diff --git a/.gitignore b/.gitignore
index 2111531..fc0d738 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,4 +6,5 @@ dist/
/build/
nasapy.egg-info/
*.pyc
-*.DS_Store
\ No newline at end of file
+*.DS_Store
+*.ipynb_checkpoints
\ No newline at end of file
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0e5c3fc..7acbd3f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,9 @@
# Version History
+## Version 0.2.6
+
+- `sentry` function now returns a summary object when `return_df=True` and a `des` or `spk` parameter are not specified.
+
## Version 0.2.5
- `sentry` function now returns results as expected when not returning a pandas DataFrame.
diff --git a/README.md b/README.md
index 94c5b33..600e913 100644
--- a/README.md
+++ b/README.md
@@ -252,6 +252,56 @@ fireballs(date_min='2019-01-01')
fireballs(date_min='2000-01-01', date_max='2020-01-01', return_df=True)
~~~
+#### Jet Propulsion Laboratory/Solar System Dynamics small body mission design suite API
+
+~~~ python
+# Search for mission design data for SPK-ID 2000433
+r = mission_design(spk=2000433)
+# Print the object data from the returned dictionary object.
+r['object']
+~~~
+
+#### Get Data on Near-Earth Object Human Space Flight Accessible Targets
+
+~~~ python
+# Get all available summary data for NHATS objects.
+n = nhats()
+# Get summary data as a pandas DataFrame
+n = nhats(return_df=True)
+# Get the results from a 'standard' search on the NHATS webpage.
+nhats(delta_v=6, duration=360, stay=8, magnitude=26, launch='2020-2045', orbit_condition_code=7)
+# Return data for a specific object by its designation
+nhats(des=99942)
+~~~
+
+#### Get Data from NASA's Center for Near-Earth Object Studies (CNEOS) Scout system
+
+~~~ python
+# Get all available summary data.
+scout()
+# Return all summary data as a pandas DataFrame.
+scout(return_df=True)
+# Return data and plot files for a specific object by its temporary designation. Note the object may no longer
+# exist in the current database
+scout(tdes='P20UvyK')
+# Get ephemeris data for a specific object at the current time with a Field of View diameter of 5 arc-minutes
+# with a limiting V-magnitude of 23.1.
+scout(tdes='P20UvyK', fov_diam=5, fov_vmag=23.1)
+~~~
+
+#### Get Data from the Center for Near Earth Object Studies (CNEOS) Sentry system
+
+~~~ python
+# Get summary data for available sentry objects.
+sentry()
+# Get summary data as a pandas DataFrame
+sentry(return_df=True)
+# Get data for a specific Sentry object by its designation.
+sentry(des=99942)
+# Get data for objects removed from the Sentry system.
+sentry(removed=1)
+~~~
+
Other function examples
#### Getting the Julian and Modified Julian Date
diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle
index c0ad1bb..957c361 100644
Binary files a/docs/build/doctrees/environment.pickle and b/docs/build/doctrees/environment.pickle differ
diff --git a/docs/build/doctrees/versions.doctree b/docs/build/doctrees/versions.doctree
index f1bfdc0..3f8f259 100644
Binary files a/docs/build/doctrees/versions.doctree and b/docs/build/doctrees/versions.doctree differ
diff --git a/docs/build/html/_sources/versions.rst.txt b/docs/build/html/_sources/versions.rst.txt
index c747487..6b47987 100644
--- a/docs/build/html/_sources/versions.rst.txt
+++ b/docs/build/html/_sources/versions.rst.txt
@@ -4,6 +4,11 @@
Version History
===============
+Version 0.2.6
+-------------
+
+- :code:`sentry` function now returns a summary object when :code:`return_df=True` and a :code:`des` or :code:`spk` parameter are not specified.
+
Version 0.2.5
-------------
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index feab27d..d512ec3 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
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\ No newline at end of file
diff --git a/docs/build/html/versions.html b/docs/build/html/versions.html
index a7d4a11..c88fa4a 100644
--- a/docs/build/html/versions.html
+++ b/docs/build/html/versions.html
@@ -88,6 +88,7 @@
diff --git a/docs/source/versions.rst b/docs/source/versions.rst
index c747487..6b47987 100644
--- a/docs/source/versions.rst
+++ b/docs/source/versions.rst
@@ -4,6 +4,11 @@
Version History
===============
+Version 0.2.6
+-------------
+
+- :code:`sentry` function now returns a summary object when :code:`return_df=True` and a :code:`des` or :code:`spk` parameter are not specified.
+
Version 0.2.5
-------------
diff --git a/notebooks/Analyzing the Next Decade of Earth Close-Approaching Objects with nasapy.ipynb b/notebooks/Analyzing the Next Decade of Earth Close-Approaching Objects with nasapy.ipynb
new file mode 100644
index 0000000..cd929da
--- /dev/null
+++ b/notebooks/Analyzing the Next Decade of Earth Close-Approaching Objects with nasapy.ipynb
@@ -0,0 +1,410 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this example, we will walk through a possible use case of the `nasapy` library by extracting the next 10 years of close-approaching objects to Earth identified by NASA's Jet Propulsion Laboratory's Small-Body Database.\n",
+ "\n",
+ "Before diving in, import the packages we will use to extract and analyze the data. The data analysis library [pandas](https://pandas.pydata.org/) will be used to wrangle the data, while [seaborn](https://seaborn.pydata.org/) is used for plotting the data. The magic command [`%matplotlib inline`](https://ipython.readthedocs.io/en/stable/interactive/plotting.html#id1) is loaded to display the generated plots."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import nasapy\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `close_approach` method of the `nasapy` library allows one to access the JPL SBDB to extract data related to known meteoroids and asteroids within proximity to Earth. Setting the parameter `return_df=True` automatically coerces the returned JSON data into a pandas DataFrame. After extracting the data, we transform several of the variables into `float` type."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ca = nasapy.close_approach(date_min='2020-01-01', date_max='2029-12-31', return_df=True)\n",
+ "\n",
+ "ca['dist'] = ca['dist'].astype(float)\n",
+ "ca['dist_min'] = ca['dist_min'].astype(float)\n",
+ "ca['dist_max'] = ca['dist_max'].astype(float)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `dist` column of the returned data describes the nominal approach distance of the object in astronomical units (AU). An [astronomical unit](https://en.wikipedia.org/wiki/Astronomical_unit), or AU is roughly the distance of the Earth to the Sun, approximately 92,955,807 miles or 149,598,000 kilometers. Using the [`.describe`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html) method, we can display descriptive statistics that summarize the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "count 724.000000\n",
+ "mean 0.030809\n",
+ "std 0.012749\n",
+ "min 0.000252\n",
+ "25% 0.021632\n",
+ "50% 0.031987\n",
+ "75% 0.041480\n",
+ "max 0.049983\n",
+ "Name: dist, dtype: float64"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ca['dist'].describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We see the mean approach distance in AUs is approximately 0.031, which we can transform into miles: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2863896.3584931544"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "au_miles = 92955807.26743\n",
+ "ca['dist'].describe()['mean'] * au_miles"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Thus the average distance of the approaching objects to Earth over the next decade is about 2.86 million miles, which is more than 10 times the distance from the Earth to the Moon (238,900 miles).\n",
+ "\n",
+ "What about the closest approaching object to Earth within the next ten years? Using the [`.loc`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html) method, we can find the object with the closest approaching distance."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
des
\n",
+ "
orbit_id
\n",
+ "
jd
\n",
+ "
cd
\n",
+ "
dist
\n",
+ "
dist_min
\n",
+ "
dist_max
\n",
+ "
v_rel
\n",
+ "
v_inf
\n",
+ "
t_sigma_f
\n",
+ "
h
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
673
\n",
+ "
99942
\n",
+ "
199
\n",
+ "
2462240.407032288
\n",
+ "
2029-Apr-13 21:46
\n",
+ "
0.000252
\n",
+ "
0.000248
\n",
+ "
0.000257
\n",
+ "
7.43332261672295
\n",
+ "
5.84141384414201
\n",
+ "
< 00:01
\n",
+ "
19.7
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " des orbit_id jd cd dist dist_min \\\n",
+ "673 99942 199 2462240.407032288 2029-Apr-13 21:46 0.000252 0.000248 \n",
+ "\n",
+ " dist_max v_rel v_inf t_sigma_f h \n",
+ "673 0.000257 7.43332261672295 5.84141384414201 < 00:01 19.7 "
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ca.loc[ca['dist'] == ca['dist'].min()]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The closest approaching known object is expected to approach Earth near the end of the decade, on April 13, 2029, at a distance of 0.00023 AU. Transforming the astronomical units into miles, we can get a better sense of the approach distance of the object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Distance: 23440.92769543333\n",
+ "Minimum Distance: 644.2481158331191\n",
+ "Maximum Distance: 23874.510393069424\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Distance: ' + str(au_miles * ca['dist'].min()))\n",
+ "print('Minimum Distance: ' + str(au_miles * ca['dist_min'].min()))\n",
+ "print('Maximum Distance: ' + str(au_miles * ca['dist_max'].min()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Oh my! It looks like this object will approach Earth relatively close, at about 23,000 miles, in a range of [644, 23874] miles. For comparison, the maximum distance is about 1/10 of the distance from the Earth to the Moon.\n",
+ "\n",
+ "Let's get a sense of the number of approaching objects to Earth by year over the next decade. First, we extract the year of the approach date using a combination of [`.apply`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html) and [`to_datetime`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html) into a new column `approach_year`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ca['approach_year'] = ca['cd'].apply(lambda x: pd.to_datetime(x).year)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Using the [`.groupby`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html) method, we create a new DataFrame with the aggregated count of approaching objects for each year."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "approaches_by_year = ca.groupby('approach_year').count().reset_index()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use seaborn's [barplot](https://seaborn.pydata.org/generated/seaborn.barplot.html) function to plot the count of approaching objects for each year."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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kSY01C2RJ1kxyfpIv99e3SXJ2kp8nOTHJPVvVJkmSNEgtZ8j+BfjphOvvBN5bVdsBNwAHNalKkiRpwJoEsiRbAn8PHNtfD/Bk4JR+lROAZ7WoTZIkadBazZC9D3gNcEd/fWPgxqq6vb++ENiiRWGSJEmDNvBAluTpwHVVNX/i8FJWrWXcfk6SeUnmLVq0aFpqlCRJGqQWM2SPBZ6Z5JfA5+l2Vb4P2DDJjH6dLYFrlnbjqppbVbOravbMmTMHUa8kSdK0Gnggq6ojq2rLqpoFPA/4dlW9APgOsHe/2n7AaYOuTZIkqYVh+hyy1wL/muQyumPKjmtcjyRJ0kDMuPtVpk9VnQWc1V/+BbBry3okSZJaGKYZMkmSpNWSgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMDD2RJtkrynSQ/TXJJkn/px++T5JtJft6fbzTo2iRJklpoMUN2O3BYVT0QeBRwcJIdgSOAM6tqO+DM/rokSdLYG3ggq6prq+q8/vLvgJ8CWwB7ASf0q50APGvQtUmSJLXQ9BiyJLOAhwJnA5tV1bXQhTZg02XcZk6SeUnmLVq0aFClSpIkTZtmgSzJ+sCpwKFVdfPy3q6q5lbV7KqaPXPmzOkrUJIkaUCaBLIk96ALY5+pqi/0w79Osnm/fHPguha1SZIkDVqLd1kGOA74aVW9Z8Ki04H9+sv7AacNujZJkqQWZjT4nY8F9gUuSnJBP/Y64B3ASUkOAq4C9mlQmyRJ0sANPJBV1X8DWcbi3QdZiyRJ0jDwk/olSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDU2dIEsyR5JFiS5LMkRreuRJEmabkMVyJKsCXwI2BPYEXh+kh3bViVJkjS9hiqQAbsCl1XVL6rq/4DPA3s1rkmSJGlaDVsg2wK4esL1hf2YJEnS2EpVta7hL5LsAzy1ql7cX98X2LWqDpmwzhxgTn91B2DBAEvcBLh+gL9v0OxvtI1zf+PcG9jfqLO/0TXo3u5XVTOXtmDGAItYHguBrSZc3xK4ZuIKVTUXmDvIohZLMq+qZrf43YNgf6NtnPsb597A/kad/Y2uYept2HZZngtsl2SbJPcEngec3rgmSZKkaTVUM2RVdXuSVwBfB9YEPl5VlzQuS5IkaVoNVSADqKozgDNa17EMTXaVDpD9jbZx7m+cewP7G3X2N7qGprehOqhfkiRpdTRsx5BJkiStdgxkkiRJjRnIJEmSGjOQSdI0SpLWNWjFJNm4dQ1acaP2v7faB7IkM5Ks27oOrbgka/fnI/XPtzySrJfk3v3lcexv9rj+/yV5SZL71Ji+cyrJ25M8uHUd0yXJR4AD+8tj91yZZIck27SuY7r0j5drta5jKsbuTjYVSU4ATgXOT3JgklltK1q1khyQ5KGt65guSd6f5DPAj5LsXlU1Tg+cST4BHAcsSLJn39/YhLIk/w58CXhmkvVa17MqJfkK8I9V9dvWtUyHJKcBewK3jdN9crEkp9J9Rd+rk2xdVXe0rmlVSnIy3WPL+UnesPhF37hI8jHgZGBekv2T3Ld1TctjbJ68pirJscCOwNHAMcArgVcleVTTwlaRCU/mBybZuXU9q1r/gPkw4LPAN4CvJdlhXB44+/4eALyG7tsq3pbk3uMy29IH5zvoPgD6H4CnLw5lox6qk5wCbFJVT50wNtI9TZTkQOC+VbVLVf0PsFmSzfpvVxl5Sb4AbE73HYffAvbox8diG/ZhZQvgicCL6WYBx2amM8mngZ2ADwKfBz4KvDXJTk0LWw5D98GwA3Qv4DVVNR+Yn+Q84GDghUluqqqfti1vxSX5B7rvBH0RsB/wkiTHVNWFbStbNZK8GNiiqhaH568kuT/wGLrZpIxycEnyWib0l+RPwAbAk5NcDVxSVX9sWePKqqo7kpwBrAv8H/BSuheInwPWAf7QsLwVluQddDNja/TXD6QL1tv3/4NfbVrgqrEecBFAkqPontjvD5ye5GtV9ZWGta2Ufq/J9lW1U3/9JuAlwNxxeLHXv+jZDDisqv4MnJJkD+D+Sf4MXFdVlzctciX0M30bAHtW1Y3AWf02fD9wY5J/r6pfNy3yLoxF4p+KJGskmQH8Ld2UOwBVdRbwAbpXCk9bvG6LGleBbwOfqqpPA6+imwl8yTjMlPW7Ry4Hjum35T36RbcCDwQY5TDWOxl4AUCSNwD7Am+lewH1BeDQftmo7yq6Ddiqqg4Hzgae3z/BfyrJfUe0vw8BN/THVx0BvAH4HfC/wMlJ9oPR3HYTHg8DrJtke7r76UuAQ4A/As9NsnWjEldKkrWATy4OY71/BdZO8spGZa1qdwAPAvYH6LfhAXTPeccCRyd5RLPqVt4awGy678Fe7AfA9+h6fDIM8f9fVa2WJ2Av4GK6V7MTx58L/BbYvHWNK9nfmhMu7wx8B/gwsHM/9mBg/dZ1rmBv9wLu3V9eoz9/K/D+Cets1brOlexxcV8HA7MmjO9N98T3gNY1rqI+T6abkQA4Cbgd+MjE+++onYBZdDN8fwC2nDB+EPBrYNPWNa5kf9sCN9B95/AbJozvAlwB/F3rGldBjzPontzvAbyL7nuVm9e1inrbFbgEOAH4PfCWfvwhdC+M9m1d40r29690x4a/qL9+AnAk8C904Wwd+m8pGrbTarPLMsk+wEzgPGABXUA5Hdgvye1VdXq/6heBf2bEZg+X6O9n1R9MnGSNqrowyauA9wL/lORgYB9ge7p/yKHX97cpMB9YUFU3QLfrq1/lBrqp6sW7/A5O8qCq+l2Leqdqif7+sv2q6kP98nWq20353X6dkZoFXNr9s3+Vugawcz/78li64wEfAuyV5IvVP6IOsyV6u6KqfplkW+DRVbVwwrb7EXApXegcGUv0d3lVXZ7kELo9CmsCbwaoqguSnAus3azYFbC0/72qWryN7khyIvCDJKdV1WnNCl1BS9l+5yTZle7xf+Oq+jeAqvpJkiuBjdpVO3VL9HcR3V6EtYH3JdkXuDfdc/pDgSfVEB/usVp8l2WS/6I7QPNPdBvqM1X1kX4X3guAx9O9Uv8k3XFXrwJmV9V1jUqekiX6W4vuQPeP0m3fO5KsWVV/TrIZ3azgGsAeVXVus6Kn4O7669d5Xb/sCrpXtE8bp/4mrHsI3YG4T6mqRYOudUUso7+5/X1yD+BwugfLw6vqE0mOBo6tqquaFb2cltHbsVV125LHMvbb7rnA31fVTU0KnqKl9dc/dm5Ktyv9HXS7uk6mmyE7ku6x88pGJU/Jcjx2pqqqv09uC7x8VLYdLLO/j1XV7ek+VeBCuv+7uf0L9TcBj6mqyxqVPCV38dy+BnBfuhcMv+q35evoXvTtA/xxKF/stZ6im+4T8G7g+xOuH043XXuP/vrWdPvTF9Ide/Vz4GGt617J/i5a3N8S6x5Ed6zVTq3rXtX90U1T3wZcCzy8dd3T0N+WwFF0u9N3aV33quoPeDjd8R0jt5tkCttuI+CN/bZ7SOu6V7K/iydsu7WAJ9HNLH0V+DHw0NZ1r+rt1y87EDgXuFfruldlf8DLgOvo3k16+Rhsv4uX3H50e06OBm4c9sfO5gVM8wbbFPgMsNuEsbWAn9Ad2JgJ4/eim/a8T+u6V1F/D+SvM6Che/XwcUYrbC5Xf/34M+hmx0YpbC7v9tsQeBZwxog9od9VfztO6G/kjtecwrZbj+5jE749Yk92U3nsXIvumKuROSZ1Ko+dE5Zv1rruVdTfxP+9Neje4PYIuo8yaV77quyvH18fOH4UHjvH+hiyqrouybvpDqT9yzDdg8dfPggvyfrVHWs0EscbLXY3/W1U/b0RWLeq/pDkxTVCb91e3v7SfVL/WcAja0R2M8OUtt/vquq/kpxZI3JMHNxtfxvSPRn8GRiZXUCLTWHb3Up38PsPq+rmAZe5wqb42Ln4ONSROB4VprT91qZ7Ew01xB+XsKTl+N9bbJ0akd2TE91dfxOeG9arqt8nOXAUnvvGMpAlmU23cS6tqvP6sdD1W3RTlzdWVfVvQ980yXuq+1yWobe8/fXji/t7L92T39CbYn/7081svntUwsqK9NffP8eqv+qOIRup/78VvG++Z1TCmI+dS33sHLv++u23P91jy3vrr29iGGrj/tw3doEsyRfppmBvBTZO8jLgR1V1U5Lb6V6VF3BTkpcC76M7CHUkNthK9Dcq/3Ar0t8jRuHVD6xUf+N+/xz6/tx2o7vtwP5YPZ8bRqY/YLyOIQNeAVxA9zkjawNvpzvg9KX0x4bRHU/1ZeBrdAfZjtIxVfZnf0N7Guf+xrk3+7O/YT+Ne3+LT+M2Q7YB3bEaiz9n5Mgk/0v3LsrrgVPoDrKdBWxDd8zRxQ3qXFH2Z3/DbJz7G+fewP7sb7iNe3+d1olwFafo59C9dXfHJcbfSPexFpv01/diBN5xYX/2N0qnce5vnHuzP/sb9tO497f4NPIfDJvkeXRvgT2fLikfRveumPdU1RUT1vsRcHxVHdOk0BVkf39Zz/6G0Dj3N869gf1NWM/+htC497c0I/X1QEvqD/I7lO7DCY+l+7TvL9B92OvL031x6mLXMkJvywb7s7/hNs79jXNvYH/2N9zGvb9lGdljyJK8h+57uB7VX3858E5gO7oD//4RODbJl/rrTwKOaFTulNmf/Q2zce5vnHsD+8P+htq493dXRjKQJdkI2JhuI5FkBnAc3ReIbl5Vpya5DHgi8PfAIrovFf1Zo5KnxP7sb5iNc3/j3BvYn/0Nt3Hv7+6M7DFkSbal+4LQayaMXQAcVlVnLrHuGjUin1O1mP3daV37GzLj3N849wb2t8S69jdkxr2/uzKSM2QAVXX54stJ7gHcQffl0r/rx14MrAt8uEbpg+F69md/w2yc+xvn3sD+7G+4jXt/d2VkA9kS7qjua1j+BFyT5ADgw4zap/Qum/2NNvsbXePcG9jfqLO/MTKyuyyXJt07MzYAHgbsXv13XY0L+xtt9je6xrk3sL9RZ3/jYSxmyJK/fLnotsADgYfWKH5K7zLY32izv9E1zr2B/Y06+xsv4zZD9nfAr6rq0ta1TAf7G232N7rGuTewv1Fnf+NhrAKZJEnSKBrpT+qXJEkaBwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkl3I8lZSWa3rkPS+DKQSRpLScbim0gAkqzZugZJ08tAJmkoJPmvJPOTXJJkTj/2+yTvTnJekjOTzOzHz0ryviQ/THJxkl378aOSzE3yDeCTSdZO8okkFyU5P8mT+vVmJfl+/3PPS/KYCXW8pl//J0neMaHEfZKck+RnSR5/F318P8kuE67/IMnOSdZL8vEk5/a17HVXtSTZLcl3knwWuGiV/aElDaWxeQUpaeQdWFW/TbIOcG6SU4H1gPOq6rAkbwTeBLyiX3+9qnpMkicAHwd26scfDjyuqv6Y5DCAqnpwkgcA30iyPXAd8HdV9ack2wGfA2Yn2RN4FvDIqrolyX0m1DejqnZN8rS+jqcso49jgQ4boEoAAAI9SURBVP2BQ/vftVZVXZjkbcC3q+rAJBsC5yT51rJq6X/WrsBOVXXFCv5NJY0IZ8gkDYtXJvkJ8GNgK2A74A7gxH75p4HHTVj/cwBV9T1ggz7kAJxeVX/sLz8O+FS/3v8AVwLbA/cAPpbkIuBkYMd+/acAn6iqW/rb/HbC7/tCfz4fmHUXfZwMPD3JPYADgeP78f8HHJHkAuAsYG1g67uoBeAcw5i0enCGTFJzSXajC0OP7memzqILLEuqZVyeeP0PE3/0Mn7lq4BfAw+he2H6pwnrL+sLfm/tz//MXTx29vV/E9gLeA5/ne0K8OyqWjBx/SRHLaOWJXuRNMacIZM0DO4N3NCHmQcAj+rH1wD27i//E/DfE27zXIAkjwNuqqqblvJzvwe8oF9ve7oZqQX977u2qu4A9gUWHzT/DeDAJOv2t7nPpJ+4fI4FPgCcO2GW7evAIUnS/+yHTuh9abVIWo0YyCQNg68BM5JcCLyZbrcldDNED0oyH3gycPSE29yQ5IfAR4GDlvFzPwys2e8OPBHYv6pu7cf3S/Jjul2YfwCoqq8BpwPz+l2Lh69IM1U1H7gZ+MSE4TfT7Z68MMnF/fXFNU6qRdLqJVXLmp2XpLaS/L6q1l/K+FnA4VU1b/BV3b0kf0N3nNgD+pkvSbpLzpBJ0iqU5EXA2cDrDWOSlpczZJK0ApI8FXjnEsNXVNU/tKhH0mgzkEmSJDXmLktJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElq7P8D3Tnm//X9GB0AAAAASUVORK5CYII=\n",
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