span>
+ <span class="l l-Scalar l-Scalar-Plain">script</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">source /etc/profile; module load LAMMPS; mpirun -n 2 lmp -in lammps.in</span>
+</pre></div>
+<p>Notice the two <code>GetFilesTask</code>s. They query files by their identifier and put them in the task's working directory.
+This is an alternative to <code>FileTransferTask</code>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [26]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># We have each FireWork prepared in a static yaml file within this folder.</span>
+<span class="c1"># Now, we read these and fix the dependencies here:</span>
+
+<span class="n">fw_A_substrate</span> <span class="o">=</span> <span class="n">Firework</span><span class="o">.</span><span class="n">from_file</span><span class="p">(</span><span class="s1">'A_substrate.yaml'</span><span class="p">,)</span>
+<span class="n">fw_B_substrate</span> <span class="o">=</span> <span class="n">Firework</span><span class="o">.</span><span class="n">from_file</span><span class="p">(</span><span class="s1">'B_substrate.yaml'</span><span class="p">)</span>
+
+<span class="n">fw_A_cluster</span> <span class="o">=</span> <span class="n">Firework</span><span class="o">.</span><span class="n">from_file</span><span class="p">(</span><span class="s1">'A_cluster.yaml'</span><span class="p">)</span>
+<span class="n">fw_B_cluster</span> <span class="o">=</span> <span class="n">Firework</span><span class="o">.</span><span class="n">from_file</span><span class="p">(</span><span class="s1">'B_cluster.yaml'</span><span class="p">)</span>
+<span class="n">fw_C_cluster</span> <span class="o">=</span> <span class="n">Firework</span><span class="o">.</span><span class="n">from_file</span><span class="p">(</span><span class="s1">'C_cluster.yaml'</span><span class="p">)</span>
+
+<span class="n">fw_D_deposit</span> <span class="o">=</span> <span class="n">Firework</span><span class="o">.</span><span class="n">from_file</span><span class="p">(</span><span class="s1">'D_deposit.yaml'</span><span class="p">)</span>
+
+<span class="n">wf</span> <span class="o">=</span> <span class="n">Workflow</span><span class="p">(</span>
+ <span class="p">[</span> <span class="n">fw_A_substrate</span><span class="p">,</span> <span class="n">fw_B_substrate</span><span class="p">,</span>
+ <span class="n">fw_A_cluster</span><span class="p">,</span> <span class="n">fw_B_cluster</span><span class="p">,</span> <span class="n">fw_C_cluster</span><span class="p">,</span>
+ <span class="n">fw_D_deposit</span> <span class="p">],</span>
+
+ <span class="p">{</span> <span class="n">fw_A_substrate</span><span class="p">:</span> <span class="n">fw_B_substrate</span><span class="p">,</span>
+ <span class="n">fw_B_substrate</span><span class="p">:</span> <span class="n">fw_D_deposit</span><span class="p">,</span>
+
+ <span class="n">fw_A_cluster</span><span class="p">:</span> <span class="n">fw_B_cluster</span><span class="p">,</span>
+ <span class="n">fw_B_cluster</span><span class="p">:</span> <span class="n">fw_C_cluster</span><span class="p">,</span>
+ <span class="n">fw_C_cluster</span><span class="p">:</span> <span class="n">fw_D_deposit</span> <span class="p">},</span>
+
+ <span class="n">name</span> <span class="o">=</span> <span class="s2">"Gold cluster impact"</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [27]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we can write a static yaml file and have a look at, then submit it</span>
+<span class="c1"># on the command line with "lpad add wf.yaml"...</span>
+<span class="n">wf</span><span class="o">.</span><span class="n">to_file</span><span class="p">(</span><span class="s1">'wf.yaml'</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [28]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># or directly submit from here</span>
+<span class="n">fw_ids</span> <span class="o">=</span> <span class="n">lp</span><span class="o">.</span><span class="n">add_wf</span><span class="p">(</span><span class="n">wf</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>2019-12-08 17:41:43,074 INFO Added a workflow. id_map: {-60: 23568, -50: 23569, -40: 23570, -30: 23571, -20: 23572, -10: 23573}
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Query-results">Query results<a class="anchor-link" href="#Query-results">¶</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Look at <code>D_deposit.yaml</code>. The final FireWork in this workflow uses
+<code>AddFilesTask</code>s like this</p>
+<div class="highlight"><pre><span></span><span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">_fw_name</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">AddFilesTask</span>
+ <span class="l l-Scalar l-Scalar-Plain">paths</span><span class="p p-Indicator">:</span>
+ <span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">traj.dump</span>
+ <span class="l l-Scalar l-Scalar-Plain">compress</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">True</span>
+ <span class="l l-Scalar l-Scalar-Plain">metadata</span><span class="p p-Indicator">:</span>
+ <span class="l l-Scalar l-Scalar-Plain">project</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">fireworks-hands-on-2019-12-09</span>
+ <span class="l l-Scalar l-Scalar-Plain">type</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">trajectory</span>
+ <span class="l l-Scalar l-Scalar-Plain">velocity</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">-25</span>
+ <span class="l l-Scalar l-Scalar-Plain">steps</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">4000</span>
+ <span class="l l-Scalar l-Scalar-Plain">timestep</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">0.001</span>
+</pre></div>
+<p>to store files in the FilePad and attach metadata. We can use such metadata to query the output files after the workflow has run.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [29]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">"metadata.project"</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s2">"metadata.velocity"</span><span class="p">:</span> <span class="o">-</span><span class="mi">25</span><span class="p">,</span>
+ <span class="s2">"metadata.steps"</span> <span class="p">:</span> <span class="mi">4000</span>
+<span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [30]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># use underlying MongoDB functionality </span>
+<span class="n">fp</span><span class="o">.</span><span class="n">filepad</span><span class="o">.</span><span class="n">count_documents</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt output_prompt">Out[30]:</div>
+
+
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>3</pre>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [33]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">"metadata.project"</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s2">"metadata.velocity"</span><span class="p">:</span> <span class="o">-</span><span class="mi">25</span><span class="p">,</span>
+ <span class="s2">"metadata.steps"</span> <span class="p">:</span> <span class="mi">4000</span><span class="p">,</span>
+ <span class="s2">"metadata.type"</span><span class="p">:</span> <span class="s1">'final configuration'</span>
+<span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [34]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">config_file</span><span class="p">,</span> <span class="n">config_metadata</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">get_file_by_query</span><span class="p">(</span><span class="n">query</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [35]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pprint</span><span class="p">(</span><span class="n">config_metadata</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>{'_id': ObjectId('5ded286311940f336850cb3f'),
+ 'compressed': True,
+ 'gfs_id': '5ded286311940f336850cb3b',
+ 'identifier': '/mnt/dat/fw/launcher_2019-12-08-16-42-47-414867/final.lammps',
+ 'metadata': {'project': 'fireworks-hands-on-2019-12-09',
+ 'steps': 4000,
+ 'timestep': 0.001,
+ 'type': 'final configuration',
+ 'velocity': -25},
+ 'original_file_name': 'final.lammps',
+ 'original_file_path': '/mnt/dat/fw/launcher_2019-12-08-16-42-47-414867/final.lammps'}
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [36]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># use ASE to read and visualize this final configuration</span>
+<span class="n">final_config</span> <span class="o">=</span> <span class="n">ase</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read</span><span class="p">(</span>
+ <span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">(</span><span class="n">config_file</span><span class="o">.</span><span class="n">decode</span><span class="p">()),</span>
+ <span class="nb">format</span><span class="o">=</span><span class="s1">'lammps-data'</span><span class="p">,</span>
+ <span class="n">style</span><span class="o">=</span><span class="s1">'atomic'</span><span class="p">,</span>
+ <span class="n">Z_of_type</span><span class="o">=</span><span class="p">{</span><span class="mi">1</span> <span class="p">:</span> <span class="n">ase</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">atomic_numbers</span><span class="p">[</span><span class="s1">'Au'</span><span class="p">]})</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [38]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># rotate around y axis</span>
+<span class="n">roty</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+ <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
+ <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+ <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [39]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># convert rotation matrix to quaternion</span>
+<span class="n">rot</span> <span class="o">=</span> <span class="n">scp</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">Rotation</span><span class="o">.</span><span class="n">from_dcm</span><span class="p">(</span><span class="n">roty</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [40]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># circle leftwards for ngl quaternion representation</span>
+<span class="n">nglrot</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">rot</span><span class="o">.</span><span class="n">as_quat</span><span class="p">(),</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [42]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">v</span> <span class="o">=</span> <span class="n">ase</span><span class="o">.</span><span class="n">visualize</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">final_config</span><span class="p">,</span><span class="n">viewer</span><span class="o">=</span><span class="s1">'ngl'</span><span class="p">)</span>
+<span class="n">v</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">remove_unitcell</span><span class="p">()</span>
+<span class="n">v</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">nglrot</span><span class="p">)</span>
+<span class="n">v</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">center</span><span class="p">()</span>
+<span class="n">v</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+
+
+
+<div id="9ae9412b-b46f-4c95-b096-bac478e4ff0a"></div>
+<div class="output_subarea output_widget_view ">
+<script type="text/javascript">
+var element = $('#9ae9412b-b46f-4c95-b096-bac478e4ff0a');
+</script>
+<script type="application/vnd.jupyter.widget-view+json">
+{"model_id": "dd0c14b7f0e545aeb257de83f049d251", "version_major": 2, "version_minor": 0}
+</script>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Parametric-study">Parametric study<a class="anchor-link" href="#Parametric-study">¶</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The true advantage of arrag of arranging files as objects within a data base is the
+comparably comfortable evalutation of parametric studies. For this purpose, we prepare
+a simple parametric sweep across different cluster sizes and velocities in the above study:
+<img src="data:image/png;base64,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" alt="Parametric study workflow"></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Build-workflow">Build workflow<a class="anchor-link" href="#Build-workflow">¶</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>We have two gold cluster data files, one small cluster with 923 atoms and one larger cluster with 3871 atoms.
+In this parametric study, we want to shoot the small cluster at 25 Angstrom / ps, but for the large cluster two vleocities are to be probed. In the following, we generate a workflow corresponding to the previous one, now branching at the cluster energy minimization step for different cluster sizes, and branching again at the last step for shooting the cluster at several velocities. Cluster size and velocity are attached to the output files as metadata, just as shown above, e.g. as</p>
+<div class="highlight"><pre><span></span><span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">_fw_name</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">AddFilesTask</span>
+ <span class="l l-Scalar l-Scalar-Plain">compress</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">true</span>
+ <span class="l l-Scalar l-Scalar-Plain">metadata</span><span class="p p-Indicator">:</span>
+ <span class="l l-Scalar l-Scalar-Plain">cluster</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">large_cluster</span>
+ <span class="l l-Scalar l-Scalar-Plain">cluster_size</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">3871</span>
+ <span class="l l-Scalar l-Scalar-Plain">material</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">gold</span>
+ <span class="l l-Scalar l-Scalar-Plain">project</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">fireworks-hands-on-2019-12-09</span>
+ <span class="l l-Scalar l-Scalar-Plain">type</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">trajectory</span>
+ <span class="l l-Scalar l-Scalar-Plain">velocity</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">-50</span>
+ <span class=" -Error"> </span><span class="l l-Scalar l-Scalar-Plain">paths</span><span class="p p-Indicator">:</span> <span class="l l-Scalar l-Scalar-Plain">traj.dump</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [43]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># parametric study in two dimensions: cluster size and cluster velocity</span>
+<span class="n">clusters</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'small_cluster'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'cluster_size'</span><span class="p">:</span> <span class="mi">923</span><span class="p">},</span> <span class="c1"># number of atoms</span>
+ <span class="s1">'large_cluster'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'cluster_size'</span><span class="p">:</span> <span class="mi">3871</span><span class="p">}</span>
+ <span class="p">}</span>
+
+<span class="c1"># depending on cluster size, we look at different sets of velocities:</span>
+<span class="n">cluster_velocities_dict</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'small_cluster'</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mi">25</span><span class="p">],</span>
+ <span class="s1">'large_cluster'</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mi">25</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">]</span> <span class="p">}</span> <span class="c1"># cluster approach velocity, Angtsrom / ps</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [44]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># build workflow to span two parametric dimensions "cluster size" and "velocity"</span>
+<span class="n">fws</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">parent_child_deps</span> <span class="o">=</span> <span class="p">{}</span>
+
+<span class="c1"># default tasks:</span>
+<span class="n">get_potential_task</span> <span class="o">=</span> <span class="n">GetFilesTask</span><span class="p">(</span>
+ <span class="n">identifiers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'/'</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">project_id</span> <span class="p">,</span><span class="s1">'potential'</span><span class="p">))],</span>
+ <span class="n">new_file_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Au-Grochola-JCP05.eam.alloy'</span><span class="p">]</span> <span class="p">)</span>
+
+<span class="n">run_lammps_task</span> <span class="o">=</span> <span class="n">ScriptTask</span><span class="o">.</span><span class="n">from_str</span><span class="p">(</span><span class="s1">'''</span>
+<span class="s1"> source /etc/profile; </span>
+<span class="s1"> module load LAMMPS; </span>
+<span class="s1"> mpirun -n 2 lmp -in lammps.in</span>
+<span class="s1"> '''</span><span class="p">)</span>
+
+<span class="c1"># substrate stays the same for all runs</span>
+
+<span class="c1"># ------------------</span>
+<span class="c1"># Minimize substrate</span>
+<span class="c1"># ------------------</span>
+
+<span class="c1"># define two tasks in Firework: Minimize substrate</span>
+<span class="c1"># - GetFilesByQueryTask * 2</span>
+<span class="c1"># - ScriptTask</span>
+<span class="n">ft</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="n">get_potential_task</span><span class="p">,</span>
+ <span class="n">GetFilesTask</span><span class="p">(</span>
+ <span class="n">identifiers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'/'</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">project_id</span> <span class="p">,</span><span class="s1">'minimize_substrate'</span><span class="p">))],</span>
+ <span class="n">new_file_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'lammps.in'</span><span class="p">]</span> <span class="p">)</span> <span class="p">]</span>
+
+<span class="n">fw_A_substrate</span> <span class="o">=</span> <span class="n">Firework</span><span class="p">(</span>
+ <span class="p">[</span><span class="o">*</span><span class="n">ft</span><span class="p">,</span> <span class="n">run_lammps_task</span><span class="p">],</span>
+ <span class="n">name</span><span class="o">=</span><span class="s2">"Minimize substrate"</span><span class="p">,</span>
+ <span class="n">spec</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'_category'</span><span class="p">:</span> <span class="s2">"bwcloud_noqueue"</span><span class="p">,</span>
+ <span class="s1">'_files_out'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'data_file'</span><span class="p">:</span> <span class="s2">"final.lammps"</span><span class="p">},</span>
+ <span class="s1">'metadata'</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s1">'project'</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s1">'step'</span><span class="p">:</span> <span class="s1">'minimize_substrate'</span> <span class="p">}</span> <span class="p">}</span> <span class="p">)</span>
+
+<span class="c1"># ---------------------</span>
+<span class="c1"># equilibrate substrate</span>
+<span class="c1"># ---------------------</span>
+
+<span class="c1"># define two tasks in Firework: Equilibrate substrate</span>
+<span class="c1"># - FileTransferTask</span>
+<span class="c1"># - ScriptTask</span>
+<span class="n">ft</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="n">get_potential_task</span><span class="p">,</span>
+ <span class="n">GetFilesTask</span><span class="p">(</span>
+ <span class="n">identifiers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'/'</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">project_id</span> <span class="p">,</span><span class="s1">'equilibrate_substrate'</span><span class="p">))],</span>
+ <span class="n">new_file_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'lammps.in'</span><span class="p">]</span> <span class="p">)</span> <span class="p">]</span>
+
+<span class="n">fw_B_substrate</span> <span class="o">=</span> <span class="n">Firework</span><span class="p">(</span>
+ <span class="p">[</span><span class="o">*</span><span class="n">ft</span><span class="p">,</span> <span class="n">run_lammps_task</span><span class="p">],</span>
+ <span class="n">name</span><span class="o">=</span><span class="s2">"Equilibrate substrate"</span><span class="p">,</span>
+ <span class="n">spec</span><span class="o">=</span><span class="p">{</span>
+ <span class="s1">'_category'</span><span class="p">:</span> <span class="s2">"bwcloud_noqueue"</span><span class="p">,</span>
+ <span class="s1">'_files_in'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'data_file'</span><span class="p">:</span> <span class="s2">"initial.lammps"</span><span class="p">},</span>
+ <span class="s1">'_files_out'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'data_file_substrate'</span><span class="p">:</span> <span class="s2">"final.lammps"</span><span class="p">},</span>
+ <span class="s1">'metadata'</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s1">'project'</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s1">'step'</span><span class="p">:</span> <span class="s1">'equlibrate_substrate'</span> <span class="p">}</span> <span class="p">}</span> <span class="p">)</span>
+
+<span class="n">fws</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="p">[</span> <span class="n">fw_A_substrate</span><span class="p">,</span> <span class="n">fw_B_substrate</span> <span class="p">]</span> <span class="p">)</span>
+<span class="n">parent_child_deps</span><span class="o">.</span><span class="n">update</span><span class="p">(</span> <span class="p">{</span>
+ <span class="n">fw_A_substrate</span><span class="p">:</span> <span class="p">[</span> <span class="n">fw_B_substrate</span> <span class="p">],</span>
+ <span class="n">fw_B_substrate</span><span class="p">:</span> <span class="p">[]</span> <span class="p">}</span> <span class="p">)</span>
+
+<span class="k">for</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">metadata</span> <span class="ow">in</span> <span class="n">clusters</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+
+ <span class="c1"># ----------------</span>
+ <span class="c1"># Minimize cluster</span>
+ <span class="c1"># ----------------</span>
+
+ <span class="c1"># define four tasks in Firework: Minimize cluster</span>
+ <span class="c1"># - FileTransferTask * 3</span>
+ <span class="c1"># - ScriptTask</span>
+ <span class="n">ft</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="n">get_potential_task</span><span class="p">,</span>
+ <span class="n">GetFilesTask</span><span class="p">(</span>
+ <span class="n">identifiers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'/'</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">project_id</span> <span class="p">,</span><span class="n">cluster</span><span class="p">))],</span>
+ <span class="n">new_file_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'initial.lammps'</span><span class="p">]</span> <span class="p">),</span>
+ <span class="n">GetFilesTask</span><span class="p">(</span>
+ <span class="n">identifiers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'/'</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">project_id</span> <span class="p">,</span><span class="s1">'minimize_cluster'</span><span class="p">))],</span>
+ <span class="n">new_file_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'lammps.in'</span><span class="p">]</span> <span class="p">)</span> <span class="p">]</span>
+
+ <span class="n">fw_A_cluster</span> <span class="o">=</span> <span class="n">Firework</span><span class="p">(</span>
+ <span class="p">[</span><span class="o">*</span><span class="n">ft</span><span class="p">,</span> <span class="n">run_lammps_task</span><span class="p">],</span>
+ <span class="n">name</span> <span class="o">=</span> <span class="s2">"Minimize cluster, N = </span><span class="si">{:d}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metadata</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">]),</span>
+ <span class="n">spec</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'_category'</span><span class="p">:</span> <span class="s2">"bwcloud_noqueue"</span><span class="p">,</span>
+ <span class="s1">'_files_out'</span><span class="p">:</span> <span class="p">{</span> <span class="s1">'data_file'</span><span class="p">:</span> <span class="s2">"final.lammps"</span> <span class="p">},</span>
+ <span class="s1">'metadata'</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s1">'project'</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s1">'step'</span><span class="p">:</span> <span class="s1">'minimize_cluster'</span><span class="p">,</span>
+ <span class="s1">'cluster'</span><span class="p">:</span> <span class="n">cluster</span><span class="p">,</span>
+ <span class="s1">'cluster_size'</span><span class="p">:</span> <span class="n">metadata</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">]</span> <span class="p">}</span> <span class="p">}</span> <span class="p">)</span>
+
+ <span class="c1"># -------------------</span>
+ <span class="c1"># Equilibrate cluster</span>
+ <span class="c1"># -------------------</span>
+
+ <span class="c1"># define two tasks in Firework: Equilibrate cluster</span>
+ <span class="c1"># - FileTransferTask</span>
+ <span class="c1"># - ScriptTask</span>
+ <span class="n">ft</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="n">get_potential_task</span><span class="p">,</span>
+ <span class="n">GetFilesTask</span><span class="p">(</span>
+ <span class="n">identifiers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'/'</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">project_id</span> <span class="p">,</span><span class="s1">'equilibrate_cluster'</span><span class="p">))],</span>
+ <span class="n">new_file_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'lammps.in'</span><span class="p">]</span> <span class="p">)</span> <span class="p">]</span>
+
+ <span class="n">fw_B_cluster</span> <span class="o">=</span> <span class="n">Firework</span><span class="p">(</span>
+ <span class="p">[</span><span class="o">*</span><span class="n">ft</span><span class="p">,</span> <span class="n">run_lammps_task</span><span class="p">],</span>
+ <span class="n">name</span> <span class="o">=</span> <span class="s2">"Equilibrate cluster, N = </span><span class="si">{:d}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metadata</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">]),</span>
+ <span class="n">spec</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'_category'</span><span class="p">:</span> <span class="s2">"bwcloud_noqueue"</span><span class="p">,</span>
+ <span class="s1">'_files_in'</span><span class="p">:</span> <span class="p">{</span> <span class="s1">'data_file'</span><span class="p">:</span> <span class="s2">"initial.lammps"</span> <span class="p">},</span>
+ <span class="s1">'_files_out'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'data_file'</span><span class="p">:</span> <span class="s2">"final.lammps"</span> <span class="p">},</span>
+ <span class="s1">'metadata'</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s1">'project'</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s1">'step'</span><span class="p">:</span> <span class="s1">'equilibrate_cluster'</span><span class="p">,</span>
+ <span class="s1">'cluster'</span><span class="p">:</span> <span class="n">cluster</span><span class="p">,</span>
+ <span class="s1">'cluster_size'</span><span class="p">:</span> <span class="n">metadata</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">]</span> <span class="p">}</span> <span class="p">}</span> <span class="p">)</span>
+
+ <span class="c1"># ------------</span>
+ <span class="c1"># Melt cluster</span>
+ <span class="c1"># ------------</span>
+
+ <span class="c1"># define two tasks in the Firework: Melt cluster</span>
+ <span class="c1"># - FileTransferTask</span>
+ <span class="c1"># - ScriptTask</span>
+ <span class="n">ft</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="n">get_potential_task</span><span class="p">,</span>
+ <span class="n">GetFilesTask</span><span class="p">(</span>
+ <span class="n">identifiers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'/'</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">project_id</span> <span class="p">,</span><span class="s1">'melt_cluster'</span><span class="p">))],</span>
+ <span class="n">new_file_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'lammps.in'</span><span class="p">]</span> <span class="p">)</span> <span class="p">]</span>
+
+ <span class="n">fw_C_cluster</span> <span class="o">=</span> <span class="n">Firework</span><span class="p">(</span>
+ <span class="p">[</span><span class="o">*</span><span class="n">ft</span><span class="p">,</span> <span class="n">run_lammps_task</span><span class="p">],</span>
+ <span class="n">name</span> <span class="o">=</span> <span class="s2">"Melt cluster, N = </span><span class="si">{:d}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metadata</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">]),</span>
+ <span class="n">spec</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'_category'</span><span class="p">:</span> <span class="s2">"bwcloud_noqueue"</span><span class="p">,</span>
+ <span class="s1">'_files_in'</span><span class="p">:</span> <span class="p">{</span> <span class="s1">'data_file'</span><span class="p">:</span> <span class="s2">"initial.lammps"</span> <span class="p">},</span>
+ <span class="s1">'_files_out'</span><span class="p">:</span> <span class="p">{</span> <span class="s1">'data_file_cluster'</span><span class="p">:</span> <span class="s2">"final.lammps"</span> <span class="p">},</span>
+ <span class="s1">'metadata'</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s1">'project'</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s1">'step'</span><span class="p">:</span> <span class="s1">'melt_cluster'</span><span class="p">,</span>
+ <span class="s1">'cluster'</span><span class="p">:</span> <span class="n">cluster</span><span class="p">,</span>
+ <span class="s1">'cluster_size'</span><span class="p">:</span> <span class="n">metadata</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">]</span> <span class="p">}</span> <span class="p">}</span> <span class="p">)</span>
+
+ <span class="n">fws</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="p">[</span> <span class="n">fw_A_cluster</span><span class="p">,</span> <span class="n">fw_B_cluster</span><span class="p">,</span> <span class="n">fw_C_cluster</span> <span class="p">]</span> <span class="p">)</span>
+ <span class="n">parent_child_deps</span><span class="o">.</span><span class="n">update</span><span class="p">(</span> <span class="p">{</span>
+ <span class="n">fw_A_cluster</span><span class="p">:</span> <span class="p">[</span> <span class="n">fw_B_cluster</span> <span class="p">],</span>
+ <span class="n">fw_B_cluster</span><span class="p">:</span> <span class="p">[</span> <span class="n">fw_C_cluster</span> <span class="p">],</span>
+ <span class="n">fw_C_cluster</span><span class="p">:</span> <span class="p">[]</span> <span class="p">}</span> <span class="p">)</span>
+
+ <span class="k">for</span> <span class="n">velocity</span> <span class="ow">in</span> <span class="n">cluster_velocities_dict</span><span class="p">[</span><span class="n">cluster</span><span class="p">]:</span>
+ <span class="c1">#steps = velocity_steps_dict[velocity]</span>
+
+ <span class="c1"># ----------------------------</span>
+ <span class="c1"># Deposit cluster on substrate</span>
+ <span class="c1"># ----------------------------</span>
+
+ <span class="c1"># define five tasks in the Firework: Deposit</span>
+ <span class="c1"># - FileTransferTask</span>
+ <span class="c1"># - ScriptTask</span>
+ <span class="c1"># - AddFilesTask * 3</span>
+ <span class="n">ft</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="n">get_potential_task</span><span class="p">,</span>
+ <span class="n">GetFilesTask</span><span class="p">(</span>
+ <span class="n">identifiers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'/'</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">project_id</span> <span class="p">,</span><span class="s1">'deposit'</span><span class="p">))],</span>
+ <span class="n">new_file_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'lammps.in'</span><span class="p">]</span> <span class="p">)</span> <span class="p">]</span>
+
+ <span class="n">st</span> <span class="o">=</span> <span class="n">ScriptTask</span><span class="o">.</span><span class="n">from_str</span><span class="p">(</span><span class="s1">'''</span>
+<span class="s1"> source /etc/profile; </span>
+<span class="s1"> module load LAMMPS; </span>
+<span class="s1"> mpirun -n 2 lmp -in lammps.in -v vel </span><span class="si">{vel:}</span><span class="s1"></span>
+<span class="s1"> '''</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">vel</span><span class="o">=</span><span class="n">velocity</span> <span class="p">)</span> <span class="p">)</span> <span class="c1"># modify LAMMPS call to override parameters here!</span>
+
+ <span class="n">at</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="n">AddFilesTask</span><span class="p">(</span> <span class="p">{</span>
+ <span class="s1">'compress'</span><span class="p">:</span><span class="kc">True</span> <span class="p">,</span>
+ <span class="s1">'paths'</span><span class="p">:</span> <span class="s2">"final.lammps"</span><span class="p">,</span>
+ <span class="s1">'metadata'</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s1">'project'</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'final configuration'</span><span class="p">,</span>
+ <span class="s1">'material'</span><span class="p">:</span> <span class="s1">'gold'</span><span class="p">,</span>
+ <span class="s1">'velocity'</span><span class="p">:</span> <span class="n">velocity</span><span class="p">,</span>
+ <span class="s1">'cluster'</span><span class="p">:</span> <span class="n">cluster</span><span class="p">,</span>
+ <span class="s1">'cluster_size'</span><span class="p">:</span> <span class="n">metadata</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">]}</span> <span class="p">}</span> <span class="p">),</span>
+ <span class="n">AddFilesTask</span><span class="p">(</span> <span class="p">{</span>
+ <span class="s1">'compress'</span><span class="p">:</span><span class="kc">True</span> <span class="p">,</span>
+ <span class="s1">'paths'</span><span class="p">:</span> <span class="s2">"traj.dump"</span><span class="p">,</span>
+ <span class="s1">'metadata'</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s1">'project'</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'trajectory'</span><span class="p">,</span>
+ <span class="s1">'material'</span><span class="p">:</span> <span class="s1">'gold'</span><span class="p">,</span>
+ <span class="s1">'velocity'</span><span class="p">:</span> <span class="n">velocity</span><span class="p">,</span>
+ <span class="s1">'cluster'</span><span class="p">:</span> <span class="n">cluster</span><span class="p">,</span>
+ <span class="s1">'cluster_size'</span><span class="p">:</span> <span class="n">metadata</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">]}</span> <span class="p">}</span> <span class="p">),</span>
+ <span class="n">AddFilesTask</span><span class="p">(</span> <span class="p">{</span>
+ <span class="s1">'compress'</span><span class="p">:</span><span class="kc">True</span> <span class="p">,</span>
+ <span class="s1">'paths'</span><span class="p">:</span> <span class="s2">"log.lammps"</span><span class="p">,</span>
+ <span class="s1">'metadata'</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s1">'project'</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'log'</span><span class="p">,</span>
+ <span class="s1">'material'</span><span class="p">:</span> <span class="s1">'gold'</span><span class="p">,</span>
+ <span class="s1">'velocity'</span><span class="p">:</span> <span class="n">velocity</span><span class="p">,</span>
+ <span class="s1">'cluster'</span><span class="p">:</span> <span class="n">cluster</span><span class="p">,</span>
+ <span class="s1">'cluster_size'</span><span class="p">:</span> <span class="n">metadata</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">]}</span> <span class="p">}</span> <span class="p">)</span> <span class="p">]</span>
+
+ <span class="n">fw_D_deposit</span> <span class="o">=</span> <span class="n">Firework</span><span class="p">(</span>
+ <span class="p">[</span><span class="o">*</span><span class="n">ft</span><span class="p">,</span> <span class="n">st</span><span class="p">,</span> <span class="o">*</span><span class="n">at</span><span class="p">],</span>
+ <span class="n">name</span> <span class="o">=</span> <span class="s2">"Deposit, N = </span><span class="si">{:d}</span><span class="s2">, v = </span><span class="si">{:.2f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metadata</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">],</span><span class="n">velocity</span><span class="p">),</span>
+ <span class="n">spec</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'_category'</span><span class="p">:</span> <span class="s2">"bwcloud_noqueue"</span><span class="p">,</span>
+ <span class="s1">'_files_in'</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s1">'data_file_cluster'</span><span class="p">:</span> <span class="s2">"cluster.lammps"</span><span class="p">,</span>
+ <span class="s1">'data_file_substrate'</span><span class="p">:</span> <span class="s2">"substrate.lammps"</span> <span class="p">},</span>
+ <span class="s1">'metadata'</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s1">'project'</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s1">'step'</span><span class="p">:</span> <span class="s1">'deposit'</span><span class="p">,</span>
+ <span class="s1">'cluster'</span><span class="p">:</span> <span class="n">cluster</span><span class="p">,</span>
+ <span class="s1">'cluster_size'</span><span class="p">:</span> <span class="n">metadata</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">],</span>
+ <span class="s1">'velocity'</span><span class="p">:</span> <span class="n">velocity</span><span class="p">}</span> <span class="p">}</span> <span class="p">)</span>
+
+ <span class="n">fws</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="p">[</span> <span class="n">fw_D_deposit</span> <span class="p">]</span> <span class="p">)</span>
+ <span class="n">parent_child_deps</span><span class="p">[</span><span class="n">fw_B_substrate</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">fw_D_deposit</span> <span class="p">)</span>
+ <span class="n">parent_child_deps</span><span class="p">[</span><span class="n">fw_C_cluster</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">fw_D_deposit</span> <span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [45]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">wf</span> <span class="o">=</span> <span class="n">Workflow</span><span class="p">(</span> <span class="n">fws</span><span class="p">,</span> <span class="n">parent_child_deps</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">"Gold cluster impact parametric study"</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [46]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">wf</span><span class="o">.</span><span class="n">to_file</span><span class="p">(</span><span class="s1">'wf_parametric.yaml'</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [47]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fw_ids</span> <span class="o">=</span> <span class="n">lp</span><span class="o">.</span><span class="n">add_wf</span><span class="p">(</span><span class="n">wf</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>2019-12-08 17:49:09,018 INFO Added a workflow. id_map: {-11: 23574, -10: 23575, -9: 23576, -8: 23577, -7: 23578, -6: 23579, -5: 23580, -4: 23581, -3: 23582, -2: 23583, -1: 23584}
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Results-overview">Results overview<a class="anchor-link" href="#Results-overview">¶</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [50]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="p">{</span> <span class="s2">"metadata.project"</span><span class="p">:</span> <span class="n">project_id</span> <span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [51]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># use underlying MongoDB functionality </span>
+<span class="n">fp</span><span class="o">.</span><span class="n">filepad</span><span class="o">.</span><span class="n">count_documents</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt output_prompt">Out[51]:</div>
+
+
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>21</pre>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [54]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># query all files of type "trajectory" within project</span>
+<span class="n">query</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">"metadata.project"</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s2">"metadata.material"</span><span class="p">:</span> <span class="s2">"gold"</span><span class="p">,</span>
+ <span class="s2">"metadata.type"</span><span class="p">:</span> <span class="s2">"trajectory"</span><span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [55]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fp</span><span class="o">.</span><span class="n">filepad</span><span class="o">.</span><span class="n">count_documents</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+<span class="c1"># that many documents matching query</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt output_prompt">Out[55]:</div>
+
+
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>3</pre>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now, we can use aggregation to get an overivew of the unique result datasets:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [56]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># use aggregation to determine unique parameter sets</span>
+<span class="c1"># see https://docs.mongodb.com/manual/aggregation</span>
+
+<span class="c1"># first filter all files by a general query, here as above</span>
+<span class="c1"># all trajectories in project</span>
+<span class="n">match_aggregation</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">"$match"</span><span class="p">:</span> <span class="n">query</span>
+ <span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [57]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># group by parameters of interest, i.e. cluster_size and velocity:</span>
+<span class="n">group_aggregation</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">"$group"</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s2">"_id"</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s2">"cluster_size"</span><span class="p">:</span> <span class="s2">"$metadata.cluster_size"</span><span class="p">,</span>
+ <span class="s2">"velocity"</span><span class="p">:</span> <span class="s2">"$metadata.velocity"</span>
+ <span class="p">}</span>
+ <span class="p">}</span>
+<span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [58]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">aggregation_pipeline</span> <span class="o">=</span> <span class="p">[</span> <span class="n">match_aggregation</span><span class="p">,</span> <span class="n">group_aggregation</span> <span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [59]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># this aggregation yields all unique parameter sets as documents</span>
+<span class="n">cursor</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">filepad</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="n">aggregation_pipeline</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [60]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we sort these sets by cluster_size</span>
+<span class="n">unique_parameter_sets</span> <span class="o">=</span> <span class="p">[</span> <span class="n">c</span><span class="p">[</span><span class="s2">"_id"</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span>
+ <span class="n">cursor</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">d</span><span class="p">:</span> <span class="n">d</span><span class="p">[</span><span class="s2">"_id"</span><span class="p">][</span><span class="s2">"cluster_size"</span><span class="p">])</span> <span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Thereby, we get a simple overview on the unique combinations of parameters "cluster_size" and "velocity" that reside in our database:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [61]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">unique_parameter_sets</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt output_prompt">Out[61]:</div>
+
+
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>[{'cluster_size': 923, 'velocity': -25},
+ {'cluster_size': 3871, 'velocity': -25},
+ {'cluster_size': 3871, 'velocity': -50}]</pre>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [62]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># next, we use a bit of cryptic pandas formatting to get a simple</span>
+<span class="c1"># two-dimensional visualization of existing and non-existing</span>
+<span class="c1"># data sets</span>
+<span class="n">unique_parameter_sets_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">unique_parameter_sets</span><span class="p">)</span>
+<span class="n">parameter_set_overview</span> <span class="o">=</span> <span class="n">unique_parameter_sets_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span>
+ <span class="nb">list</span><span class="p">(</span><span class="n">unique_parameter_sets_df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">unstack</span><span class="p">(</span>
+ <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">)</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span>
+ <span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="s1">'color: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">'green'</span> <span class="k">if</span> <span class="n">v</span> <span class="k">else</span> <span class="s1">'red'</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [63]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">parameter_set_overview</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt output_prompt">Out[63]:</div>
+
+
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+<style type="text/css">
+ #T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow0_col0 {
+ color: red;
+ } #T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow0_col1 {
+ color: green;
+ } #T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow1_col0 {
+ color: green;
+ } #T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow1_col1 {
+ color: green;
+ }</style>
+<table id="T_62f0cd3e_19dc_11ea_899a_fa163eeb709b">
+<thead> <tr>
+ <th class="index_name level0">velocity</th>
+ <th class="col_heading level0 col0">-50</th>
+ <th class="col_heading level0 col1">-25</th>
+ </tr> <tr>
+ <th class="index_name level0">cluster_size</th>
+ <th class="blank"></th>
+ <th class="blank"></th>
+ </tr></thead>
+<tbody> <tr>
+ <th id="T_62f0cd3e_19dc_11ea_899a_fa163eeb709blevel0_row0" class="row_heading level0 row0">923</th>
+ <td id="T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow0_col0" class="data row0 col0">False</td>
+ <td id="T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow0_col1" class="data row0 col1">True</td>
+ </tr> <tr>
+ <th id="T_62f0cd3e_19dc_11ea_899a_fa163eeb709blevel0_row1" class="row_heading level0 row1">3871</th>
+ <td id="T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow1_col0" class="data row1 col0">True</td>
+ <td id="T_62f0cd3e_19dc_11ea_899a_fa163eeb709brow1_col1" class="data row1 col1">True</td>
+ </tr></tbody>
+</table>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Thsi might look like overkill for only two parameters, but will help to keep an overview when mapping out a high-dimensional parametric space.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Results-anaylsis">Results anaylsis<a class="anchor-link" href="#Results-anaylsis">¶</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>With properly formulated queries, evaluating the results selectively becomes easy.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="Large-cluster">Large cluster<a class="anchor-link" href="#Large-cluster">¶</a></h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [64]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># query a specific "trajectory" file</span>
+<span class="n">query</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">"metadata.project"</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s2">"metadata.type"</span><span class="p">:</span> <span class="s2">"trajectory"</span><span class="p">,</span>
+ <span class="s2">"metadata.velocity"</span><span class="p">:</span> <span class="o">-</span><span class="mi">25</span><span class="p">,</span>
+ <span class="s2">"metadata.cluster_size"</span><span class="p">:</span> <span class="mi">3871</span><span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [65]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">fp</span><span class="o">.</span><span class="n">filepad</span><span class="o">.</span><span class="n">count_documents</span><span class="p">(</span><span class="n">query</span><span class="p">)</span> <span class="p">)</span>
+<span class="c1"># that many documents matching query</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>1
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [66]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the FilePad yields trajectory and attached metadata</span>
+<span class="n">traj_file</span><span class="p">,</span> <span class="n">traj_metadata</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">get_file_by_query</span><span class="p">(</span><span class="n">query</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [67]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pprint</span><span class="p">(</span><span class="n">traj_metadata</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>{'_id': ObjectId('5ded2ad811940f336850cbe5'),
+ 'compressed': True,
+ 'gfs_id': '5ded2ad611940f336850cbaa',
+ 'identifier': '/mnt/dat/fw/launcher_2019-12-08-16-53-00-210573/traj.dump',
+ 'metadata': {'cluster': 'large_cluster',
+ 'cluster_size': 3871,
+ 'material': 'gold',
+ 'project': 'fireworks-hands-on-2019-12-09',
+ 'type': 'trajectory',
+ 'velocity': -25},
+ 'original_file_name': 'traj.dump',
+ 'original_file_path': '/mnt/dat/fw/launcher_2019-12-08-16-53-00-210573/traj.dump'}
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [68]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">traj_large</span> <span class="o">=</span> <span class="n">ase</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read</span><span class="p">(</span>
+ <span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">(</span><span class="n">traj_file</span><span class="o">.</span><span class="n">decode</span><span class="p">()),</span>
+ <span class="nb">format</span><span class="o">=</span><span class="s1">'lammps-dump-text'</span><span class="p">,</span>
+ <span class="n">index</span><span class="o">=</span><span class="s1">':'</span><span class="p">,</span>
+ <span class="n">specorder</span><span class="o">=</span><span class="p">[</span><span class="n">ase</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">atomic_numbers</span><span class="p">[</span><span class="s1">'Au'</span><span class="p">]])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [69]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">w</span> <span class="o">=</span> <span class="n">ase</span><span class="o">.</span><span class="n">visualize</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">traj_large</span><span class="p">,</span> <span class="n">viewer</span><span class="o">=</span><span class="s1">'ngl'</span><span class="p">)</span>
+<span class="n">w</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">remove_unitcell</span><span class="p">()</span>
+<span class="n">w</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">nglrot</span><span class="p">)</span>
+<span class="n">w</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">center</span><span class="p">()</span>
+<span class="n">w</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">zoom</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="Small-cluster">Small cluster<a class="anchor-link" href="#Small-cluster">¶</a></h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [70]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># query a specific "trajectory" file</span>
+<span class="n">query</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">"metadata.project"</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s2">"metadata.type"</span><span class="p">:</span> <span class="s2">"trajectory"</span><span class="p">,</span>
+ <span class="s2">"metadata.velocity"</span><span class="p">:</span> <span class="o">-</span><span class="mi">25</span><span class="p">,</span>
+ <span class="s2">"metadata.cluster_size"</span><span class="p">:</span><span class="mi">923</span><span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [71]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">fp</span><span class="o">.</span><span class="n">filepad</span><span class="o">.</span><span class="n">count_documents</span><span class="p">(</span><span class="n">query</span><span class="p">)</span> <span class="p">)</span>
+<span class="c1"># that many documents matching query</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>1
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [72]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">traj_file</span><span class="p">,</span> <span class="n">traj_metadata</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">get_file_by_query</span><span class="p">(</span><span class="n">query</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [73]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pprint</span><span class="p">(</span><span class="n">traj_metadata</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>{'_id': ObjectId('5ded2b3411940f336850cc20'),
+ 'compressed': True,
+ 'gfs_id': '5ded2b3111940f336850cbf3',
+ 'identifier': '/mnt/dat/fw/launcher_2019-12-08-16-54-48-943523/traj.dump',
+ 'metadata': {'cluster': 'small_cluster',
+ 'cluster_size': 923,
+ 'material': 'gold',
+ 'project': 'fireworks-hands-on-2019-12-09',
+ 'type': 'trajectory',
+ 'velocity': -25},
+ 'original_file_name': 'traj.dump',
+ 'original_file_path': '/mnt/dat/fw/launcher_2019-12-08-16-54-48-943523/traj.dump'}
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [74]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">traj_small</span> <span class="o">=</span> <span class="n">ase</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read</span><span class="p">(</span>
+ <span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">(</span><span class="n">traj_file</span><span class="o">.</span><span class="n">decode</span><span class="p">()),</span>
+ <span class="nb">format</span><span class="o">=</span><span class="s1">'lammps-dump-text'</span><span class="p">,</span>
+ <span class="n">index</span><span class="o">=</span><span class="s1">':'</span><span class="p">,</span>
+ <span class="n">specorder</span><span class="o">=</span><span class="p">[</span><span class="n">ase</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">atomic_numbers</span><span class="p">[</span><span class="s1">'Au'</span><span class="p">]])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [75]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">v</span> <span class="o">=</span> <span class="n">ase</span><span class="o">.</span><span class="n">visualize</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">traj_small</span><span class="p">,</span> <span class="n">viewer</span><span class="o">=</span><span class="s1">'ngl'</span><span class="p">)</span>
+<span class="n">v</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">remove_unitcell</span><span class="p">()</span>
+<span class="n">v</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">nglrot</span><span class="p">)</span>
+<span class="n">v</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">center</span><span class="p">()</span>
+<span class="n">v</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">zoom</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">v</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+
+
+
+<div id="6f3cc15f-cd25-4581-9ed7-6ec58b35ab69"></div>
+<div class="output_subarea output_widget_view ">
+<script type="text/javascript">
+var element = $('#6f3cc15f-cd25-4581-9ed7-6ec58b35ab69');
+</script>
+<script type="application/vnd.jupyter.widget-view+json">
+{"model_id": "72e7ae269396473eb396f457021b2ce0", "version_major": 2, "version_minor": 0}
+</script>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [76]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">w</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+
+
+
+<div id="a11f9b0b-1c38-4836-905b-0fd03c02be88"></div>
+<div class="output_subarea output_widget_view ">
+<script type="text/javascript">
+var element = $('#a11f9b0b-1c38-4836-905b-0fd03c02be88');
+</script>
+<script type="application/vnd.jupyter.widget-view+json">
+{"model_id": "e5e4fe373616491b879f11e92f6e754c", "version_major": 2, "version_minor": 0}
+</script>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="Selective-analyisis-at-fixed-cluster-size">Selective analyisis at fixed cluster size<a class="anchor-link" href="#Selective-analyisis-at-fixed-cluster-size">¶</a></h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Here, we take a slice at fixed cluster size and compare thermodynamic
+observables along the trajectroies for slow and fast impact velocity.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [77]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compare along velocity dimension</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [78]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># query a specific "trajectory" file</span>
+<span class="n">query</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">"metadata.project"</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s2">"metadata.material"</span><span class="p">:</span> <span class="s2">"gold"</span><span class="p">,</span>
+ <span class="s2">"metadata.type"</span><span class="p">:</span> <span class="s2">"log"</span><span class="p">,</span>
+ <span class="s2">"metadata.cluster_size"</span><span class="p">:</span> <span class="mi">3871</span><span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [79]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">fp</span><span class="o">.</span><span class="n">filepad</span><span class="o">.</span><span class="n">count_documents</span><span class="p">(</span><span class="n">query</span><span class="p">)</span> <span class="p">)</span>
+<span class="c1"># that many documents matching query</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>2
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [80]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">files</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">get_file_by_query</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [81]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Interseting thermodynamic data in LAMMPS log occurs between a table header </span>
+<span class="c1"># line always starting with "step", e.g. "Step KinEng PotEng TotEng Temp" and</span>
+<span class="c1"># a concluding line always starting with "Loop", e.g. "Loop time of 111.167 </span>
+<span class="c1"># on 2 procs for 4000 steps with 12671 atoms". We use this knowledge to</span>
+<span class="c1"># extract this table with a regular expression:</span>
+<span class="n">regex</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="s2">"(^Step(?:.(?!^Loop))*)"</span><span class="p">,</span> <span class="n">re</span><span class="o">.</span><span class="n">MULTILINE</span> <span class="o">|</span> <span class="n">re</span><span class="o">.</span><span class="n">DOTALL</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [82]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">content</span><span class="p">,</span> <span class="n">metadata</span> <span class="ow">in</span> <span class="n">files</span><span class="p">:</span>
+ <span class="n">m</span> <span class="o">=</span> <span class="n">regex</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="n">content</span><span class="o">.</span><span class="n">decode</span><span class="p">())[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># takes the first thermo output block</span>
+
+ <span class="n">d</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">(</span><span class="n">m</span><span class="p">),</span><span class="n">sep</span><span class="o">=</span><span class="s1">'\s+'</span><span class="p">)</span>
+ <span class="n">d</span><span class="p">[</span><span class="s2">"velocity"</span><span class="p">]</span> <span class="o">=</span> <span class="n">metadata</span><span class="p">[</span><span class="s2">"metadata"</span><span class="p">][</span><span class="s2">"velocity"</span><span class="p">]</span>
+
+ <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">,</span><span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [83]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'KinEng'</span><span class="p">,</span> <span class="s1">'PotEng'</span><span class="p">,</span><span class="s1">'Temp'</span><span class="p">,</span> <span class="s1">'Press'</span><span class="p">,]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [84]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">V</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">velocity</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span> <span class="c1"># unique velocities, should be -25 and -50</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [85]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cols</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="n">rows</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span><span class="n">cols</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">c</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">columns</span><span class="p">):</span>
+ <span class="n">ind</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span><span class="o">//</span><span class="n">cols</span><span class="p">,</span><span class="n">i</span><span class="o">%</span><span class="k">cols</span>)
+ <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">V</span><span class="p">:</span>
+ <span class="n">data</span><span class="p">[</span> <span class="n">data</span><span class="o">.</span><span class="n">velocity</span> <span class="o">==</span> <span class="n">v</span> <span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+ <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span><span class="n">x</span><span class="o">=</span><span class="s2">"Step"</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">c</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"v = </span><span class="si">{:d}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">v</span><span class="p">))</span>
+ <span class="n">ax</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img 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%0A">
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="Selective-analyisis-at-fixed-velocity">Selective analyisis at fixed velocity<a class="anchor-link" href="#Selective-analyisis-at-fixed-velocity">¶</a></h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Similarly, we analyisistake a slice at fixed velocity v = -25 and compare thermodynamic
+observables along the trajectroies of large and small cluster.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [86]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compare along cluster size dimension</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [91]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># query a specific "trajectory" file</span>
+<span class="n">query</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">"metadata.project"</span><span class="p">:</span> <span class="n">project_id</span><span class="p">,</span>
+ <span class="s2">"metadata.material"</span><span class="p">:</span> <span class="s2">"gold"</span><span class="p">,</span>
+ <span class="s2">"metadata.type"</span><span class="p">:</span> <span class="s2">"log"</span><span class="p">,</span>
+ <span class="s2">"metadata.velocity"</span><span class="p">:</span> <span class="o">-</span><span class="mi">25</span><span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [92]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">fp</span><span class="o">.</span><span class="n">filepad</span><span class="o">.</span><span class="n">count_documents</span><span class="p">(</span><span class="n">query</span><span class="p">)</span> <span class="p">)</span>
+<span class="c1"># that many documents matching query</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>2
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [93]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">files</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">get_file_by_query</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [94]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">content</span><span class="p">,</span> <span class="n">metadata</span> <span class="ow">in</span> <span class="n">files</span><span class="p">:</span>
+ <span class="n">m</span> <span class="o">=</span> <span class="n">regex</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="n">content</span><span class="o">.</span><span class="n">decode</span><span class="p">())[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># takes the first thermo output block</span>
+
+ <span class="n">d</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">(</span><span class="n">m</span><span class="p">),</span><span class="n">sep</span><span class="o">=</span><span class="s1">'\s+'</span><span class="p">)</span>
+ <span class="n">d</span><span class="p">[</span><span class="s2">"cluster_size"</span><span class="p">]</span> <span class="o">=</span> <span class="n">metadata</span><span class="p">[</span><span class="s2">"metadata"</span><span class="p">][</span><span class="s2">"cluster_size"</span><span class="p">]</span>
+
+ <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">,</span><span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [95]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">N</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">cluster_size</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span> <span class="c1"># unique cluster sizes</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [96]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cols</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="n">rows</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span><span class="n">cols</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">c</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">columns</span><span class="p">):</span>
+ <span class="n">ind</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span><span class="o">//</span><span class="n">cols</span><span class="p">,</span><span class="n">i</span><span class="o">%</span><span class="k">cols</span>)
+ <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">N</span><span class="p">:</span>
+ <span class="n">data</span><span class="p">[</span> <span class="n">data</span><span class="o">.</span><span class="n">cluster_size</span> <span class="o">==</span> <span class="n">n</span> <span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+ <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span><span class="n">x</span><span class="o">=</span><span class="s2">"Step"</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">c</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"N = </span><span class="si">{:d}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
+ <span class="n">ax</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img 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%0A">
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Here, storing data sets within FireWork's FilePad with proper metadata attached facilitates meaningful evaluation.</p>
+
+</div>
+</div>
+</div>
+ </div>
+ </div>
+</body>
+
+
+
+
+</html>
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diff --git a/demos/7_filepad/doc/gold_droplet_impact.mp4 b/demos/7_filepad/doc/gold_droplet_impact.mp4
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index 0000000000000000000000000000000000000000..8410780203043a78ced8aab065966d64db8a326b
Binary files /dev/null and b/demos/7_filepad/doc/gold_droplet_impact.mp4 differ
diff --git a/demos/7_filepad/fig/010_deposit_gold_cluster_zoom.png b/demos/7_filepad/fig/010_deposit_gold_cluster_zoom.png
new file mode 100755
index 0000000000000000000000000000000000000000..316fbb6589ab096f9f5918c4029f0159463be8da
Binary files /dev/null and b/demos/7_filepad/fig/010_deposit_gold_cluster_zoom.png differ
diff --git a/demos/7_filepad/fig/020_deposit_gold_cluster_parametric_study_zoom.png b/demos/7_filepad/fig/020_deposit_gold_cluster_parametric_study_zoom.png
new file mode 100755
index 0000000000000000000000000000000000000000..0581803b44eadf848d31518cc6d0acc8ebf3dfd4
Binary files /dev/null and b/demos/7_filepad/fig/020_deposit_gold_cluster_parametric_study_zoom.png differ
diff --git a/demos/7_filepad/infiles/A_minimize_cluster.in b/demos/7_filepad/infiles/A_minimize_cluster.in
new file mode 100644
index 0000000000000000000000000000000000000000..d6755c6f2fa887d2d404d93de7a7fdffbe4efe81
--- /dev/null
+++ b/demos/7_filepad/infiles/A_minimize_cluster.in
@@ -0,0 +1,32 @@
+# LAMMPS input files contain a list of commands that are executed in order of
+# appearance. General structure is
+# <command> <parameters>
+# All possible commands are listed at:
+# http://lammps.sandia.gov/doc/Section_commands.html
+# LAMMPS will ignore lines that start with a '#'
+
+dimension 3
+# The next statement is new. Default is "units lj". This sets units to
+# Angstroms (distance), ps (time), g/mol (mass), Kelvin (temperature), ...
+units metal
+# Nonperiodic - we want a cluster (in vacuum)
+boundary s s s
+atom_style atomic
+
+#read_data cluster_923.lammps
+read_data initial.lammps
+
+# This defines an embedded-atom method potential. Au_u3.eam is the parameter
+# file. Open it in an editor.
+pair_style eam/alloy
+pair_coeff * * Au-Grochola-JCP05.eam.alloy Au
+
+thermo 100
+thermo_style custom step ke pe etotal temp press
+thermo_modify norm no
+
+dump 1 all custom 100 traj.dump id type x y z vx vy vz
+
+minimize 0.0 1e-3 1000000 1000000
+
+write_data final.lammps
diff --git a/demos/7_filepad/infiles/A_minimize_substrate.in b/demos/7_filepad/infiles/A_minimize_substrate.in
new file mode 100644
index 0000000000000000000000000000000000000000..b8c2996fc7edbd288137029e75ce8e31bc633228
--- /dev/null
+++ b/demos/7_filepad/infiles/A_minimize_substrate.in
@@ -0,0 +1,30 @@
+# LAMMPS input files contain a list of commands that are executed in order of
+# appearance. General structure is
+# <command> <parameters>
+# All possible commands are listed at:
+# http://lammps.sandia.gov/doc/Section_commands.html
+# LAMMPS will ignore lines that start with a '#'
+
+dimension 3
+units metal
+boundary p p s
+atom_style atomic
+
+lattice fcc 4.08
+region substrate block -6 14 -6 14 -20 -15
+create_box 1 substrate
+create_atoms 1 region substrate
+group substrate region substrate
+
+pair_style eam/alloy
+pair_coeff * * Au-Grochola-JCP05.eam.alloy Au
+
+thermo 100
+thermo_style custom step ke pe etotal temp press
+thermo_modify norm no
+
+dump 1 all custom 100 traj.dump id type x y z vx vy vz
+
+minimize 0.0 1e-3 1000000 1000000
+
+write_data final.lammps
diff --git a/demos/7_filepad/infiles/B_equilibrate_cluster.in b/demos/7_filepad/infiles/B_equilibrate_cluster.in
new file mode 100644
index 0000000000000000000000000000000000000000..562881f33fd7455fb1281a56390d9b70d277d0e6
--- /dev/null
+++ b/demos/7_filepad/infiles/B_equilibrate_cluster.in
@@ -0,0 +1,36 @@
+# LAMMPS input files contain a list of commands that are executed in order of
+# appearance. General structure is
+# <command> <parameters>
+# All possible commands are listed at:
+# http://lammps.sandia.gov/doc/Section_commands.html
+# LAMMPS will ignore lines that start with a '#'
+
+dimension 3
+# The next statement is new. Default is "units lj". This sets units to
+# Angstroms (distance), ps (time), g/mol (mass), Kelvin (temperature), ...
+units metal
+# Nonperiodic, we want a cluster (in vacuum)
+boundary s s s
+atom_style atomic
+
+read_data initial.lammps
+
+# Set an initial temperature. Temperature are in Kelvins now!
+velocity all create 100 1229832
+
+pair_style eam/alloy
+pair_coeff * * Au-Grochola-JCP05.eam.alloy Au
+
+thermo 10
+thermo_style custom step ke pe etotal temp press
+thermo_modify norm no
+
+dump 1 all custom 1 traj.dump id type x y z vx vy vz
+
+fix 1 all nve
+
+# Timestep is in ps! Time steps are on the order of 1 fs.
+timestep 0.005
+run 1000
+
+write_data final.lammps
\ No newline at end of file
diff --git a/demos/7_filepad/infiles/B_equilibrate_substrate.in b/demos/7_filepad/infiles/B_equilibrate_substrate.in
new file mode 100644
index 0000000000000000000000000000000000000000..ecf0121f0d7921c6568477599bed3d34b95f0881
--- /dev/null
+++ b/demos/7_filepad/infiles/B_equilibrate_substrate.in
@@ -0,0 +1,36 @@
+# LAMMPS input files contain a list of commands that are executed in order of
+# appearance. General structure is
+# <command> <parameters>
+# All possible commands are listed at:
+# http://lammps.sandia.gov/doc/Section_commands.html
+# LAMMPS will ignore lines that start with a '#'
+
+dimension 3
+# The next statement is new. Default is "units lj". This sets units to
+# Angstroms (distance), ps (time), g/mol (mass), Kelvin (temperature), ...
+units metal
+# Nonperiodic, we want a cluster (in vacuum)
+boundary p p s
+atom_style atomic
+
+read_data initial.lammps
+
+# Set an initial temperature. Temperature are in Kelvins now!
+velocity all create 100 1229832
+
+pair_style eam/alloy
+pair_coeff * * Au-Grochola-JCP05.eam.alloy Au
+
+thermo 1000
+thermo_style custom step ke pe etotal temp press
+thermo_modify norm no
+
+dump 1 all custom 100 traj.dump id type x y z vx vy vz
+
+fix 1 all nve
+
+# Timestep is in ps! Time steps are on the order of 1 fs.
+timestep 0.001
+run 1000
+
+write_data final.lammps
\ No newline at end of file
diff --git a/demos/7_filepad/infiles/C_melt_cluster.in b/demos/7_filepad/infiles/C_melt_cluster.in
new file mode 100644
index 0000000000000000000000000000000000000000..8337c6381c87224d4ea8f3be234bf85baf7565c3
--- /dev/null
+++ b/demos/7_filepad/infiles/C_melt_cluster.in
@@ -0,0 +1,43 @@
+# LAMMPS input files contain a list of commands that are executed in order of
+# appearance. General structure is
+# <command> <parameters>
+# All possible commands are listed at:
+# http://lammps.sandia.gov/doc/Section_commands.html
+# LAMMPS will ignore lines that start with a '#'
+
+dimension 3
+# The next statement is new. Default is "units lj". This sets units to
+# Angstroms (distance), ps (time), g/mol (mass), Kelvin (temperature), ...
+units metal
+# Nonperiodic, we want a cluster (in vacuum)
+boundary s s s
+atom_style atomic
+
+read_data initial.lammps
+
+# Set an initial temperature. Temperature are in Kelvins now!
+velocity all create 300 1229832
+
+pair_style eam/alloy
+pair_coeff * * Au-Grochola-JCP05.eam.alloy Au
+
+thermo 1000
+thermo_style custom step ke pe etotal temp press
+thermo_modify norm no
+
+dump 1 all custom 100 traj.dump id type x y z vx vy vz
+
+fix 1 all nve
+# This rescales the temperature every 1000 time steps.
+fix 2 all temp/rescale 500 100 1800 0.0 1.0
+# This is new. Output a couple of (global) quantities to a separate file, akin
+# to the thermo command above. Quantities are averaged.
+# After rescaling velocities, we wait for 500 steps for relaxation and then
+# average over the remaining 500 steps.
+fix 3 all ave/time 1 500 1000 c_thermo_pe c_thermo_temp c_thermo_press file ave.out
+
+# Timestep is in ps! Time steps are on the order of 1 fs.
+timestep 0.01
+run 10000
+
+write_data final.lammps
diff --git a/demos/7_filepad/infiles/D_deposit.in b/demos/7_filepad/infiles/D_deposit.in
new file mode 100644
index 0000000000000000000000000000000000000000..29202f39eca7ebf06a42c2405d3fdb190c55b53d
--- /dev/null
+++ b/demos/7_filepad/infiles/D_deposit.in
@@ -0,0 +1,38 @@
+# LAMMPS input files contain a list of commands that are executed in order of
+# appearance. General structure is
+# <command> <parameters>
+# All possible commands are listed at:
+# http://lammps.sandia.gov/doc/Section_commands.html
+# LAMMPS will ignore lines that start with a '#'
+variable vel index -25
+variable steps index 4000
+# override on command line, i.e. via "-v vel -30"
+
+dimension 3
+units metal
+boundary p p s
+atom_style atomic
+
+read_data cluster.lammps
+group cluster type 1
+read_data substrate.lammps add append offset 0 0 0 0 0 shift 0 0 0
+
+group substrate subtract all cluster
+
+velocity cluster set 0 0 ${vel} sum yes
+
+pair_style eam/alloy
+pair_coeff * * Au-Grochola-JCP05.eam.alloy Au
+
+thermo 10
+thermo_style custom step ke pe etotal temp press
+thermo_modify norm no
+
+dump 1 all custom 100 traj.dump id type x y z vx vy vz
+
+fix 1 all nve
+
+timestep 0.001
+run ${steps}
+
+write_data final.lammps
diff --git a/demos/7_filepad/infiles/NOTE b/demos/7_filepad/infiles/NOTE
new file mode 100644
index 0000000000000000000000000000000000000000..8fa14c002756117b77d16bbb5101697a9c8b6c68
--- /dev/null
+++ b/demos/7_filepad/infiles/NOTE
@@ -0,0 +1,10 @@
+The third-party EAM potential file `Au-Grochola-JCP05.eam.alloy` by
+
+ G. Grochola, S.P. Russo, and I.K. Snook (2005), "On fitting a gold embedded
+ atom method potential using the force matching method", The Journal of
+ Chemical Physics, 123(20), 204719. DOI: 10.1063/1.2124667.
+
+has been excluded from this repository. You will find it publically available
+for download at https://www.ctcms.nist.gov/potentials/system/Au/ :
+
+https://www.ctcms.nist.gov/potentials/Download/2005--Grochola-G-Russo-S-P-Snook-I-K--Au/2/Au-Grochola-JCP05.eam.alloy
\ No newline at end of file
diff --git a/demos/7_filepad/infiles/cluster_3871.lammps b/demos/7_filepad/infiles/cluster_3871.lammps
new file mode 100644
index 0000000000000000000000000000000000000000..66c1bba76ef2d8b0b8e358345c3334963d1f42b5
--- /dev/null
+++ b/demos/7_filepad/infiles/cluster_3871.lammps
@@ -0,0 +1,3882 @@
+cluster_3871.lammps (written by ASE)
+
+3871 atoms
+1 atom types
+0.0 46.680200000 xlo xhi
+0.0 46.680199999 ylo yhi
+0.0 46.680199999 zlo zhi
+
+
+Atoms
+
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diff --git a/demos/7_filepad/infiles/cluster_923.lammps b/demos/7_filepad/infiles/cluster_923.lammps
new file mode 100644
index 0000000000000000000000000000000000000000..0a2073a39174b0837aae66ad32d0b609e8c08a1d
--- /dev/null
+++ b/demos/7_filepad/infiles/cluster_923.lammps
@@ -0,0 +1,934 @@
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diff --git a/docs/agenda.rst b/docs/agenda.rst
index 3e522e06cab842cb1fdc761ae15dbd641605d16b..30fb179d23d0c84c7fcac34b49cdc26469f69a77 100644
--- a/docs/agenda.rst
+++ b/docs/agenda.rst
@@ -60,5 +60,10 @@ Advanced and productive use
* Recover from failures
* Detect duplicates
* Detect lost runs
-* Use FilePad to store files
-* Query and analyse data from fireworks and workflows
\ No newline at end of file
+* Query and analyse data from fireworks and workflows
+
+FireWorks, FilePad and Metadata
+===============================
+
+* Use FilePad to store files and query files using metadata
+* A demo as Jupyter notebook in **demos/7_filepad**