Repository: SpatioTemporal/pystare (language: python)

Issue:
Whither to_utc_approximate?
AttributeError: module 'pystare' has no attribute 'to_utc_approximate'?


Discussion / hints:
It's now core._to_utc_approximate.

So what's the right way to get a utc datetime from a temporal id?
for now, we have [to_stare_timestring](https://pystare.readthedocs.io/en/latest/reference/temporal.html)

Other than that, we could do 
1. [to_julian_date](https://pystare.readthedocs.io/en/latest/reference/api2/pystare.temporal.to_julian_date.html#pystare.temporal.to_julian_date) and then format with [astropy](
https://docs.astropy.org/en/stable/time/index.html)
2. [to_ms_since_epoch_utc](https://pystare.readthedocs.io/en/latest/reference/api2/pystare.temporal.to_ms_since_epoch_utc.html#pystare.temporal.to_ms_since_epoch_utc) and then format with [datetime](https://docs.python.org/3/library/datetime.html)

Full roundtrips:
```python
import pystare
import astropy.time
import datetime

# Create the TIVs
times = ['2021-09-03', '2021-07-17 11:16']
times = astropy.time.Time(series, scale='utc', format='datetime64')
tivs = pystare.from_julian_date(times.jd1, times.jd2, scale='utc', forward_res=48, reverse_res=48)

# Back via JD
jd = pystare.to_julian_date(tivs, scale='utc')
times = astropy.time.Time(val=jd[0], val2=jd[1], format='jd')
times.to_datetime()

# Back via MS
mss = pystare.to_ms_since_epoch_utc(tivs)
dts = [datetime.datetime.utcfromtimestamp(ms/1000) for ms in mss]
[dt.isoformat() for dt in dts]
```

I shall add those to the documentation
```


Fix patch:
diff --git a/README.md b/README.md
index 5f57f06..b67f9f5 100644
--- a/README.md
+++ b/README.md
@@ -4,14 +4,17 @@
 PySTARE exposes the STARE library to python.
 
 
-## Requirements:
+## Requirements
 Pystare requires STARE to be installed.
-It expects either 
-    - libSTARE.a in /usr/local/lib/ and STARE.h in /usr/local/include/ or
-    - the variables STARE_LIB_DIR and STARE_INCLUDE_DIR to be set e.g. with:
+It expects either:
 
-    export STARE_INCLUDE_DIR=/path/to/directory-containing-stare.h/
-    export STARE_LIB_DIR=/path/to/directory-containing-stare.a/
+- ```libSTARE.a``` in /usr/local/lib/ and STARE.h in /usr/local/include/ or
+- the variables STARE_LIB_DIR and STARE_INCLUDE_DIR to be set e.g. with:
+
+```shell
+export STARE_INCLUDE_DIR=/path/to/directory-containing-stare.h/
+export STARE_LIB_DIR=/path/to/directory-containing-stare.a/
+```
 
 
 Build and install STARE e.g. with:
@@ -42,79 +45,86 @@ Wheels for manylinux exist on pypi
     pip3 install pystare/
     
 ### Manual build
-    
-    python3 setup.py build_ext --inplace 
-    python3 setup.py bdist_wheel
-    python3 setup.py sdist
-    
+
+```shell
+python3 setup.py build_ext --inplace 
+python3 setup.py bdist_wheel
+python3 setup.py sdist
+```
     
 ## Tests
 pystare uses [pytest](https://docs.pytest.org/en/6.2.x/). Pytest is configured in ```pytest.ini.```
 
-Run```pytest``` to run all tests.
+Run ```pytest``` to run all tests.
 
 To run the doctest,
 
-```
+```shell
 pytest --doctest-modules 
 ```
 
 To run tests of a specific module
 
-```
+```shell
 pytest /path/to/module.py
 ```
 
 ## Documentation
 pystare uses sphinx
 
+```shell
+pip install sphinx-markdown-tables sphinx-automodapi myst_parser nbsphinx numpydoc pydata-sphinx-theme
+```
     
-    pip install sphinx-markdown-tables sphinx-automodapi myst_parser nbsphinx numpydoc pydata-sphinx-theme
 
 ## Usage
 
 ### Spatial
-
-    import numpy
-    import pystare
+```python
+import numpy
+import pystare
     
-    print('Spatial tests')
-    lat = numpy.array([30,45,60], dtype=numpy.double)
-    lon = numpy.array([45,60,10], dtype=numpy.double)
 
-    indices = pystare.from_latlon(lat, lon, 12)
-    print('0 indices: ', [hex(i) for i in indices])
+lat = numpy.array([30,45,60], dtype=numpy.double)
+lon = numpy.array([45,60,10], dtype=numpy.double)
+
+indices = pystare.from_latlon(lat, lon, 12)
+print('0 indices: ', [hex(i) for i in indices])
 
-    lat, lon = pystare.to_latlon(indices)
-    print(lat, lon)
+lat, lon = pystare.to_latlon(indices)
+print(lat, lon)
 
-    lat, lon, level = pystare.to_latlonlevel(indices)
-    print(lat, lon, level)
+lat, lon, level = pystare.to_latlonlevel(indices)
+print(lat, lon, level)
 
-    level = pystare.to_level(indices)
-    print(level)
+level = pystare.to_level(indices)
+print(level)
 
-    area = pystare.to_area(indices)
-    print(area)
+area = pystare.to_area(indices)
+print(area)
+```
+ 
 
 ### Temporal
+```python
+import numpy
+import pystare
 
-    import numpy
-    import pystare
-    
-    print('Datetime tests')
-    datetime = numpy.array(['1970-01-01T00:00:00', 
+datetime = numpy.array(['1970-01-01T00:00:00', 
                         '2000-01-01T00:00:00', 
                         '2002-02-03T13:56:03.172', 
                         '2016-01-05T17:26:00.172'], dtype=numpy.datetime64)
-    print(datetime)
-    print(datetime.astype(numpy.int64))
+print(datetime)
+print(datetime.astype(numpy.int64))
     
-    index = pystare.from_utc(datetime.astype(numpy.int64), 6)
-    print([hex(i) for i in index])
+index = pystare.from_utc(datetime.astype(numpy.int64), 6)
+print([hex(i) for i in index])
+
+index = pystare.from_utc(datetime.astype(numpy.int64), 27)
+print([hex(i) for i in index])
+
+```
 
-    index = pystare.from_utc(datetime.astype(numpy.int64), 27)
-    print([hex(i) for i in index])
 
     
 
diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst
new file mode 100644
index 0000000..ec8e870
--- /dev/null
+++ b/docs/source/examples/index.rst
@@ -0,0 +1,16 @@
+.. _examples:
+
+Examples
+====================================
+
+.. toctree::
+   :maxdepth: 2
+
+    Spatial Conversions <notebooks/spatial_conversions.ipynb>
+    Temporal <notebooks/temporal.ipynb>
+    Expand Intervals <notebooks/expand_intervals.ipynb>
+    Intersect <notebooks/intersect_single_res.ipynb>
+    Example Visualization 1 <notebooks/example-viz-1.ipynb>
+    Example Visualization 2 <notebooks/example-viz-2.ipynb>
+    Example Visualization 2a <notebooks/example-viz-2a.ipynb>
+    Example Visualization 3 <notebooks/example-viz-3.ipynb>
\ No newline at end of file
diff --git a/docs/source/examples/notebooks b/docs/source/examples/notebooks
new file mode 120000
index 0000000..d4cb9b9
--- /dev/null
+++ b/docs/source/examples/notebooks
@@ -0,0 +1,1 @@
+../../../examples/
\ No newline at end of file
diff --git a/docs/source/requirements.txt b/docs/source/requirements.txt
index d748a40..1ea65ff 100644
--- a/docs/source/requirements.txt
+++ b/docs/source/requirements.txt
@@ -12,3 +12,6 @@ nbsphinx
 numpydoc
 pydata-sphinx-theme
 pystare
+pyerfa
+astropy
+matplotlib
\ No newline at end of file
diff --git a/examples/dominica.ipynb b/examples/dominica.ipynb
deleted file mode 100644
index 94c19c5..0000000
--- a/examples/dominica.ipynb
+++ /dev/null
@@ -1,463 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "figured-modification",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "example-dominica\n",
-      "compressed = pystare.to_compressed_range(sids)\n",
-      "0 0x22ff98a00000000b\n",
-      "1 0x22ff98e00000000b\n",
-      "2 0x22ff96a00000000b\n",
-      "3 0x22ff96c00000000b\n",
-      "4 0x22ff96e00000000b\n",
-      "5 0x22ffea400000000b\n",
-      "6 0x22ffeb000000000b\n",
-      "7 0x22ffeb200000000b\n",
-      "8 0x22ffeb400000000b\n",
-      "9 0x22ffef000000000b\n",
-      "10 0x22ff8d200000000b\n",
-      "11 0x22ffea200000000b\n",
-      "12 0x22ff99200000000b\n",
-      "13 0x22ff9ea00000000b\n",
-      "14 0x22ff91a00000000b\n",
-      "15 0x22ff98200000000b\n",
-      "16 0x22ffe8800000000b\n",
-      "17 0x22ffe8e00000000b\n",
-      "18 0x22ffe9c00000000b\n",
-      "19 0x22ffef200000000b\n",
-      "20 0x22ffef600000000b\n",
-      "21 0x22ff95400000000b\n",
-      "22 0x22ff95600000000b\n",
-      "23 0x22ff95800000000b\n",
-      "24 0x22ff8d600000000b\n",
-      "25 0x22ff99800000000b\n",
-      "26 0x22ff98400000000b\n",
-      "27 0x22ff94600000000b\n",
-      "28 0x22ff98c00000000b\n",
-      "29 0x22ff94c00000000b\n",
-      "30 0x22ff95c00000000b\n",
-      "31 0x22ffe8c00000000b\n",
-      "32 0x22ff8d400000000b\n",
-      "33 0x22ff97c00000000b\n",
-      "34 0x22ff8bc00000000b\n",
-      "35 0x22ff99400000000b\n",
-      "36 0x22ff90400000000b\n",
-      "37 0x22ff91400000000b\n",
-      "38 0x22ff8e400000000b\n",
-      "39 0x22ffebc00000000b\n",
-      "40 0x22ff97e00000000b\n",
-      "41 0x22ff98600000000b\n",
-      "42 0x22ff99600000000b\n",
-      "43 0x22ff94e00000000b\n",
-      "44 0x22ff8e600000000b\n",
-      "45 0x22ff8a600000000b\n",
-      "46 0x22ff8be00000000b\n",
-      "47 0x22ff95a00000000b\n",
-      "48 0x22ff99a00000000b\n",
-      "49 0x22ff94000000000b\n",
-      "50 0x22ff99c00000000b\n",
-      "51 0x22ff95e00000000b\n",
-      "52 0x22ff99e00000000b\n",
-      "53 0x22ff8a000000000b\n",
-      "54 0x22ffea000000000b\n",
-      "55 0x22ff96200000000b\n",
-      "56 0x22ff8e200000000b\n",
-      "57 0x22ffea600000000b\n",
-      "58 0x22ff8ac00000000b\n",
-      "59 0x22ff8ae00000000b\n",
-      "60 0x22ff8d000000000b\n",
-      "61 0x22ff8e000000000b\n",
-      "62 0x22ff8aa00000000b\n",
-      "63 0x22ff91000000000b\n",
-      "64 0x22ff91200000000b\n",
-      "65 0x22ff91600000000b\n",
-      "66 0x22ff94200000000b\n",
-      "67 0x22ff94a00000000b\n",
-      "68 0x22ff96800000000b\n",
-      "69 0x22ff8b800000000b\n",
-      "70 0x22ff97800000000b\n",
-      "71 0x22ff98000000000b\n",
-      "72 0x22ff9c800000000b\n",
-      "73 0x22ff9e800000000b\n",
-      "74 0x22ff98800000000b\n",
-      "75 0x22ff99000000000b\n",
-      "76 0x22ff9ee00000000b\n",
-      "77 0x22ffe8200000000b\n",
-      "78 0x22ff94400000000b\n",
-      "79 0x22ff95000000000b\n",
-      "80 0x22ff95200000000b\n",
-      "81 0x22ff94800000000b\n",
-      "82 0x22ffeb600000000b\n",
-      "83 0x22ffeb800000000b\n",
-      "84 0x22ffeba00000000b\n",
-      "85 0x22ffebe00000000b\n",
-      "86 0x22ff8a800000000b\n",
-      "87 0x22ff8a200000000b\n",
-      "\n",
-      "compressed = pystare.to_compressed_range(sids)\n",
-      "0 0x22ff8a000000000b\n",
-      "1 0x22ff8a3fffffffff\n",
-      "2 0x22ff8a600000000b\n",
-      "3 0x22ff8affffffffff\n",
-      "4 0x22ff8b800000000b\n",
-      "5 0x22ff8bc00000000b\n",
-      "6 0x22ff8bffffffffff\n",
-      "7 0x22ff8d000000000a\n",
-      "8 0x22ff8e000000000a\n",
-      "9 0x22ff90400000000b\n",
-      "10 0x22ff91000000000a\n",
-      "11 0x22ff91a00000000b\n",
-      "12 0x22ff940000000009\n",
-      "13 0x22ff96200000000b\n",
-      "14 0x22ff96800000000a\n",
-      "15 0x22ff97800000000b\n",
-      "16 0x22ff97c00000000b\n",
-      "17 0x22ff97ffffffffff\n",
-      "18 0x22ff980000000009\n",
-      "19 0x22ff9c800000000b\n",
-      "20 0x22ff9e800000000b\n",
-      "21 0x22ff9ebfffffffff\n",
-      "22 0x22ff9ee00000000b\n",
-      "23 0x22ffe8200000000b\n",
-      "24 0x22ffe8800000000b\n",
-      "25 0x22ffe8c00000000b\n",
-      "26 0x22ffe8ffffffffff\n",
-      "27 0x22ffe9c00000000b\n",
-      "28 0x22ffea000000000a\n",
-      "29 0x22ffeb000000000a\n",
-      "30 0x22ffebffffffffff\n",
-      "31 0x22ffef000000000b\n",
-      "32 0x22ffef3fffffffff\n",
-      "33 0x22ffef600000000b\n",
-      "\n"
-     ]
-    },
-    {
-     "ename": "AttributeError",
-     "evalue": "module 'pystare' has no attribute 'exceptions'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-48d050e4f556>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'__main__'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m     \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-1-48d050e4f556>\u001b[0m in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0mtransf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mccrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlateCarree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m     \u001b[0mdominica1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtransf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-1-48d050e4f556>\u001b[0m in \u001b[0;36mdominica1\u001b[0;34m(proj, transf)\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m     \u001b[0mexpanded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpystare\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_intervals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompressed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'expanded = pystare.expand_intervals(compressed,-1,False)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m     \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Dropbox/UCSB/STARE_Project/pystare/pystare/spatial.py\u001b[0m in \u001b[0;36mexpand_intervals\u001b[0;34m(intervals, level, multi_resolution)\u001b[0m\n\u001b[1;32m    303\u001b[0m     \"\"\"\n\u001b[1;32m    304\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m27\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 305\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mpystare\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPyStareLevelError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    306\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpystare\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expand_intervals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintervals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_resolution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m     \u001b[0mexpanded_intervals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size_as_intervals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: module 'pystare' has no attribute 'exceptions'"
-     ]
-    }
-   ],
-   "source": [
-    "import pystare\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.tri as tri\n",
-    "import cartopy.crs as ccrs\n",
-    "import numpy\n",
-    "\n",
-    "def plot1(lon=None,lat=None,lons=None,lats=None,triang=None,c0='r',c1='b',transf=None,lw=1,ax=None):\n",
-    "    if(lon is not None and ax is not None):\n",
-    "        x=np.zeros([lon.size+1],dtype=np.double);x[:-1]=lon[:];x[-1]=lon[0]\n",
-    "        y=np.zeros([lat.size+1],dtype=np.double); y[:-1]=lat[:]; y[-1]=lat[0]\n",
-    "        ax.plot(x,y,True,transform=transf,c=c0)\n",
-    "    ax.triplot(triang,c1+'-',transform=transf,lw=lw,markersize=3)\n",
-    "    ax.scatter(lons,lats,s=10,c=c1,transform=ccrs.PlateCarree())\n",
-    "    return\n",
-    "\n",
-    "def plot2(sids,c0='r',c1='g',transf=None,ax=None):\n",
-    "    lons,lats,intmat = pystare.triangulate_indices(sids)\n",
-    "    triang = tri.Triangulation(lons,lats,intmat)\n",
-    "    plot1(None,None,lons=lons,lats=lats,triang=triang,c0=c0,c1=c1,transf=transf,lw=1,ax=ax)\n",
-    "    return\n",
-    "\n",
-    "def dominica1(proj,transf):\n",
-    "    \"Example and analysis for pystare bug#27.\"\n",
-    "\n",
-    "    sids = numpy.array([2521898143583305738, 2521997099629805578, 2521989952804225034,\n",
-    "       2521991052315852810, 2521893745536794634, 2521897044071677962,\n",
-    "       2521897593827491850, 2521898693339119626, 2521899792850747402,\n",
-    "       2521991602071666698, 2521889347490283530, 2521899243094933514,\n",
-    "       2521900892362375178, 2521901442118189066, 2521901991874002954,\n",
-    "       2521903091385630730, 2521887698222841866, 2521886048955400202,\n",
-    "       2521992701583294474, 2521902541629816842, 2521906389920514058,\n",
-    "       2521890447001911306, 2521993251339108362, 2521886598711214090,\n",
-    "       2521908588943769610, 2521892646025166858, 2521894295292608522,\n",
-    "       2521989403048411146])\n",
-    "\n",
-    "    sids = numpy.array([2521902129312956427, 2521902404190863371, 2521899930289700875,\n",
-    "       2521900067728654347, 2521900205167607819, 2521991876949573643,\n",
-    "       2521992701583294475, 2521992839022247947, 2521992976461201419,\n",
-    "       2521997099629805579, 2521889484929237003, 2521991739510620171,\n",
-    "       2521902679068770315, 2521908726382723083, 2521894432731561995,\n",
-    "       2521901579557142539, 2521989952804225035, 2521990365121085451,\n",
-    "       2521991327193759755, 2521997237068759051, 2521997511946665995,\n",
-    "       2521898418461212683, 2521898555900166155, 2521898693339119627,\n",
-    "       2521889759807143947, 2521903091385630731, 2521901716996096011,\n",
-    "       2521897456388538379, 2521902266751909899, 2521897868705398795,\n",
-    "       2521898968217026571, 2521990227682131979, 2521889622368190475,\n",
-    "       2521901167240282123, 2521887973100748811, 2521902816507723787,\n",
-    "       2521892920903073803, 2521894020414701579, 2521890721879818251,\n",
-    "       2521993526217015307, 2521901304679235595, 2521901854435049483,\n",
-    "       2521902953946677259, 2521898006144352267, 2521890859318771723,\n",
-    "       2521886461272260619, 2521888110539702283, 2521898830778073099,\n",
-    "       2521903228824584203, 2521897044071677963, 2521903366263537675,\n",
-    "       2521899105655980043, 2521903503702491147, 2521886048955400203,\n",
-    "       2521991602071666699, 2521899380533886987, 2521890584440864779,\n",
-    "       2521992014388527115, 2521886873589121035, 2521887011028074507,\n",
-    "       2521889347490283531, 2521890447001911307, 2521886736150167563,\n",
-    "       2521893745536794635, 2521893882975748107, 2521894157853655051,\n",
-    "       2521897181510631435, 2521897731266445323, 2521899792850747403,\n",
-    "       2521887698222841867, 2521900892362375179, 2521901442118189067,\n",
-    "       2521906389920514059, 2521908588943769611, 2521901991874002955,\n",
-    "       2521902541629816843, 2521909001260630027, 2521989540487364619,\n",
-    "       2521897318949584907, 2521898143583305739, 2521898281022259211,\n",
-    "       2521897593827491851, 2521993113900154891, 2521993251339108363,\n",
-    "       2521993388778061835, 2521993663655968779, 2521886598711214091,\n",
-    "       2521886186394353675])\n",
-    "\n",
-    "    expanded_stare_test = numpy.array([\n",
-    "        0x22ff8a000000000b,\n",
-    "        0x22ff8a200000000b,\n",
-    "        0x22ff8a600000000b,\n",
-    "        0x22ff8a800000000b,\n",
-    "        0x22ff8aa00000000b,\n",
-    "        0x22ff8ac00000000b,\n",
-    "        0x22ff8ae00000000b,\n",
-    "        0x22ff8b800000000b,\n",
-    "        0x22ff8bc00000000b,\n",
-    "        0x22ff8be00000000b,\n",
-    "        0x22ff8d000000000a,\n",
-    "        0x22ff8e000000000a,\n",
-    "        0x22ff90400000000b,\n",
-    "        0x22ff91000000000a,\n",
-    "        0x22ff91a00000000b,\n",
-    "        0x22ff940000000009,\n",
-    "        0x22ff96200000000b,\n",
-    "        0x22ff96800000000a,\n",
-    "        0x22ff97800000000b,\n",
-    "        0x22ff97c00000000b,\n",
-    "        0x22ff97e00000000b,\n",
-    "        0x22ff980000000009,\n",
-    "        0x22ff9c800000000b,\n",
-    "        0x22ff9e800000000b,\n",
-    "        0x22ff9ea00000000b,\n",
-    "        0x22ff9ee00000000b,\n",
-    "        0x22ffe8200000000b,\n",
-    "        0x22ffe8800000000b,\n",
-    "        0x22ffe8c00000000b,\n",
-    "        0x22ffe8e00000000b,\n",
-    "        0x22ffe9c00000000b,\n",
-    "        0x22ffea000000000a,\n",
-    "        0x22ffeb000000000a,\n",
-    "        0x22ffeb800000000a,\n",
-    "        0x22ffef000000000b,\n",
-    "        0x22ffef200000000b,\n",
-    "        0x22ffef600000000b\n",
-    "        ])\n",
-    "    \n",
-    "    expanded_stare_test = numpy.array([\n",
-    "        0x22ff8a000000000b,\n",
-    "        0x22ff8a200000000b,\n",
-    "        0x22ff8a600000000b,\n",
-    "        0x22ff8a800000000a,\n",
-    "        0x22ff8b000000000a,\n",
-    "        0x22ff8b800000000a,\n",
-    "        0x22ff8b800000000b,\n",
-    "        0x22ff8bc00000000b,\n",
-    "        0x22ff8be00000000b,\n",
-    "        0x22ff8c0000000009,\n",
-    "        0x22ff8d000000000a,\n",
-    "        0x22ff8e0000000009,\n",
-    "        0x22ff8e000000000a,\n",
-    "        0x22ff900000000008,\n",
-    "        0x22ff90400000000b,\n",
-    "        0x22ff91000000000a,\n",
-    "        0x22ff91a00000000b,\n",
-    "        0x22ff940000000009,\n",
-    "        0x22ff96200000000b,\n",
-    "        0x22ff96800000000a,\n",
-    "        0x22ff97800000000b,\n",
-    "        0x22ff97c00000000b,\n",
-    "        0x22ff97e00000000b,\n",
-    "        0x22ff980000000008,\n",
-    "        0x22ff980000000009,\n",
-    "        0x22ff9c800000000b,\n",
-    "        0x22ff9e800000000b,\n",
-    "        0x22ff9ea00000000b,\n",
-    "        0x22ff9ee00000000b,\n",
-    "        0x22ffa00000000007,\n",
-    "        0x22ffc00000000007,\n",
-    "        0x22ffe00000000007,\n",
-    "        0x22ffe8200000000b,\n",
-    "        0x22ffe8800000000b,\n",
-    "        0x22ffe8c00000000b,\n",
-    "        0x22ffe8e00000000b,\n",
-    "        0x22ffe9c00000000b,\n",
-    "        0x22ffea000000000a,\n",
-    "        0x22ffeb000000000a,\n",
-    "        0x22ffeb800000000a,\n",
-    "        0x22ffef000000000b,\n",
-    "        0x22ffef200000000b,\n",
-    "        0x22ffef600000000b\n",
-    "        ])\n",
-    "\n",
-    "    print('compressed = pystare.to_compressed_range(sids)')\n",
-    "    k = 0\n",
-    "    for i in sids:\n",
-    "        print(k,hex(i))\n",
-    "        k += 1\n",
-    "    print('')\n",
-    "    \n",
-    "    compressed = pystare.to_compressed_range(sids)\n",
-    "    # compressed = compressed[0:6] # Broken\n",
-    "    # compressed = compressed[0:5] # Broken\n",
-    "    # compressed = compressed[0:4] # Broken # fixed\n",
-    "    # compressed = compressed[0:3] # Okay\n",
-    "    print('compressed = pystare.to_compressed_range(sids)')\n",
-    "    k = 0\n",
-    "    for i in compressed:\n",
-    "        print(k,hex(i))\n",
-    "        k += 1\n",
-    "    print('')\n",
-    "    \n",
-    "    expanded = pystare.expand_intervals(compressed,-1,False)    \n",
-    "    print('expanded = pystare.expand_intervals(compressed,-1,False)')\n",
-    "    k = 0\n",
-    "    for i in expanded:\n",
-    "        print(k,hex(i))\n",
-    "        k += 1\n",
-    "    print('')\n",
-    "\n",
-    "    expanded_mr = pystare.expand_intervals(compressed,-1,True)    \n",
-    "    print('expanded = pystare.expand_intervals(compressed,-1,True)')\n",
-    "    k = 0\n",
-    "    for i in expanded_mr:\n",
-    "        print(k,hex(i))\n",
-    "        k += 1\n",
-    "    print('')\n",
-    "\n",
-    "    ncols = 4 # 5\n",
-    "    fig, axs = plt.subplots(nrows=1,ncols=ncols,subplot_kw={'projection':proj,'transform':transf})\n",
-    "    # plt.figure()\n",
-    "    # plt.subplot(projection=proj,transform=transf)\n",
-    "\n",
-    "    iax=0\n",
-    "    \n",
-    "    ax=axs[iax]\n",
-    "    # ax.set_global()\n",
-    "    ax.coastlines()\n",
-    "    ax.title.set_text('1. Original\\n(res=0xA)')\n",
-    "    ax.annotate(\n",
-    "        '1. sids\\n2. compressed = pystare.to_compressed_range(sids) # Intervals. Terminators expressed as dots.\\n3. expanded = pystare.expand_intervals(compressed,-1,False) # Intervals expanded according to embedded resolution.\\n4. expanded_multires = pystare.expand_intervals(compressed,-1,True) # Intervals expanded into coarsest common ancestors.',\n",
-    "        xy=(50, 50), xycoords='figure pixels')\n",
-    "    plot2(sids,c0='r',c1='b',transf=transf,ax=ax)\n",
-    "    iax += 1\n",
-    "\n",
-    "    ax=axs[iax]\n",
-    "    # ax.set_global()\n",
-    "    ax.coastlines()\n",
-    "    ax.title.set_text('2. Compressed\\n(Incorrect usage)')\n",
-    "    plot2(compressed,c0='r',c1='b',transf=transf,ax=ax)\n",
-    "    iax += 1\n",
-    "\n",
-    "    ax=axs[iax]\n",
-    "    # ax.set_global()\n",
-    "    ax.coastlines()\n",
-    "    ax.title.set_text('3. Expand Compressed\\n(mono-res)')    \n",
-    "    plot2(expanded,c0='r',c1='b',transf=transf,ax=ax)\n",
-    "    iax += 1\n",
-    "    \n",
-    "    ax=axs[iax]\n",
-    "    # ax.set_global()\n",
-    "    ax.coastlines()\n",
-    "    ax.title.set_text('4. Expand Compressed\\n(multi-res)')    \n",
-    "    plot2(expanded_mr,c0='r',c1='b',transf=transf,ax=ax)\n",
-    "    iax += 1\n",
-    "\n",
-    "#    ax=axs[iax]\n",
-    "#    # ax.set_global()\n",
-    "#    ax.coastlines()\n",
-    "#    ax.title.set_text('5. Expand Compressed\\n(multi-res)\\nfrom STARE lib tests')    \n",
-    "#    plot2(expanded_stare_test,c0='r',c1='b',transf=transf,ax=ax)\n",
-    "#    iax += 1    \n",
-    "     \n",
-    "    plt.show()\n",
-    "    \n",
-    "    \n",
-    "\n",
-    "#    compressed = pystare.to_compressed_range(flat_list) \n",
-    "#    compressed.size\n",
-    "#    22\n",
-    "#    we get\n",
-    "#\n",
-    "#    array([2521886048955400202, 2521887148467027967, 2521887698222841866,\n",
-    "#        2521889347490283530, 2521890447001911306, 2521892646025166858,\n",
-    "#    2521893745536794634, 2521894845048422399, 2521897044071677961,\n",
-    "#    2521899243094933514, 2521900342606561279, 2521900892362375178,\n",
-    "#    2521903641141444607, 2521906389920514058, 2521908588943769610,\n",
-    "#    2521989403048411146, 2521990502560038911, 2521991052315852810,\n",
-    "#    2521992151827480575, 2521992701583294474, 2521993801094922239,\n",
-    "#               2521997099629805578])    \n",
-    "    \n",
-    "    return\n",
-    "\n",
-    "def main():\n",
-    "    print('example-dominica')\n",
-    "\n",
-    "    proj   = ccrs.PlateCarree()\n",
-    "    # transf = ccrs.Geodetic()\n",
-    "    transf = ccrs.PlateCarree()\n",
-    "\n",
-    "    dominica1(proj,transf)\n",
-    "\n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "seasonal-remedy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "competitive-headline",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/example-viz-1.ipynb b/examples/example-viz-1.ipynb
index 3b1d973..710c58f 100644
--- a/examples/example-viz-1.ipynb
+++ b/examples/example-viz-1.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3d69fb49-40d3-436f-938a-06fff713d5cb",
+   "metadata": {},
+   "source": [
+    "## Example 1"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -20,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "respected-belgium",
    "metadata": {},
    "outputs": [],
@@ -74,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "allied-queen",
    "metadata": {},
    "outputs": [
@@ -108,19 +116,11 @@
       "hull im len:  80\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-4-4228bf3f8f63>:44: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
-      "  ax = plt.axes(projection=proj,transform=transf)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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z+/bbb7XvbW1tuXr16nx/BH2XiIgIHjt2jEeOHOFXX33Fx48fk1SL3enTp3n16lX26tWL69evJ0n6+/uzQoUKBEB7e3s6OztzwYIFvHPnDkUiEa2srNipUydu2bKFAwcOZGBgIBs2bEgAjIyMJKn+ojZq1IjdunXLcBGGSqXirVu3uGLFClarVo2NGjWin5+fdv+DBw84ZMgQzp8/n9OnT+f3339PW1tbvnz5MsfXHxYWRicnJz58+FC7beJEErhH4AwBQSu8mt5ro0aN0rlpALBp06Y5OrfGl/3vv//m2O6iilKp5N9//82BAwcKCoVC5eLiEiwWi8cbBDgP2qFvA4pbAdDcwcHhqrW1taDpCfbp04eurq4EQHNzc+2XtkWLFgTU8y7TikBRIDAwkB06dNDaunz5cu7cuZN9+vShi4sL7ezsWKtWLd66dYtRUVFaN4SxsTF9fHx0Bo2eP39OS0vLDOcNL1iwgHZ2dvT09GTfvn155MiRdMKssaFSpUocNmwYx40bR3d3d/bt2zfH1/X555/zm2++0dlWuvQZAk5vzjOMcjl58SL57Fmoju8cAC0tLfnTTz/x2bNnOe6x3r17l9OnT8/R6r/ihFKp5JkzZzho0CBBLperZDLZEwD98SacgKFkU0P0bUBxKABKWVlZ7ZLL5cl169ZVTZ8+Xfs4aW9vz9mzZ/PRo0c6gy8AOGzYMBYlHj58yBEjRug8RmvKd999x44dO2rfOzo6Mj4+nvv376dKpaKDgwPXrl3Lffv2peutjx8/ngMHDtTZJggCV69ezTJlyvDJkydZ2qXxKS9ZsoQbNmxg+/bt6ejomKOFFSqVilOnTmW5cuXSLYyoWnUkATcCjgR2slo1slo10sKCrF5dSam0tva6PTw8OH68UscVYSA9MTExBEA7Ozva2dmpHB0dL8GwKCN7eqJvA4pqAWAkFovHubi4hJUuXVqYM2eOdkXRf//9p/2SfvHFFyxVqhQB0NPTkx06dODKlSuZkJDAokBKSgq3bt3Kjh070snJiVOnTmXPnj219ltZWREAf/zxR54/f54nT57kjh07CID79u3TClGjRo0ydY307t2bmzZt0r4PCgpi/fr16e3tna0e/osXLzhu3Dh+9tln7NGjB7du3ZqjGQxnz55l5cqVWb9+/XQuiZkzZ9LZ2ZNGRqVob7+BADlihHpffDx5/jw5f76SzZqdpovLDZqZqb8VmmIQ3reEhYVx+vTpDAsL024LCAjgggUL6O3tTVtbW9rb2yeZmpouA2DLIvA9LopF7wYUtQKgooODwxmZTKbq27evcPnyZZ3HzOTkZE6aNIlyuZwVK1bkhAkTePPmTW0wF0EQeP78ed65c4f6JCUlhevWrWPJkiXZrFkzbt26lUFBQSTf9lIsLCwIgDY2Nvz000/p5eVFADx79ixnz55NJycnGhkZcevWrVn+iAwZMoTLly8nqb7+YcOGsV+/foXymC0IAh0cHPjHH39k6A6oWrUqe/W6zKpVP+OUKVO4eLH6U//zz28qqFTkq1ekIDAggDqCC6gH3z52YmNj2atXL+3nQzMA++4ThWZg0cbGhgqFQpDJZA8BtGYR+F4XpaJ3A4pCASAyMjL60tnZOdzT01NYtWoVY2NjqSE6Oppjxozh8OHDCYBdunThvXv3tPsFQeDVq1fp5uaW7rH9wIEDDA0NZWFy7tw5VqhQgc2bN+fFixczrXfz5k06ODjwwIED2m22trYEQKVSyT179vDmzZvvPd///vc/rQ/7u+++Y5UqVRgREZEv15Id3N3d+d9//2W4r3LlfhSJ2rNZs310dXXl4cOH2b79FgJD2aL5CD4HqBIZsYXd1XSCa+jpqtH49J2cnLTCa2Jiwh49elAQBD579owLFy7UfuavXr3K1NRU7t69m3Xq1BEUCkWCiYnJAgAWLALfd30XvRug14sHrEuUKPGrTCZL7dKli3Dp0qUMe0u7du3SEdJdu3bx1atXTElJ0a7USlt8fHy4a9cu/vjjj2zQoAEBFJrw/vTTTyxRogR37tyZ6UCQIAj8/fff6ebmxjlz5mi3BwUF0cjIiH/++afWv7tq1ar3nlMQBMbHx/OLL75gt27dcjXrIC988skn/PPPPykIAqdOncrdu3eT1MxaiCNQmsARAtvS/a88MEorsMt/jtYeZ2FB5mIc74PgypUr/N///sehQ4dqpyCWL19ee8969erFly9fsk6dOuzWrZt2u2Y6ore3t057jx8/5qhRoyiXy1X29vanALizCHz/9VX0boBeLhrwkkgkl+VyuWrChAnZEgmN3zZtcXBwYMuWLTlnzhz26tWL165dS/dIHRISQkC9zv7q1avvPU9eEASBANihQ4cs6z148IB2dnY8c+aMzvaLFy8SUC++mDBhAqtVq6Yz11ifPHz4UDsz5N3e94oVK+jl5cX27duzatWqNDIy4qtXr1ipEgnMJ1CZQBxtbUkTE800vu0EzhIQCJDu8Oe+rXHU/E6VLk2WKEF261b416pvRowYQW9vb65atYo///wzp06dyn379rFevXoEwBFvnOIRERGUSCSsXbs269atS19fX164cCFTV1RsbCyXLl3KUqVKCXK5PABASxYBPSjsoncDCvVigYZyudzP3d1dWLduXboVVe+iWaY7cuTIdIKrGXx6H4IgaFdcFbToaoJ+d+nSJcswguvWrWPr1q0z3Hfs2DGGh4czNTWVcrmcT58+LShzc8Tnn3+uXR3n5uams08QBO7du5eff/659n8zYsSINz3dGQQmECD79buQ5v+3MUN3QkblYxPeFy9e0NHR8b2zTkhy+vTpBMALFy5w6tSp9PHxoUQi4bx58zL9fqlUKh48eJDVqlUTFApFBIDPWQT0obCK3g0olIsEOioUihBvb29hz549WS5QCAoK4s8//5xuWpWTkxOXLl3KKlWqcN68edmO83rt2jUqFIpcBUPJKUFBQdy8eTMHDBjABg0apOtxJCQkaAfI0i6NzYjp06fT2Ni4yMw5bdu2Lfv378+qVavSx8cnwzqdO3emQqGgZr70999/TxsbNzo5LaGNDbl+/QZ26uRLYCGBAP6LahwA9YyGksZBFCDi0RoTaWyk0hFdB4dCvtgiwMSJEzlq1Khs1d25cycdHR05c+ZMpqSk8O7du9pAPS9evMjy2IsXL7JZs2aCXC5PEIvF37AI6EVBF70bUKAXB3SSy+XhNWrUEE6fPp2pj3Pz5s0sXbp0up5slSpVuGvXrlyHGExOTqZCoeCsWbNydXxuiYyMZPv27bXXceHCBY4ePZoKhYK+vr709/d/bxua3s4XX3yRL8HSc8vz58/ZoUMH7eKTn376iTY2NjoDmRrGjRtHzQIHQB0KccmSJdyx408aG8+lSDSUgB0B0n/sMhKgAsG0s1N/E46POUQCvKjo8lH3dEn1D7idnR23bduWbol2Rt+Hp0+fsm3btvTy8uKuXbsYHx9PLy8v2tracu3ate89371799ixY0dBLpcnisXiMR/yggu9G1AgFwW0lcvl4TVr1hTe18OMj4/XilO7du34zz//ZJmP633ExsZqc4n17t2bLVu21ItoqVQqVq1alZ9++im7d+/Oli1bZihUWREYGKgVbX3w6NEjymQyzpgxg7a2ttroZjNnzqRCodB++QVB4JUrV9ilSxftyLpm4r6rqystLX0I9CYgIfAPa9UiWb06Wa+e9lweHupvQ/ycZZz5xh1hb6uimRl57ZpeLl/vrFmzhl27dtWJx6wZYO3Vq1e6mM+CIPDw4cOsWrUq69Spw9OnT3P06NEEsh/I6MGDB+zUqZMgl8sTjYyMRrEI6El+F70bkK8XA9SUy+XPK1euLJw+fTrTf2xBUrFiRQJgvXr1aGpqmulUpoJm586drFGjBq9evUqJRKIzoT0neHp6cvHixflsXfY4efIka9SowefPn2u/9DVq1GBSUhLr16/Prl278vXr1+zfvz9Lly7N1q1bc+XKlXz58iUDAwN59epVHj16lCYmQoa+2rJOEZw7lwwNVU/XTbuvPB6Qw4dz00aB1auTeoinXmQIDQ3lokWLuHPnTk6bNo0lSpTQ/j/++uuvdPVVKhV///13li5dmu3ataO3tzcBaOMSZ4f79++zVatWglwujwXQnUVAX/Kr6N2AfLkIwEUqld4uU6aMsHfvXr1FedLMHli2bBnNzMw4ZMgQvdhBkgMGDGD37t0pk8l04q9mB0EQeO/ePbZt25Z2dnYMDw8vICuzZtiwYZTL5VQoFNqpScePHyepfhzVzIO2t7fPMidayZK6gtrd4QRr41KWg2cTMZ0EKIz7js2aCVy0qJAuuhiQmpqqM2jZs2dPvn79Ol295ORkLl68WFvP09Mzx9/NK1eusE6dOoJcLg8FUJtFQG/yWvRuQJ6MB8zs7Ox2KhQK1erVq4vEoM+KFStYuXJltm7dusAjimXF1q1bWbNmTZ44cSLTOnFxcbx9+zZv3rzJ8PBwxsfHc8+ePfTw8NA+on/22WeFaLUu165d48KFC3n79m2uX79e++X99ttv6evrS0Ad+yKrucR79pCurmTNmqSpqfoTLzMJJ6tW1daJiyN/+SVj8V2OkXzw1Qo6OpI1apBmZmT9+gV/7cWBRYsW6QR4WrFiRYb+3sjISI4fP54Acuzi0nDkyBGWKVNGkEql11HMM13o3YDcFhMTk6+lUmnq2LFj8y3TQF65c+eO9gNobGxcZOOqJiYm8rfffqNIJKKzs7N2tN/MzIw+Pj4sV66c9jqyWtFWmGjsqVu3LmvVqsVmzZrR3t4+wx6Whlu3SCcnMm2SiH92hxMg13bUDaZz5EjGoptZMQivmmPHjunkeTMzM+P27dsz/OznZayEVPewly1bRrlcrrKyslpfXAfb9G5Ajg0GKsvl8pCmTZsKaWOyFgU07gVNyW66msJky5Yt6WZpaMqVK1fo7u7OL7/8khs2bNBuLwoEBweza9eu2mWokydPznKVX8eOpFhM1q79zo7Vq9kZuwmQbzIQccMG9TehVi3SxeWtsJ45QwopqdxSa3E60TU1LbhrLW74+fnRw8NDm1YIABcVoD8mIiKCgwYNEmQyWSKAziwCupSToncDsm0oYOTg4LC3ZMmSQk5C/hUmN27c0BnlLUo93aioKG2v1s3NjYcOHaKDgwPnzZtHQRB0fjAGDx7MYcOG0cPDo8iIbkBAAMViMRs3bsw9e/ZkWbdrV12BtLcnv/uOPHiQjGrWmSxbNp2IvutFEYvffDtIMjmZzqLbBEK09Y2NyW+/Jf3902el+BgJCQmhXC5n586dCSBd5o6C4Pr166xSpYoglUrvAXBkEdCp7BS9G5AtI4GOMpksafTo0TkaAS1Mnj9/zlKlSmmXC+tzbuu7pM0OvHfvXpLUDnCMHTuW7du3Z+3atbl+/XrOmjVLp/c7ZcoU/Rqfhlu3brF169bvjVNsYcF0ovq+4uysPjY8PJwjRozgxo1/Ekhix46/0sdHnRGilMiLAOlt84x+fmrRfXuuWwTa0cbGjc2bN9cm8vyYmDNnDlu2bMnffvtN+/mpVq1agXY+VCoVly1bRplMpjQzM/uBRUCv3lf0bkCWxgFmTk5O57y8vISinALl5cuXLF++vE7wmKLG3bt3mZKSwuDgYJ4/f579+/fXEdd6aeashoSE8P79+9yyZYteBwMzIjo6mlKpNNM4vQ8evB0wA64QqM+GDY+qd27ezBQY8/KGO5mKr6Pjz7S0tEtzb8wIqKcBHtyxg8MlOwmQKUfUA5QVK5LATQKuBH6ih8cF2tnZceTIkYV1S4oMKSkprFatGo8cOaIzvnH58uUCP/fz58/ZvHlzQSaTBQFwYRHQr8yK3g3I1DCgrVQqTZ40aZKQVwd8QfLff//Rw8ODM2bM0LcpWfL69esMI6IB6kDshR0ZLC+4u7tnGBcgNZWsUSOa339/hZ9+KtDYuLf2Gg8ePEh26UK6uPDOLVU6sZVKyebN77+pf5bAqnT3ycLic0Y88SNAthcfIc+dY48eJNCf6sUXAo2MzAiA06ZN08Od0T/Tp0/n2LFjSaoHbDX37scff+TDhw/zLdtzRgiCwF9//ZUymUxlZmY2jkVAxzIqejcgnUGAyMHBYW/ZsmWLdO+WJC9dukSFQpGtZY764u7du2zXrh1tbGy0QaY1K4pOnjyZ5xHlguTRo0ccNGgQv//+e06dOpUbN27kunXraGlpqY13LAgCb9y4wePHj3Po0L9ZokQ52trasl27dly9ejXFYgUBtZ/xXzMzxn4xViu0aQfNLl1SZzpWKBQUBPLYMVIdiWwSgRgCfxBwoqmpGXv57iVAJlhLCZAmJnsoFkspkTzVLkHO7dLx4syzZ8946tQp1q1bV7vtxYsX6X68vvvuuwJ9gnr+/DkbNGigCaJuzSKga2mL3g3QMQYoK5VKIwcNGlRk0t1kxp49eyiRSLhv3z59m5IpDx48oJOTE1esWMHo6GgmJiZy9uzZhfK4l1c0fujvvvuOM2fO5IQJE9i3b1/279+fGzdu1NabPXs2bWzcaG7emCJRKc6cuYply5alnZ0dT5w4wfnzFxGoTUfrn+kMEBhE4LnOCjON8I4bN44//PADSVIQSCOjt/vURUVgDNXxGwZRExaygdMDjhw5itbWfXnqVAo7duxId3d3/vXXX0VqMLUgefXqFQFQKpWycePGOvucnNRJQYcOHcqaNWsSQK5XSGYXQRA4f/58SqXSFABtWQT0TVP0boCmmJqajpDL5SpNAOqijL+/P2UyWZGZw/ousbGxvHr1KqVSKYvK7IPc4ObmluUy6tTUVJYt+wmBlWl8smcIgHXqjKKNjRNnzvyFzZr1f9PLGkHge0okMh13ysuX6mN79VrHmjVr8t69+yxfPo7AN296yrYEuvPkyUtcu1bg28DohwmQwVCwtuQGy5ZtTFfX3kxOTubSpUsJgNWrVy/yHYj8YOPGjQTA8+fPp1vBePfuXcpkMm1Pt1SpUoVm1/Xr1+nh4SHY2tr+wiKgcywKogtA5OjoeLRy5cpCQU8xyQ/8/PxYpkwZLlu2TN+mpOPdecKtWrXi999/r2+zco2Pj0+mwXYuX75MExMTWlo2JxCRpje69c31f0/1jILabwRyyJvt3gTkHDLkEp8+Jffv38+aNWvS1rYRgSZp7p+YjRp155MnT/jqVQiBRTQ3L0OpVPommacbgUfa807GFIYFhtPEpAMbNmzNR48eadtq3rx5Id+5giMiIoKffPIJf//9d53tYWFhNDMzY2adpoCAAG0WlU6dOjEyMrIQrFUTGxvLTz75RJBKpf5Fwd2gb8F1lEqlYYMGDSpSU6yyolOnTpw6daq+zUjH06dPuWqVevBnwIABfPTokb5NyhPR0dG0tLTM8DFUEARWrlyZGzdufBOo/G1p1UqdWqlmzZra+uUxhYD1O77FVgTi07zv8+avBYGFrFv33TTzJCDwwYMAvn4dSSBJe84SiNO+FovVg0empgPp47OMAHjs2LECv1+FxezZs7XZOX777Td27NiRc+fOZUBAAAGky0aSlhcvXtDFxYUAWLZs2WzHpM4PBEHgwoULKZFIkgDU4McougBqSiSS5HXr1uXLTS0MLl++TFdX10JNupgd7OzeTnEq6oOP2WX58uX85JNPMtwXExNDY2Nj7fJvzeKEli3JatXupBNdtSBqZiM0oETyhN7e3ly2bCVdXb1obq5401uWEDhNIH3g8pQUdTsDB5LDhqlfOzur/9rakrHte7CW+Nqbc3kQuERASYmkOZs0afJB+Hbv31fP7liyZAmbNm1KQB1voW3btuzQoQOrVq3KevXqZZnMNDg4mKVKlWL9+vXp6+tbeMa/4cyZM1QoFCojI6MB/JhE18jIqL9CoVAVlfxb2WXhwoUcNGiQvs3QQZMuBQDv37+vb3PyjRMnThBAupitJLlp0yZWqFAh3T6lUh0TYfz4W1o/asmSrQk4penRnqGtrXqqn52dHTdv3kz1Kr19BFoT+B+BjAOXq6eHqYsmY1CpUm++RXfvkmIxG4gvEKhFdXaKJNrbq1i6dGneuHEjX++PPoiLi2OzZs1YqVIlLly4kCNHjuTOnTu1YiyXy9m9e3dKJBLOnz8/0xkKS5cuZbt27Whra6uX+xIQEMBy5coJdnZ2q/gxiK61tfUsDw8PISAgIL/uYaHh6+vL1atX69sMHbp3705Af4HGC4qEhAQC6gA306dPZ0BAADdv3sxZs2ZRLpfz6NGjvHbtGteuXcvJkydz7NixbNq0Kb29a9DYuF4akf1C+3rMmDE8elT9qb9xg9rcdepyiyVLBlAkktHHZymfPn3KXbt28dy5cyTJgwcPsmbNHQRea4W3WzcyMFD9+vJlMrHfF2oXA64SqEOgPCtVmkVnZ+cPZrGEUqnkX3/9RQ8PDzo7O2sHyE6cOMEWLVoQUCdGrVOnDvv06ZMu8t/EiRMJgBMnTuSff/5JZ2dnRkVFFfp1xMTEsGHDhoKTk9NJFHLgnEIVXEdHxz9r1KghZBUZqqiiUqloaWmpt9iymdG5c+civRIut2gmum/atEkrjN27d+dnn33GqVOnskOHDixdujQHDhzIyZMn83//+x+3bt3KI0eOsF+/fTQ1HffmOIHPTE35w5gx2sdejWg+f/6cbm6eBCqybt1AKpVKHjt2jN27d6eFhQXFYjFLlSrFihUraqc9ATO1x4tEb0UXICuVTyFAnnb7nGIxWbXqAY4bN45Tpkz5oJ5CSPX3ISYmhu3bt2erVq20SSgXLlxIALx+/TqbNm3KCRMmaI/x9/fnhAkTCEAbl2Hw4MHs2rWrXtwvKSkp7NWrlyCVSh8DMOKHJrpOTk4nW7RoIbwvA29RJSYmhkZGRvo2Q0tsbCxXr15NiUTCoKAgfZtTYMTExGhF18jIiO7u7qxYsSLnz5+foeuBVGfhAaLVA1qIYVvTE9yyhdQkSH727K1Qqhc3DNaeY+HChSTV805jYmJ45swZ7b4yZUYRGKpz7Lvlc7M/SJGIM0a94HtCRBR7BEGgqalpuizXK1asYPXq1Xn06FECYGxsLP/++29aW1vzk08+YdmyZRkZGcmXL1/yzJkztLS05OPHj/V2Dd9++y2lUulLAGb8EEQXgEgqlV7z9fUt0st538f169epWcmlT1JSUrhhwwY6OzuzTZs2BbqsUt+kpqZyxIgR/Oyzz6hUKpmQkMBr165lmtpbgzr2wjoCcmoWMGRV5PJ4bt68md988w0vXbqk09azZ88ol8v5/fffc9OmTXR29tUeR5KnTum2ZYNIRorsGdT0M9rbqwOkf8jMnTuXTk5OOsvgBUFgly5daGZmxsWLF1OlUvHTTz8lAO2gr4+PDz09PVm1alWOHTtW7wkIpk2bRqlUGgHAksVddKVS6b+9evUSilrglNygGTC4pqdMhUFBQWzRogUbNWrEkydP6sWGwuL58+esWLEi27Rpk+OIXa6uJHCXgB0roSlreG5lSEgIlUp1QPO3IplEdZyFQALq2Q937mTe7siRI7lgwQLu26c+XhPO910RL4P/KADsUDeMaRbPfbC8fPmSXl5enDBhgnbwTBAE7VLo0NDQNL7zt8XJyalILZdeuHAhpVJpZEELb4EKrkQiudKjR48PQnBJ9QfJxcWFt27d0q79Lyx+/fVXOjo6cvz48UU6XkJ+0a1bN06ePDnHvr6nT9XTvWrUIGtgB4ExbNKkCe3s7Dhs2DAKgkAnJxJIeTPYVYUikQMlkoEE/tYRz0WL1FPFNLRp04YHDx6kIKj3a1a7ZupqcDxIa2vho0jxExoaygYNGrBz587pFj6oVCqOGDGCDg4OBECRSMT9+/cXyWl08+bNo0QiiQBgweImuk5OTsd8fX0FfT825CePHj1iyZIltWmoT506VSjn/fXXX+nu7l4kM1EUBFFRUbS1tc3VoGX37uT//kfy/n3GwpIA+fvv6jbLly/PkSNHUR3y8b7W/XDjxnOOGzeOMpmMNjY2LFmyIYFlVMdZKE3AlJUq/UJbWwftLJEvv1R/ezRpfmQy9V+xmExKIsvLo9KJ8IcuvElJSRw5ciRr1aqVobtg+/btBMAWLVrowbrsM3PmTEokkpcFNbhWIIJrb2//a6NGjYq1Dzcj9u/fz/Lly9PR0ZEVK1bk5s2b863ttWvXcsiQIfzmm2/4ww8/8MmTJ/Tz86MgCFQoFLx+/Xq+nauo8+TJE7q7u+f4uNOn1Zl/4+NJzpxJAjQzE7T+1ydPntDLazIBD3p6ln+zOkrQ9liTkpIYGhrKAwcOsEePHuzTpw83b75NW9v2BNoQGEt10Bty/nxdQSXJqVPfvqZSyWloTPV83X8JqJNafuiEhYURQDrfOKl+Uly5ciV37typB8tyxujRoymVSh8WxHSyfBdcMzOzHypXrixklRK7uKJZZtuqVSuWLVuWDx48yHEbiYmJnDhxIt3d3enu7s4GDRposzWsXLmS8+bNY506dbSDDi9evKDDu8ujPnB27NhBT0/PHB2jVJJVqpB//PFmQ40aZJ06vHdP/Sm/e5d8/Fj9es6cVG7fvp0PHz7kkCFphDITDh06RAB88SKazZv/nKErwcVFHc8XILdsSRsHQ0TAlMAzSqVkEVvMWCCsWLGC9vb2WQYrKuoIgsAePXoITk5Ox1iURRfAJ25ubsIrzdycDwxN+DoAtLGxydHacc280/Lly7Nbt268e/cub968ydmzZ7NRo0acO3eutm5cXByjo6Pp6urKEydOUC6XF8TlFDni4+O5YsUKymSyHA1WdutGliihzvwrCHw7efbN/GXNnNq0vVINiYnqbVqxzgDNtDVNKia1oKYXXo3oAmRQEAkMYk2j1gRm0NKiMYcPT6WLC7l374efV23gwIH89ttv9W1GnkhOTmatWrUEa2vr2SyKoguglFQqVd7Javi3GJOUlEQvLy9WrKhO3VK3bl326dMnW8cKgsABAwawRo0aPHbsWLYGEM6fP8+yZcvy3LlzrFy5cl7NL/L4+/vT2dmZtWrVylGwnm7d0oufppRySWHr1urH+rTb313im5EYv8ugQYO0P7g//PBDpufs0CHt+yA62NgQOExz4+asX78+Fy/+m/b2mv0JBD5M4W3RogUHDhyobzPyzOvXr1m6dGkBQCcWJdEFYCSVSqP//PPPAr4F+kOpVBIA27dvTxsbG86ZM4c1atTI1vzCs2fPskyZMjmKq3r8+HE2bdqUhw4d0obEMzY2zjLteHElKSmJHTp0yFX0NgcHXdEzMSErWz6hiSglU2F811ujmWubVbRBpVJJIyMjzps3j5aWrgQOawfPAPUsh2rV0p+rUaPjNBNbE/id21avpkwmo53dFwRavhHxlaxUKceXXeTp0qVLsfDdZoe7d+9SKpUqAbixqIiuRCI598033xT0teud+/fvUyQSEQB37txJAMzOkuYvv/ySkydPztG5/Pz8KJFI2KNHD44YMUIbEu9DXH325Zdfsk2bNrnK9Nypk67IDfwkRO1LSJPF+H09XfJtLzUr9u/fz1KlyhM4SDu7UgwNDeUPP6iP1Ty82Njonsvenvx1zgoCYHk7KZ8+fUpgNoEvtT3nli0X89KlSzrhTQVB4OvXr/W2Uiuv9OjRg9u2bStyiU1zy7Zt2zSLJ/I8sJZnwTUyMhpatWpVoShNci4oYmJiaGamTjx4+fJluri48MSJE1kec/v2bUokkgwTKWaFIAj09PSkhYUFd+3aRVNT01wN3BV1VCoVXVxcMs3u+z5++ol0d6f2kf2HTvfVL65cIUmeO6d+26CBuoebkeCSbyOIZUVyskCgC4GJHDZsGEeNGsWnT9XHaSaXZOTuuHs3QSuwQ3qcIaCiXN6EAFiyZDMOHz6cVapUoYWFBUuXLs0SJUpQJBLR2tqaDg4OnJLmB6S40LJlSx45coQeHh7s3r27vs3JF/r16yfY29v/SX2KLgCFTCZTFseIYbkhKipK2+M8ePAg582bxy5dumR5TLt27bhixYpcnS86OprR0dEcO3YsAeh9qWRBMHLkSDZr1ixXE+XDw9VC6u+vfl+nzptPtJUVmZqqXcTwPjElydhYdb3DhzOvU7IkCbyknZ0d169fTwcHBy5evJgA2bv323rduqnt+uSTtOJ7XSu8IlFp1qpVi97eV2lr68QFCxbw6dOnTEhI4KNHjxgTE0OlUklBEBgSEsKSJUty6dKlRWr1Vlbcv3+fjo6O9PPzIwCamJhkGiejOBEfH88yZcoIANpRX6Irk8mebtiwoTCut8hw/Phx7WBKYmIiHR0dmVmaocePH1Mikbw3VoAGlUrF3bt3s0uXLmzbti2//PJLBgQE8MqVK0UyW0VeuXfvHmUyWa5D+40fT37xxdv3r1+rP9GHfNQjU99+q36f3aB2WQn0xo3qfUePUpuh2M/Pj66urpTJzmUp7D//rGk7hMBRemETFYpIjhtHDht2hYMGDaKTk1Omc7GvXbvGxo0bs2HDhgwODs7exbyHmzdv5nvAe5VKxcuXL9PT05O//PILk5OTaWNjQyMjow8mytr169cplUqTAJiwsEXXxMRkTMOGDYW8LuXbvn07D2fVvShihIaGsn///nzx4gVJ9XLVLVu2ZFg3bXbZ9xEfH0+5XK7tDQFgjRo1CIC1atXSZkn4kJgwYQL79euXq2M7dFC7btu3T7Mx4m2utOho9d/hw7Pf5t696mPedS2HhKi3a1Kd3b9/n25ubly8eDHXrFnD6tW7UTNNLDPSuhv6YyMBgfv2ka1aqffv2LFDJxvGu6hUKn7yySds2rRp9i8oA1JTU7l+/XrtZ2zbtm25akepVOosR//rr7+omVY3b9487XZNqMdDhw7lye6ixLhx4+jo6Jjr+bu5dStYSqVSZX4kktyyZQsbNmzIMmXK0M/PL8/tFTY9evRgZimHBg8ezFWrVqXbHh0dzZkzZ7Jhw4b08PDgV199pU1kOGnSJN6/f5/Tp09nyZIlCYC+vr6sV6/eB5F9QENSUhKNjIz49OnTHB/7rt+0evU3wWcOHOBl1NLZl9M+AUD27fv2fVoXRVrvztOnT2ljY8Nz587R1dWdgJIZ/KtJkhcu6NqrKS9ekNbW6vm9N27coEQiyXLg6ezZsyxRogT37NnDHTt2aMNQZpdff/31TVJN0MrKilu3bqVUKs3xAObr169ZpUoVikQiNmnShNWqVaOLiwtnz57N6OhonbqCIHDRokU62ZeLO0lJSXR3dxcA1GRhia6Tk9Pp/HzcvXnzJiUSCd3d3YtkEIzMuH37NgFkOq90wIABGfpzBwwYQF9fXx47doyenp6ZpgBSKpXaaPyenp4EwOXLlzMhIYGJiYnF2sebkJBAmUyWq/gVdnYZi1hGRaHIWdutW+u6GDQr1jL6Fzs7O/PatWts3rw5ga9Ytmx6n6tSqT6+RAnS0lL9+qD4Ex0bra1jaGHhymnTpr3XvkuXLtHT05PVqlWjnZ1dhok7M2PNmjXp8pJ9/vnn7N69e478xT179uTo0aP58OFDAqBCoeCHuAI1K44cOUK5XB7OwhBdABVLlSol5Hf23tatWxMAi1PetIMHD9Lb2zvTH4pffvmFPXv21Nnm5+dHe3t7hoaGcteuXfTy8soyE/Lvv/9OACxRogQBsHHjxtpHQ3t7e/7444/5ek2Fybhx4zh9+vQcH+ftrSus3bqpg8wc9PqWbXE4UwGWy8nvv886fKPGL3zmDHnpkvr1Tz9lXLdLly789ddfGRwcTAeHRgRG8d3gc2XLqttISiKfP1e/Dl21k6kworlR8hvbLhGwYadOkTm6D+3bt+euXbuyVXfr1q10dHTk0aNHdbYnJiaybdu2lMlk2RLe5ORkWlhYMCoqipcuXWKJEiVoamrKpUuX5sj2D4HmzZsLRkZGw1nQoiuXy59s3bo13y/A3t6erVu31ku+pNwSHx/P6tWrc9y4cRkK761bt+jo6MjJkydz+fLl7N27Nx0cHLhq1Sq+fPmSaTMVZERoaChnzZrFPXv2UBAETp06lba2tgTAc+fO0dvbmwA4a9Yszpw5k3v37i3Iy8135s6dm+NH5IQEdUSvVq3eiql2h4kJx1qsSNfTXb1aHeErMzFu357ctu2tL/fd/Zk9fP3++++sV68eExISuGRJNAF7rl79VLt/92718UeOvD0GIH+cJJDVqvG8qP6bc3QhYJFu0cb7mD9/PgcMGPDeelFRUZRIJJm6pwRBYNOmTTlu3Lj3tqVSqSiVSnno0CHu2bOHNWrUoLe3N3///fcsj4uIiODPP//MuXPnZntguajz4MEDymSypJzO3c1pL7d++fLlCyQ+bnGdRB0SEsIKFSrQw8Mjww/1hQsXWLZsWQ4cOJBr167Vjj5/9dVXBJDlKrUdO3Zoe7VHjx7VBsLRlHLlyrFevXocPnw4AdDR0bGgLrNAmD59uk4OreywatXbRQw7dqg/wbGxVIcYA9IJZkYIgnr+7rBh6sf+97koMpvbm5SUxF69erF169Z89IgEplIub8a4uDjGx6uPrVZN9xitXceOvXk9/M3/MzbT82RGSEgIbW1t0/lRda9VvQR9yJAhWbYVHh5OFxeXbMW8uHjxIiUSCS9evKjN7JGVDaTaH6353Ba3zkFW9O7dWzA1NZ3GghJduVzut3v37sK8pmKBSqXiunXrtGlLNDMbsqJp06bMzhPD8ePHeerUKZ15jvfv39d5TExJSeGzZ89ytaJLn3Tu3Jm//PJLtusrlWSZMuTZs2+3AW9EeOpU3kZlAuqFCg8eqPdp5vBmh9BQctmy9KKbVQ80NTWVnp6ePHXqNNWB0VtyypSp2mPf7Uuop7EpaWT0J4FmBEAxHrBt2+zbmRZfX98s7+GyZctYsWLFbM1++emnnzh+/HhGR0dz5MiRWT517t69my4uLtmOk6F2wThw7969rFChQraOKQ74+flRLpfnqLebk15uRQ8Pjw8mC0RB8ODBAw4ePJhSqZQTJkzgDz/8wHLlyvHrr7/WEc0lS5ZQM9d35MiRHD58OK+8WUH1sSAIAp2dnXO0zHXHjvSBwAcMePMpbtkyXe8WICtWzJldGp9udnq6GpYsWcJPP/2UPXoIVKcJctaK/7u0aUMCiwmUpxN+ZiDA7jX9+dtvObNTw65du1ivXr0M3Vs//fQTra2tuWPHjmy1tWzZMtavX5+1a9cmAI4ePTrL+hs2bKCTkxMPHDjw3rafPXtGAFyzZg1lMlm27Cku+Pr6CmKx+Gvmt+g6OjqeW716deFeTTHl/v37/Oqrrzh69GheuXKFrVq10om9cOzYMTZu3JgjR47kTz/9xMmTJ9PMzEybIvxjYPPmzfTy8sq2W2nCBNLcPL0AJierP8XfmyzQDoBp+P77rH2yGaEJAdm5c9bLhtMSFRVFHx8fOjoOJvDszWN0SrpjAwICaG4+kYAVgZ0EBD4Tl+Ri77W5zhyckpLCmjVrcvny5Trb4+LiCID37t3LdltRUVGcOnUqZ86cyYCAADo6Or53JdmlS5fo5OTE961KValUBMBGjRq9dxVnceP69et0dnaOYn6KLgAzuVyuKm6Pr0WFoKAgSiQSrl27lhnFGhYEgWXLluU///yjB+v0g4uLCwcPHpytuhMnpu99GhmpB8CWLNHd7uLy9riUFPW2NWuyZ5OmrbTui+wSExNDY+NP37gMyhE4SHt79b74+HiuW7eOUqmUnp5jCTzU2vujfB2vmtZnpYq5f4J89OgR7ezsdNwBmnx+uY1pQZLNmjXL1uKJcePGccyYMe+tt3//fq1ft3Hjxvztt9+K7VjOu1SqVEkAUJv5JbpisfinwYMHF58JtEWQs2fPsmPHjrSxsWH//v1ZtWpV7VSxK1eusGzZsh9FwkkNBw4cYMOGDbNVt1IlXWF93+CXWEyOHq0WT2Pjt37ZrHqtr15RO5Mht3TtqiTQ+Y2wDCYQxM6d+9HT05O1atXivn37SGqyFZOOjuq//bCJVuYpWYaWfB+dOnVKlz5q6NChXLJkSa7b1KSn+vvvv9mqVatMOwWBgYG0t7fP1qwEd3d3ncHgjR9IuuQ3rpbzzC/RdXZ2Dv/YfI4Fhb+/P8ePH08A2iSHS5cu5WeffZat49euXcs///yTxT1Y/KlTp1ivXr1s1X23p/tu0G8JQt47A+F9/lnN/rzGlFEo4ghYpxGWEfzmm0tan2tqaip37gwioJ67u3SRUntuK6vsuTMy4rfffksXOGjy5MmcmIcI6YIgsFGjRvzyS3UYSolEwkmTJmXYOahQoUK2PpNTpkwhAA4bNkw9iCgWs0uXLsW+xxsTE0OZTKbMzoBadlwL9iVLlsxzjAUDuixatIgNGjSgUqlkkyZNMo3fQKofT/fs2cN+/frp9BI2bdpUiBbnL1FRUbSxseHatWuzVX/iRLJCBbVfN+2K0vhYdaJIG7zNvpvWxWBkpCu6Gc1EmDRJvS8H7s9MUc9+GEMADA4O5qBBJCDQ0fECf/55FqtXr/7m/5fKY8fUx6xT/JijgbuMSElJYcWKFXXcAZktQ88JP/zwAz09PbXL0Bs0aMAFCxbo1BEEgQ0aNOD+/fuzbEulUvH8+fNcsGABAbBTp04Ui8UEkGc7iwJt27YVAPRiXkVXLBZP/eqrrwr/Cj5wNJko/vjjD7q5uWU6P/LRo0eUSqV0d3fn6NGjtYLbr18/uri40NfXl9999x2fPHnC0aNHs2rVqixRogRtbW1zlKlCH5w/f54eHh7MSaS6YcPepFh/Q4u6sQTIb2x/IUDa2r7dp1K9v6erSaeW24Gsd7l9mwT8aWXlyokTJ7Jv3750cytHoCyBr7lixa43gfATtPONXY1evPeHITucP3+eJUuW1K4sGzZsWI7nQb+LJqqeJvXOw4cP6eTkxIULFzI+Pp6pqan8+uuv6e3t/d65uhcuXCAA7cKgtCW7Tz1Fma1bt9LJyekS8yq6Li4uz8+kHRI2kG9ofuUHDhzI/v37k1QHE1m3bp12NHjUqFEcP348SXUoRAAcMWIESfUXYuLEiRw+fDiNjY1Zr149lilTRvtBLg6xGY4fP05nZ+f3jn5ruHtXvZw3OfltXIPvMIf87z+tr1fDsGHq9x07qv82aaLbVtpgNvn1IKcReje3f9m/f3+uXr2aV69eZUyMJpllIK2spHR0VPfQd+1S11+HQfwcmwiQXbvm/vx169bl/v37mZSURABs164dI/KQgvjmzZsEoDP75p9//qGvry9btWrFuXPnsn79+tk6hyZqXkREBP/8808d0R05cmSxH9OIjIykQqFIYV5FVy6XK4v7zSiqnD17lqampqxYsSKrV69Ob29v2tvb08TEhAD49ddf09bWlrdv3yapjs+b2aOYpneTkpLCLVu2sDil9pk5cyY/+eSTbNdv0UKd5vyLL94IplRGCgIvX1a/Dwt7K6iapAUAaWqq287gwert+X2bNEuOMxLyli2PEGiq07M1NSWvKjoyUFSSMpnAWbNyf+5169axbdu2XLlypVbQNG6s3HDr1i1tO6dPn9Zuv3//Pi0tLSmVSrOVFWXfvn0EwEWLFpF8mxnF0tKSn3/+OQEU++zBJFmhQgUBQCnmVnQBVKtZs6bBmVuAREREaD/UXl5e1ERtKlOmDEeMGJFuxc/jx4+zFYmtS5cudHR0ZHF4SomMjKSVlVW2I13t26cedALI9qLDb5WV6m0tW1Kbu0zTX1ixQv1eoz337qnfz5iR31dDLl6sbjuj1XDffvst+/Wbks7t8Y3tLyRA/wN36ehItm2b/XnCaYmPj2etWrUIgCtWrGCNGjVYp06dbK1+zIjr19UZL9q3b88WLVpotwuCwHHjxvHSpUvZaqdLly4sX768zmf322+/1X72p0+fzlKlShWrKIMZMXr0aIrF4iyXBb9PdGdlNwi3gdzz6tUrbtiwQfsB1GQCzqtP9tChQ7SwsCgWj21t27blkCFDsrVctV69t2IVCDcuLP02wtVnn73dZ2b29hhNz3fGDF23QkFw44a67XdXzAuCQHd39zeP7On9zZmVnApveHg49+zZw2fPnlEqlXLjxo25XpCgUqnYoEEDmpubEwBPnTqVK2G8evWqjgspNTVVx72wdetWAiiWMbXTsnfvXkql0uvMrehKpdJre/bs0YPpHye3b9+m/5vuUatWrfjHH3/kqT2VSkUvLy/27NmTd+/e5V9//ZWt7MX6ICIigt27d2edOnXeK7ymphpBukFgJtWZdccSeJ1utkLamQyalOnqpbhZp1zPCxpfc9pUQiT5999/08vLiwsWCOmEVSYjJ7r+SoVx+ulvuR1YEwSBJUuWZPfu3dk+LxOQSf78888EQBcXl0yj6mWG5mnOyclJ+/kTBIHjx4/n+fPntYHVi5NLLDMCAwPp4uISx9yKrpubW3Rx/+UprrRr146//vprnttJSEhg9+7daWVlxbp169LGxoY9e/bM0+BKQSEIAkeMGMFKlSoxJCSEpHqWx7///ss///yTN27cYHBwEs3MrhBoQqAkP4EnFbJFLF++H83MOmbaW2zY8K0PGFAHQi9IMupJd+3alRMmLNMKsovLO/Xmz2cYHPPc003L3r17+fnnn+c4R9nRo0fp6+vLyZMn8+7du1SpVDQ3N+fjx49Zo0YN/vzzz9lqJyEhge3bt+dXX33F4cOH08TEJF2UsWXLllGTIaW4uxcEQaBCoVAxt6Irl8uVxSUD6YdGu3btWBBPGVFRURw1ahR9fHyKrNth8uTJ9PDw4LRp01ixYkWWL1+enTt3ZpkynjQysmWJEhKWKbORQKpWsJKSkujkJCWwiup5siUJxNPSknRyyliIxWJ1TIf//lO3ocnimxeR0zBqlK7oXrx4kebmzgRidbZrUrj/8gsp/PdYa9unn5ImJmTlynm3JTdoHvdHjhxJZ2dnBgUFsWnTpty8eTOfPXtGGxub90a1EwSBXbt2ZY8ePZicnMzExERqAj29S3h4+AeRMZgky5YtKwCwZC57usV7mUgxxsPDgytXriyQtgVBYN26dfn9998XSPv5weHDhzlixAgeO3aMgiDw++/V6W68vYOZkJBIhobyDrwJqOfGRkaSwGECbWhp+R2BMgTervKLi8u+DxVQB0lP2+nKqSBfuaJuJzRUnW3B1rYygS3a9o2N1YIfGKjO8Zb23K/rqyfwPnig/sEID8/HG5tNBEFg3759+cknn/B///sfK1SowDlz5rBt27aa3tx789vt2rWLnp6eWjEdM2YMW7Ro8cGn9mnVqpUKQEvmVHQB2Ht5eRlEVw/s3buXDg4OBerfunTpEivrqxuVQ8aP1xWlzp3JqM37SaQPWq7pvK9fH01Awhs3nmgD37xbNP7ezPZryrt+4uwIb2qquu7mzQLHjh1LkagTgfS+3HeLJx7xJaTqeW8kR44ka9VSx5/Iw4reXJGcnMyuXbtyzJgxnDFjBsuVK8eyZcvy4MGDrFWrVqYhHf38/NiuXTva2try3Llz2u01atTg9u3bC8t8vTFw4EABQKZpfLIS3bqNGjUyiG4hExkZSQDZilGaF1JSUmhhYVEs4vi+mxMtqyKVqo9Rj45b09j4bVr2qChdP+obtzFHj07fTteu5KFDZKdOGZ/HyUmdOy0wUO0KyKgXDKioUHxPV1dvAmEZCndSUtbXY26u+76whffly5d0cXHh8uXLOXDgQMpkMlpYWLBt27asW7euTt3ExEQOHz6cXbt2Zc+ePdMlzfztt99YvXr1DM8TGBjImJgYhoSEsHr16lqffnFk0qRJAoBZzIXoftK+fXuD6BYicXFx7NixI6VSaaEMKHz55ZfZDrSjTzIKeJNYvT6PeH2ThWDdJiDPsGdLvu3BaqKQzZ6tFkKxmDo+12PH0retmQWRUalcWd1JTUxMpEjUhUAjAqFs107dvr29up4m8W9o6LttCPxPXI7rKy1krVrpe8aVKhXuvSfVPVc7OztGRETw2rVr9PHx0c44SLvoYvHixaxTpw6HDBmS4YKJgIAAAuCSJUv4ww8/sF27dpw2bRrnzp1LAJw9ezY3b95MAOygWSNdDHlzPSuYC9Ht+emnnxbvocRixsaNG+nh4cHwQnDiqVQq2tvbc/To0QwNDS3w8+WViRPVIR19fUk+fEgCtHwzKNUR+wiQv6A/d5v35pXWk9ir5GwClTIVR0tRnPa1N+7wL6feZO/ejPp0EAFyWY2NDOz0pVrobAN51q23WriNX5K91XVjuw3IpH0V1SEeuxFQZ/zdbt5Pe1w7xQ0CpLJnH+0xu817sy9+JUBGmkjUGxs0YN06usJb2D1dDW3atNGGjvT396eDgwPXvBOoeMyYMZw3b16mbUydOpUSiYQjRozgu7EXADA8PFybF7CSPn5d8ok3szF+YS5Et1e3bt0MoluItGjRolAjh/3www+sWbMmAXDw4MHaQauiyqFDZNWqpLB5CyvjFgHyOnzoZ+RJgGyEM6SnJ+npSYl4JIHRrI5rBMibqMx14qHsYXUonUjWwmUOwC/8TTaOd0u2Y1XT+zr7NW3+KvuOAPmqdF0KZT21+/ehAwFyC3oz2MqTMx2c6QJXqnOmpRfldiX+1nkf6VFdew6ALIUAEuA60VCWLZ3Kb77Rj083Lf/88w+lUikDAwNJksOHD+fXX3+tU2fu3LkcOXJkpm306tWLmzZt0sYHWbp0KT08POjj46P94Q8MDCQAzp07l0FBQZwzZw4rVKjAxo0bF7jLLb9Yvnw5AWxkLkS3Y4cOHQzuhUIiJCSEABhZUDP2MyE6OpoAOGPGDFauXJmVKlXKNFW3vhEEUiIhRSJ17++4uDXZuDEpCPzmG/WnWaXSLH7oxVGjltDZ+a1vVENSknqeblrhK1mS7NFDrX3vimSrVm/j7GrcEho3xIsXareBpm5gYCAdHBxoavpYpw25XO0D1rgX0hZXV3L/fnXIyt+3qK/NFYEsIU7gwwdF50fw22+/1aZpP3jwIAHwwYMH2v0BAQG0t7fn9QySw+3cuVObyNLOzo4SiUTbw+2eZhk3Sc6ZM4e3bt3SxhAZOnQoV61aRS8vL5LqoFDLli3jjh07imRQp3nz5hHASuZCdBs1bdrUILqFRGhoKDUTxPWFIAjaWKcjR47k0aNHGRoaWug/BJnxrm+3Qe1k/rpJ4MWLaj9q2n1Tp/5Dd3d3xsfHc9w49bbkZLV4fvop2aWLejmxmVn6ZJfvCrJYrJ6u1qDB25gPmqIZEAsIIIEntLOTskuXRQTeCqyNjW77Jia6bRgZka1bq+vb2Oj6d/XZu30XPz8/Ojk5adMCjRw5ko0aNdKuoiTVLoaMFk58/fXXXLhwIW/fvk1nZ2devXqVAPjTTz+lqxsREcH//vuPcXFx3LNnj44L4vbt25TL5ezTpw/r16/PqlWrcsmSJRmmwUrLnj17+Pfff+fxDmSPN4Ha5zEXoquoVKmSQXQLCc3cWQB6tePRo0fs27cvZ8+ezbp169Le3p7W1tacOnWq3hdTvJu2RyIh+/Qha9ZML2TVq5Nt2vThwIFDqVKpe48eHmpBLV1a3dvNjLQ9V42wRkWRp06RFha6+zTzbffsIYEvqE7Toz4Hqa6TNlTjy5dplzGri0b0BSF9T7uouTYHDhzIUaNGkSRjY2P5xRdfcMiQIdr9a9asoYWFBaVSKdu0acMff/yRQUFBLFeuHA8fPsz58+ezXLly1CwLbtq0qfbYr776ilKpVEdk//jjDwKgjY0NjY2NaWtrq42/LAgCDxw4wM8//5wODg4cPXp0pivvmjVTp7ufM2dOAd4dNV988YUA4BvmQnRFJUuWLDrPNh84ml7E2dxkRSxggoKC2KJFC3br1i3bkcAKgqzS9rz72G5qSlauHE2RyJvOzr+ke6R/Xw8ys8UQ7wpygwbklClk69axVGf5fa4j1oB6tgNJJiSQtWuTU6eqhTajXvb7UhPpm8jISMrlcm30uokTJ6brrcbExHDFihVcuXIlO3furBXQ2NhYBgcHa99rZkDExsaSJCdMmEATExOOHz+e//vf/wiAY8aM0aYLkkqlHDZsWIYr154/f87x48dTLpdnGBsiPDycbm5uBJDt2M25pX379ioAHZlT0SXVy4CL8sDKh8SGDRuKdGrqpKQktmrVik2aNOGsWbPeuwS0oJg4MeNBpYx6pyR55coN2ttLKJdH6OzPSw8yI0EeN24cTU0/1zmHvb3aVwyoe7E9e6onMLzvK5XZNRYVDh06RCcnJx4+fJhdu3bljh07Mq2bnJzMa9euaR//NcHVjYyMtOL722+/ZXm+8PBw3rp1i8uXL2e1atVYtmzZTAfVIiIiWKFCBe7Zs4cqlYrBwcFcsGABfX19tefbtWsXDx48mPsb8B7exNSVMDei6+rqGl/co/4UF54+fUoARTpBX3JyMrds2cIGDRpw9uzZ+jYnHZn1Tjt27MhOnbYUaA/S1dWVrVvfSzdA5uurfj1lClmnjrq3+yFw7tw5SiQSWltbs2PHjvz999+zNaj1+vVr7eDZL7/8QgC8devWe4/7/fffCYCCIPDo0aN0cHDQ8SWnZd26dZTL5RlOTXv+/Dl79+5NAAW2HNnZ2Tn3AW8UCsWDv/76q0AMM6BLREQEXVxcOGTIkCI5IpuWPXv2sHXr1vo2I9vMmjWLXbp0KdAepFQq5e7du7XC36UL2bfvWwG2tdVNqPkhsGvXLmoynFhbW7NJkybs0qULS5QowREjRmQ6uJWUlKR9/M/uZ93BwUFnvKNnz57pUs6n5dNPPyUATpo0id7e3hw3bpzWNXb69GkCYNmyZXnx4sXsXm62CAsLo7OzcxJzK7pGRkYrZhREaH0DGRIbG8u6deumm3Re1AgODqajo6O+zcg20dHRLFGiRIEm6vz666/53Xff6WybMKFo+2fzSmhoKNu1a0dra2t26dKFZmZmbN26NQ8fPsxu3bqxbt26nDt3rrZH+e79V6lU2R6cVSqVfPnmV+vx48d0dXXVcUtoBtVGjRrFf/75hx06dEg3jzgt69ato5mZGWUyGQFkK+VQdjh27BidnZ0fMbeiC6Bls2bNDE7dQuTixYuUyWRFepXY9evXi02wHA2VK1cu0EHKHj16pPNNvjvboqjNRMgPBEHgvXv3uHTpUs6dO5c+Pj60tbWlu7u79pG+evXqHDdunPZ92hkK7du356NHjzhw4EAC4JQpU957Tk1ONQD88ssvSZIrVqxg+fLlOXXqVDo6OvLw4cO0sbHJcnWnIAgMCgoiAJYoUYLt2rXL8/2YPHkyjYyMljMPoiuSy+Wqouxn/BAZMWJEkQ67OGvWLPbr10/fZmQbQRBobm7+3lCEeWHGjBnaLM0aivpMhIIiNDSUt27d4uHDhzlu3DieOnVKK5IlS5akQqEgAJYvX54TJkzQ7hOJRNlamPPs2TNeunSJYWFhLF++PKVSKe3s7Lht2zaS5DfffMORI0dqc8W9LxPJ3Llz6erqSgB5nhZZo0YNAUA15lZ0qR5Me33z5s08GWIgZwQGBtLe3r5I9nZv3rxJJycnPn78WN+m5IiGDRty3759Bdb+3bt36eTklO5/VtRnIhQmsbGxWoE9evSozvSxy5cv56rNlJQU3r59WyeB6+vXr1mqVCk2bNiQAFihQoUs25g5cyYBdULYvEyJTExMpFwuV/I9mvpe0TU1NV2TnS6/gfzlq6++0k5CLyqkpqayXLly/P333/VtSo5Zu3ZtgU/Ja9asWYEK+4dAUlISz58/z5SUFCYmJnL9+vV89uxZhnV37NjBzp07Z5nX77///iMAnjx5Umf7q1ev+Mcff7BNmzb85ptv3mvX3bt3CSBP8X4PHz5MhUKRpT+X2RFdAB7ly5c3+BcKmfDwcDo5OfHevXv6NkXLgQMHWKdOHX2bkSs2bdqUbo1/flO/fv0sU94vW7aMz58/L1AbPhROnjxJLy8vAmDp0qU5aNAg1q1bl4cPH9ap99lnnxFAoQaKyoy+ffsKAMbwPZoqxnsg6R8bG5vk5+f3vqoG8hFHR0dMmTIFQ4YMgUql0rc5iI6OxtixY/Htt9/q25QcExsbi+nTp6Nv374Feh6xWKzpqKTD398fX331FX7++ecCteFDYPny5RgwYADq1KmD06dPY+PGjbC2tsalS5dw+fJlbb1nz57h999/R58+fRAZGalHi4HU1FScOHGCAFa+r+57RRcAIiIifn8zkdlAITJy5EgkJCRg165d+jYF27dvh1wuR7du3fRtSo5ZtWoVateujU6dOhXoeWJjY2FlZZVue1RUFLy8vNCgQQOcO3euQG0obpDEP//8gx9//BFBQUE4f/48Ro8ejT179mDTpk1o2rQpmjRpgujoaFStWhU//PCD9tjVq1dj9OjRSE5ORkpKih6vAvjrr78A4AnJ5PdWfl9X+M0vt8TV1VUo6pP2P0SWLl3KRo0a6W3ZrYZVq1Zx6NCherUht9SvX5/Hjh0r8PN4e3tnGNZw/vz5NDMz482bN1muXLkCt6O4oFKpOHjwYAJg27Zt6enpSQDpsmAfOnSILi4u2nm6GmrWrMmTJ0+yfPnyBEA/P798sSshIYHjxo3L0bzuJk2aCAA+ZTb0NFs9XZJhSqXS79ChQ7n+JTCQO0aMGIHSpUujTp06uH79ut7sKFmyJAIDA/V2/twSExODO3fuoE6dOgV6nuDgYAQHB6NUqVLabWFhYRg7dizWrFmDu3fvYuPGjfjvv/8K1I7iAkkMGjQIT548QWhoKA4cOICePXuiZ8+eaNOmjU7d4OBg2Nra6vRmb926hYCAANStWxdnz55FhQoVcO/evXyx7dixY5g/fz4CAgKyVf/p06f477//Ukhm75E0O8pMdW+3dfXq1Q0LJfSAIAj87bffKJFI+N1332njmRYmp0+fZuPGjQv9vHnl+PHjbNKkSYGeIyYmhm3atOHENPPCZs2aRRMTE/bu3Vs7eAaAdnZ2BWpLcWDfvn3aqWKJiYnvrS8IAqdPn87SpUszODiYKpWKjRo14qpVq7T7LSws8i3u861btwiAL168yLKeUqnkF198QSsrK5qbm2e5ICJtybbokoRCoYi4cOFCvlyYgZzz4sULbUbWwnhcTsuBAwfYvn37Qj1nfvDnn38W2FSx1NRUzp49mxKJhIMHD9ZOrI+KiqJUKuW///6rrfvixQva2tpq0918rFy6dEkruHfu3MnRsV9//TWlUinHjBnD+vXra+fUKpVKAvmXdSUhIYELFizI8gfhzJkzbNu2LQHQ1tZWBcCEBSG6APo0bNjQ0NvVM/v376eDg4N2BU5hsGzZMvbt27fQzpdfHD9+nLVq1cr33G9KpZKff/45mzZtqpOyhiQvX77MGjVq6GzbsmULO3bsmK82FDcSExPZsmVLzpkzJ909yw6CIHDKlCn87LPPdKbe3bt3jxKJpNAi9GlEHgD79OlDGxubbcyJjuakMknI5fLIohho+2Pj8uXLlMlkhRaRbOzYsVlmei2qJCcns2LFivma1DA5OZnt2rVjixYtMhzgvHnzZrpVUI0bN84y7uyHTlJSEn19fdmlS5d8Dzy0fft21q5du0ADGqVFEATu2rWLf/zxB2UymRKABXOgodkaSEvLq1ev+rxZKZXTQw3kI7Vr14ZcLsfFixcL5XxBQUEZTocq6piammLEiBH5Ou1u4MCBMDExwdGjR1GiRIl0+21sbBAeHq59f/PmTfj5+aFz5875ZkNxIDk5GX/88Qe6desGDw8PiMVibNu2DRYWFvl6nk6dOsHZ2RkjR47M8bEJCQmYPXs2Xr9+ne1jRCIRunbtir/++ouxsbGrSCbm6KQ5UWhNkclk/lnFsjRQOHz//ffZisqUV/z9/enk5MTo6OgCP1dBEBAQQIlEkm853sqXL8+rV69muE8QBHbr1k0nzOOgQYM4c+bMfDl3ceCff/5ho0aNaGJiwvr163PNmjW5cifkhOfPn2d7YC4t//77LwHkOIDT7du3KZVKkwAYMYf6mSvRBeDh7OysKq5fwg+Fw4cPs3nz5oVynrQJBIsjTZs21SY0zCvDhw/PNFbr3r176erqyrCwMJLq5dx2dnZFMnhRfjNz5kw6OjpSLpdz27ZthTpoGBERQQA5drclJiZSJBJla7aCBkEQWKNGDcHY2LgXc6GfOXYvvOkd+8fHx28dPXq0wcegR7y8vPD48eMCaz8uLg6HDx/GyJEj0bNnzwI7T2EwYcIErFmzJl/aSk1NxZMnTzLcd/78eYwYMQJOTk4AgI0bN6JTp06QSCT5cu6iSkBAACZNmoTXr1/j0aNH6NWrF0qWLFlo5w8NDQUAfPbZZzk6ztzcHGvXrgWgnledHdatW4egoCD/1NTU7TmzUk2uRBcAoqOj+x0+fDj+n3/+yW0TBvKIm5sbQkNDkZSUlK/txsTEYM6cOXBxccH8+fPRvHlzDBs2LF/PUdg4OjoiNTU1X9oiiXr16mW4z87ODhEREdp669atw/Dhw/PlvEUVkpgxYwY+//xzqFQq2NjYFLoN5cuXR4UKFXLlNx8yZAhIokqVKu+tGxwcjMmTJwshISENcmEmAMA4tweSpEgkatWvX78Lt2/fFtna2ua2KQO5xNjYGKVLl8aTJ09QqVKlfGnzyZMnaN68OapVq4YbN27Aw8MjX9rVN8bGxnkWXZI4cuQI9u/fj+3bM+7kGBkZaV/v378flpaWqFu3bp7OW9Q5c+YMTp8+jRs3bkAsznU/Ls/UrVsXL1++LLD2BUFAz549GRUVNZFkSG7bybXoAgDJSzY2NmsHDBjwxZ49e0R5actA7ihfvjwePXqUL6JLEv369cO3336L0aNH54N1RQc3Nzc8f/4cJCES5fyjGhwcjJ49eyI6Ohrr169HixYtMqzn7e2NYcOG4dGjRzhz5gz++OOPXJ2vOPHHH3+gdu3a0HfHq379+ijIJ+958+bh8ePHD5KSkubkpZ08/yzFxMQMv3jx4qv169fntSkDuaBy5cq4efNmvrT1/PlzPH78GF9++WW+tFeUsLe3h7m5OYKDg3N1/Nq1a5GUlISbN2/C19c303odOnTAtm3b0KRJEzx79gxt27bNrcnFgqNHj2LVqlVFIqbE0aNHUaNGjQJp++rVq1iwYEFKaGho7by2lS/PAiEhIVUnTZqUqs+ALB8r9evXx/nz5/OlLQcHByiVSr3HJi0IRCIRypYtm2txiIiIQIsWLXTcB5nRuHFjjBs3Tu89v8JAcz+PHTumVzv27NmDmzdv4osvvsj3tsPCwtC1a1eGhYW1JBmf1/byRXRJhoWGhrbt0qULszsCaCB/qFy5Mh48eJAvbVlZWcHT0xMfasD6Nm3aIDdPZCRx8OBBfP755wVgVfEmKCgI48eP187W0BerV6/GtGnTMlyskhdSU1PRsWNHvn79ehrJs/nRZr55vUmeCgsLm9KmTRsmJ78/jq+B/EGpVAKAZv50nggLC0NgYCAcHBzy3FZRZOzYsTh//jyuXbuW7WMEQcDEiRNhb28Pb2/vArSueBIUFFSoU8MyYuXKlQgKCsrS7ZMbSGLw4MH09/f/JyEh4X/51W6+DjUmJCRMf/bs2f5evXpREIT8bNpAJpQqVQp2dnY4ezbvP8J//vknWrZsCU9Pz3ywrOhhaWmJAQMGYMuWLdk+Zv78+Thx4gSOHj36wQ+I5ZTk5GRcuHChwPyo2WHdunWYPXu2dqZIfjJnzhz89ddfwWFhYc3zteHcrKh4X5FKpXdHjRqV75GdDGTM2LFjOWvWrDy3061bN27cuDHvBhVhHj9+TKlUmq3lotevX6eTkxMDAgIK3rBiyOHDh9mwYUO9nf/o0aNUKBR8/Phxvre9adMmSiSSWACWzGd9LBDRBSCSSqXBU6dOzfebYSA9R44cobe3d55C2129epUSiUS7fPVDpnXr1ty6dWuWde7cuUOFQsGdO3cWklXFj0mTJvGHH37IdL9KpeLTp0956tQpjh07lpUrV6ajoyNr1arF48eP5+nce/fupUQiYUFEPNy/fz8lEkkSAAkLQh8LolGqhddMIpFEzJ07N99vigFdBEFgtWrV8vRBbteuHdeuXZuPVhVd6taty19++SXT/adPn6ZMJivUeMXFjSdPnlAikfD+/fvp9r1+/ZpTp06lp6cnHR0dWadOHU6dOpWXL19maGgoly9fTgC8cOECU1JSuGHDhmzHTAgJCeHIkSPp7OzMK1eu5Pdl8ejRo5RIJCkAyrKgtLGgGqZaeC2lUmnkwoUL8/3mGNBlzJgxnDNnTrbqxsXF8ebNm9r3MTExlEqlBfKYVhQpVaoUDx06RJJ8+vQpV61axSVLlnDcuHFs3LgxXVxc+Ndff+nZyqLL33//TXd3dy5YsIAk+ejRI27atIkbNmzgxo0bWblyZfbt25fnzp3L1MUolUq5cOFCbTBwTRaIrLhy5Qrd3Nz49ddfMyQkJF+viVQHvJdIJKkAvFiAupinFWnZ8BfHi0Qi19mzZwclJSXZTZgwoSBP91Hj6emZ7UUSd+7cQb169eDg4ICxY8ciJCQEPj4+KFu2bMEaWUQgie7du6N69eq4e/cu2rdvD0tLSyQmJqJXr14YNGgQzMzM9G1mkWXJkiUwMTFBjRo10LVrV5w9exZt2rSBSqWCsbExRo8ejUGDBmU68Hjr1i2EhoZi7NixAIBTp07B2DhrKfrll18wfvx4rF69Gl27ds217StXrsTVq1exceNGne0HDx7E4MGDU8PCwnxIPsz1CbJDQSq6pgCwkEqlId99951hcK2A2LVrF319fbNVVxAEzpgxgwDYunVr9u3b96PK3ZWamsoFCxZww4YNTEpK0rc5xY7nz5+zfv36rFq1KhctWpTjXmdUVJS2hwsgy7GIhIQEDh06lB4eHnz48GGe7E5MTNSeM607Y9u2bZrYuB4sDD0sjJNQLbxGMpksoH///kJ2HiUM5Izr16+zcuXKOTpmzJgxrFevXqHlljJgQINSqWTXrl1Zu3btTOuEh4ezbt267NmzZ74E0N+6dSsBsGvXrtptixcvplQqjQegYGFpYWGdiGrhFUkkkrPNmzcX4uLi8nwTDbzl9OnTVD+4ZB+VSkUrKyuGh4cXkFUGDGRMamoq3d3defDgQZLqcQbNNL579+5x9+7d9Pb2Zn49HWsySwBg9erVef36dY4ePZpSqTQEgDULUwcL82SaYm9vv8nb21sICgrK8800oCYhIYFyuZz//PNPlvUSExMZEBDAO3fucPbs2SxVqlThGGjAQBpiYmIoEon45ZdfcuTIkQTABg0asHr16nRzc2ObNm24fv36fBHcv//+m66urpw0aZJWeKtVqyZIpdI7yEW6nbwWEamf5A+mpqajJBLJ0t27d4vr1KmjFxs+NA4cOIDRo0fDz88v00GMlStXYtSoUShfvjx8fHwwZcoUVKhQoZAtNWAA8PPzw/Lly5GUlITDhw+DJJYuXYpOnTrlS1zesLAwtGjRAtHR0ahQoQICAwORkJAAY2NjRkZGbo2IiOibD5eRY/QmugAgEonqS6XSv2fMmGEydOhQvdnxIVGiRAkEBgZmmB4mOTkZiYmJcHFxwZMnT6BQKPRgoQEDaiIiIpCSkgJ7e3uoVKp8CVZDEidOnICtrS38/f3Ru3dvNGvWDJGRkRg6dChmzJghvH79elRycvLqfLiE3BupzwLAUSqVvurdu7dQWHnrP2T69u3LRYsWZbhv6NCh2scrwzxUA/okJCSEzs7OFIvF2s9kXnn48CHbtGmjMzMCAJcuXcpp06ZRKpUmAqhBfWuevg2gWnhFDg4Ou8qXLy/kdVrIx87ff/9NAOzQoUO6lOMHDx4kADo5OTEmJkZPFhr42FGpVOzRowdHjRrFESNG0NzcnJcvX851e0FBQfziiy90hNbW1pajR4+mv78/W7RoIUil0kcALFgU9E7fBqQtxsbGvaRSqXLt2rWG+by5RBAE7Qdv79696faPGzeOAOjn56cH6wwYoM5KtDp16uQqoJBKpeLJkyfZp08fbVtisZjjx4/n69evSapn9Li4uKisrKzmsQjom6bo3YB0BgESmUwW1K5dO+FjCL5SEGh6u+7u7un2rVixgj179tSDVQYMqFm5ciUbNmzI9evXZ2v5b1oePHjAkSNHUqFQ6PRshw0bpp36mJSUxDFjxlAqlSYUBXfCu0XvBmRWrKysFigUClVGvTUDWZOamkoA7NWrV7p9vXr1yhf/mQEDhcmVK1fYrl07SqVSdurUiR4eHvTy8uLOnTuZdizo5s2b9PT0FBwdHY8DMGER0LJ3i94NyNI4wEsmk0V069ZN0DwyGMgekydP5jfffKOzTRAEWltbc//+/XqyyoCBnLNkyRLtPN6mTZtSLpdzxYoVOmMWycnJnDRpkiCVSpMBdGQR0K/Mit4NeK+BgMja2nqZQqFQbd261eDrzSabNm2ira0tv/jiC53tGzZsoKOjI/v06WMYTDNQLLh9+zY9PDzYvn17bt26lfHx8Tr7L1y4wDJlyghOTk6nAJixCOhWVkWv83RzgkgkcpfL5efKlSvnvHHjRpGHh4e+TSryLFy4EPfv30+XjPHVq1do2bIlpk+fji5duujJOgOalFb5sRDgYyQyMhJjx47l4cOHE0NDQzuQPK1vm7KFvlU/p8XIyOhLqVSaOmHCBMO83vfg5+dHJyendNGZ4uPjKRaLGRoaqifLDKhUKrq6uhIADxw4oG9zihUqlYrr1q2jQqFQWVtbr8WbRV7FpejdgFwZDZg5ODgcKVmypLBjxw6DyyEL1qxZw0qVKumk4Xnx4gUB0OAn1x+aACxVq1blixcv9G1OseHSpUusXLmyIJPJHgJwYRHQo5wWvRuQJ+MBb5lM9qxatWrC1atXs/df+8gQBIETJ06kp6enduDhyZMntLa21skeoWHLli2sUqUKR40ale0UKgYMFDSBgYHs2LGjIJfLY1HEB8reV/RuQL5cBNBVLpfH+/r6CoZJ/xnTrVs39uvXj/v376cgCOzXrx8BcPPmzTr1RCKRdu6joQdmQN+8fv2aX3/9NWUyWaqZmdkkFgG9yWvRuwH5WUxMTH6QyWQpAwYMEF6+fJn1f/MjIyUlhZMnT9au3NEIq0Qi4d9//62t9+rVK/7777+MiIjQo7UGPnbi4uL4v//9jzKZTGVtbf0riuic29wUvRuQ7xcEiCwsLBbLZDLll19+WSAJ7Iozf/75p85Knr1799LZ2Zlly5bl/PnzDS4FA3olPj6ec+bMoUKhUNnb2x9GIQcYL4yidwMK7MIAEysrqzUymUw5atQovnr1KrP/80eHSqXi7t272axZM5qbm7NJkyZaEc4opbYBAwVNfHw8Z8+eTYVCoXJwcDgOQMIioCMFUYrNPN3cIhKJTEqUKLHM2tp6cLt27YwmT54sKl26tL7NKjIkJCTg6NGjMDExQePGjWFra6tvkwx8RERERGDRokVYv369kJycfCoyMrIPyTB921WQfPCiq0EkEhmZmppOc3Bw+LZmzZqm06dPF/n4+OjbLAMGPkqeP3+OWbNmYdeuXaqkpKSdMTExw0hG69uuwuCjEV0NIpFIZGRkNFIqlc5ydna2+umnn0QdOnSAkZGRvk0zYOCD59KlS5g2bRpv3LiRGh0dvToxMXEcyVR921WYfHSimxaRSNRSLpevNjU19fj6669FgwcPhp2dnb7NMmDggyI5ORk7d+7ErFmzGBkZGfnixYvvSW7Qt1364qMWXQ0ikaiUnZ3dKjMzs9ZNmzYVf/fdd6IaNWro2ywDBoo1AQEBWLp0KbZv3y4IgnA7NDR0BMlL+rZL3xhENw0ikUgkFovHyOXyn2xtbW2/+uorUZ8+fQyDSwYMZJOUlBTs378fixYtYkBAQPLr16/Xp6SkjCcZr2/bigoG0c0EkUhU2cHBYbGJiUmTevXqiUePHi1q3LjxRx8RShAEzJkzB8+ePcOyZctgbGysb5MMFAHu3LmDFStWYN++fQLJByEhId+RPKJvu4oiBtF9DyKRSARgqLOz8xSxWKzo2bOnaNCgQahYsaK+TSt0Xrx4ARcXF+37uLg4WFpa6tEiA/rkxYsX2Lx5MzZs2MD4+PjY0NDQZUqlcibJRH3bVqTR90Th4lQA2JuYmCxydnaO8fT0FGbPns1nz57xY2HBggUEQIVCwbt37+rbnEJBEARev36dc+bM4ciRI7lp06Y8tff48eN0WZo1PHjwgD/++CPbtm3LpKQkkmRAQAB///13Tps2TZsDjFRnSkgbXS8yMpJ+fn68evUqv/32WwLg9OnT82RrRkRERHD9+vWsUaOGoFAokqysrHYAKMsi8P0sLsXQ080lIpGorJWV1c/W1tYd7e3tzfr37y/q1asXSpYsqW/TCoxx48YhLi4Oa9aswb59+9CpUyd9m1QgJCUlwc/PD3v37sWpU6fg7++PTp06QSwWY/HixQgMDISJiQkiIiJQsWJFqB+G0vPixQscPXoU0dHRePnyJU6cOIEbN25g4MCBcHNzg7GxMUxNTREeHo5jx44hPDwcderUwZ49e9CsWTMEBAQgPj4eTZo0wc6dO1G5cmW0adMGx44dw507d2BnZ4fY2Fg4ODggISEBTk5OCAkJQWKiuqNZuXJl3LlzB7Vr18alS5cytfN9REREYM+ePVi/fr0QGBioSk5OvhQRETGR5Llc3+SPGIPo5gMikcjLyspqqo2NTUdbW1vz3r17iz799FNUqFAh1x/0osjFixfRpk0bxMbG4qeffsLQoUPh5uamb7NyxatXr3D69Gn07NkTYrEYAQEBOHz4MM6ePYujR4/C0tISvr6+qFevHnr06AEzMzOQxKhRo/Dbb79pBTMsLAzLli3Dl19+qW13/fr1OHLkCB48eIA2bdrAyckJzs7OcHV1hZ+fHwD1gFNYWBhsbW1RokQJNG/eHI0bN4ZSqcTRo0dhbGyMMmXKwNPTE2KxGA8fPsSGDRtgY2ODFi1aoFatWggPD4etrS1ev34NV1dXiEQipKam4vjx4zA1NcXt27fx7bffon379hg2bFiOfiSDg4OxZ88ebNmyhYGBgcrk5OTzkZGR01hcsjMUYQyim8+IRKJy5ubmPzg6On4qFottOnToIOrZsycaNGhQ7Aedtm/fjt69ewMA3N3dERsbi3r16mHx4sUoU6aM3uyKi4vDv//+i8qVKyMiIgKvXr1CZGQk4uPj4eHhgVq1akEkEkGpVOLKlSs4efIkVq1ahdjYWJiZmcHb2xv37t1Dp06d0KBBA3To0AEymSzT8z19+hRRUVHYuXMnZs6ciZkzZwIA7t27hyNHjqBHjx7o1q0b6tevjxIlShTWbUhHVFQUhg0bhh07dgAAlEplpouASOL27dvYuXMnduzYIcTFxaXExsaeiI2NnWPo0eYvBtEtQEQika1YLP5aoVB8oVKpnL29vUU9evQQtWvXrlj2EOPj49G5c2ecOHECAFCpUiU0bNgQO3bswIABAzB9+vQCEZnnz58jKCgI586dQ9WqVREXFwdTU1M8f/4cZ8+exbFjx5CamoqkpCRYW1vj9evXaNKkCaRSKY4fPw4zMzMYGxsjMjISpUuXRrt27fDpp5/Cx8cHu3fvRlRUFIYMGQJTU9P32kISX3zxhTbvnFgsRuPGjVG7dm2ULVsWHTt2hFwuz/d7kFuUSiVMTEwAAAMGDMDatWu17yMiInDixAns2LGDFy5coJGRUVRISMi21NTUeSQD9Wn3B42+ncofSwEgAtDOxsbmiLOzc4K7u7tq2LBhwoEDBxgdHU19oUndoykKhUKbxufFixe8fPmyTv0TJ04QAMuUKUMANDY25tSpU9m7d296e3vz4sWL+WpfQEAAFQqF1r4KFSrwk08+oampKfv168c1a9ZoBzNPnjzJ4OBgnQGmiIgI3rx5k8+ePcuX7McjRozQ2tK3b1+eO3cuz20WBIIg0N/fn7/99hu9vLwIgFKplCdPnuT48eNZsWJFQaFQpEgkkssABuEDildb1IveDfhYCwBHAOPlcvkduVye4uXlpRozZgyPHz/OuLg45gdKpZJ3797VEVU7OzvOmTOHV65c4U8//cSbN2+ydevWOnVKlSrFAQMGaN+bmJiwSpUqHDhwIAHQwcGB1atXp4+PD42NjVm7dm0KgsBt27ZRJpNxypQp+WL/4sWLCYDz58+nIAhFItbvuXPnuHLlyiI1a0WpVNLf35+7du3izJkz2aNHD7q4uFAmk7FZs2bs0KEDK1WqJMjlcqWLi8tTsVg8D4YZB3orBvdCEUEkEpUSi8VDZTJZL5KlbGxsxM2aNRO1atVK1KBBgxw9sr548QLz58/HokWLAKgfgTXpvt+lZMmSePbsWYb7qlSpgiVLlsDLywt+fn548OABduzYgSpVqsDJyQk3b97EsGHD0KhRI62/+t9//0XNmjURHR0NGxubHN6FtyQlJaFatWqwtbXFpUsf/cpRLYIg4L///sO1a9dw/fp17V87Ozt4e3vD1tYWycnJ8PPzE16/fk1jY+PQV69e7VUqletI3tC3/QYMPt0ii0gkUgDoK5FIepiYmFQyMjIy8/HxQbNmzUT16tWDj48PzM3NMzz266+/xrJly7Tvb968CU9PT4hEIsTFxYEkLC0tYWlpCZJ49OgRwsPD4e/vjwcPHsDOzg5//fUXjIyMcPz48WzZu2TJEkRGRmLatGmwsbFBVFRUjq9Zs8rt/v37uHLlCurWrYtly5bB3d09x219KCQmJuLSpUs4d+4cLly4gMuXL8POzg7VqlWDi4sLjI2NERQUxOvXryMhIUEJwD8kJGSPIAibSd7Xt/0G0mMQ3WKCSCQyA9DJzMysk0QiaaZUKmVWVlZGdevWRePGjUXVqlWDt7c3TE1NM5wlcerUKVSuXBlOTk75bhtJODk5ISIiAgDQoUMHHDhwIMtj+vbti9q1a6NPnz5wcnLCb7/9hgkTJsDX1xdt2rSBj48PSpUqle+2FnU0PdmJEyciMjIS165dQ8WKFVGhQgXY29sjMjKS//77L8LCwgQTE5Oo6OjoS7GxsfsB/EkyUt/2G3g/BtEtxohEIkcA3a2srNrb2dnVUqlUTqampkbe3t6sU6eOeMeOHbh3716646pUqQKFQgF7e3uULl0anp6eqFixIqpUqQILC4tc20MScrkcoaGhGDp0KHx9fVG7dm04OTlBqVRi8+bNiI+PR+PGjeHj44M2bdrg+vXrWLRoEbZv347Tp0+jVatWKF++PCZPnqzX6VYFiSAICAwMxK1bt3Dr1i3cvXsXAQEBCAsLw4sXL7TuoEaNGvHZs2eIj48XTExMouPi4m5HRkb+BWA7yaf6vg4DucMguh8Yb3rErUUiURu5XN7MyMjIXalUmltYWIisra2Rmpoq6tu3LyQSCUxNTREYGIgnT55g8+bN2jbWrFmDTp06QSaT5Wpxx4MHD3D48GEcPHgQt27dgiAIcHNzw927d3XqSaVSxMfHw9zcHNu2bcOoUaPg7+8PlUqFn3/+GRMmTMjr7dA7SqUSDx48wPXr13H58mVcvXoVDx48gJWVFUqWLAk7OzukpqbyxYsXiImJoVgsThWLxS9DQkLOpqamHgdw0NCD/bAwiO5HwptecUuRSNRQIpHUNzMzK5OammplYmIikslkEIvFotu3b4uSkpIgFotha2sLlUoFb29veHp6olq1avDy8oKHhwdkMhlsbGyyLchhYWH4999/cfr0ady9exeBgYEIDg6GqakpQkNDIZfLkZSUhKioKKxevRqNGzeGl5dXsVvNp1Qq8fDhQzx+/Bjnzp3DmTNncPfuXdjZ2Wl6+4yNjYVSqRRMTEySVCrVs7CwsEtKpfI8gBM0zI39KDCI7kfOmyhqFQDUE4lE1ZycnKpbWFh4pKam2pM0jo+PF2tWMaWkpCAhISHTtlq0aIEff/wRTZs2fe95SeLBgwdYtWoVRowYUWyitpFETEwMgoOD8eLFCwQHB+Pp06dYvHixdtWbsbExjYyMVGZmZrFKpTIwPDz8hkqlugngEoAb/MjS0xjQxSC6BrJEJBKZAKgIoAoAL1NT03J2dnZeRkZGitTUVOvk5GQTTVWxWAxLS0s0adKEMplMJJfLIZPJ4OjoCAcHBzg6OqJEiRIwNzeHmZmZthRmj5akdvVacnIykpOTkZiYiIiICLx+/RqvX79GWFgYQkJC8OrVK4aEhDAsLAxRUVGi1NRUpqamUiwWq4yMjOJUKlVoTEzMk7i4uEcAHgG4A+AWDaENDWSBQXQN5AsikcgIgCsADwClALgBkFtYWLhaW1u7mJqaOolEImuSJiSNSYoFQRALgiAWi8UQiUQQi8UikUiEN0UkFothampKMzMzkZmZGS0sLGBubg5zc3OYmpqKlEolEhMTmZSUpBXRpKQkpKSkiARBAEm++avzWiQSUSwWCyKRSPWmKEnGKZXKiLi4uBfx8fHPSb4EEATgOQB/AAGGHqqB/MAgugaKLG9cH1YAbABYv/lr9aaUAJAEIO5NiX3zNwpAHEmVHkw2YOC9GETXgAEDBgqRjzvhlwEDBgwUMgbRNWDAgIFCxCC6BgwYMFCIGETXgAEDBgoRg+gaMGDAQCHyf0IfzuBjf1XLAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {
@@ -287,19 +287,11 @@
     "\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "amber-telescope",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/examples/example-viz-2.ipynb b/examples/example-viz-2.ipynb
index 587cf16..d779811 100644
--- a/examples/example-viz-2.ipynb
+++ b/examples/example-viz-2.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5af6d7ab-3c43-4800-a66a-cbd36d5b9a4e",
+   "metadata": {},
+   "source": [
+    "## Example 2"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -23,46 +31,7 @@
    "execution_count": 2,
    "id": "indonesian-duncan",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hull0:  204\n",
-      "hull1:  256\n",
-      "intersected:  44\n",
-      "graphics-0\n",
-      "graphics-1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-2-89011555e987>:83: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
-      "  ax = plt.axes(projection=proj,transform=transf)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "graphics-2\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def shiftarg_lon(lon):\n",
     "    \"If lon is outside +/-180, then correct back.\"\n",
@@ -120,19 +89,11 @@
       "graphics-2\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-3-3233bb6fc733>:46: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
-      "  ax = plt.axes(projection=proj,transform=transf)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {
@@ -210,7 +171,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/examples/example-viz-2a.ipynb b/examples/example-viz-2a.ipynb
index 37efbbb..b4fea4b 100644
--- a/examples/example-viz-2a.ipynb
+++ b/examples/example-viz-2a.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f28e79dc-0b72-48e6-a1cb-fefaae8125c3",
+   "metadata": {},
+   "source": [
+    "# Example vis 2a"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -99,19 +107,11 @@
       "graphics-0\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-3-bb1f750dd606>:24: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
-      "  ax = plt.axes(projection=proj,transform=transf)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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Xr144dOhQHibXGYmIiICXlxfi4uJw9uxZREREoEaNGqhZsybU1NRQq1YtmJmZ5bv9r+Hg4AAfHx/UqVMHPXr0gI+PD3bs2AFFRUXBMR9I88FVU1ODuro6UlJSEBgYCGtra/Ts2RMpKSk4ffo0tm/fjj179uCvv/7Chw8fcOrUKZw8eRKWlpaQyWR48eKF4AKWmJiIvn37Yt26dRgxYgT279+P3377DStXrkRcXBzKli2b4bVr/vz50NLSQt26dVGjRg08fPgQFy9exJIlS2BpaQkFBQVMmTIFjRs3RsWKFQW/ytyQ7j8MpPmIzp8/X7BJFwbLly/HtGnTYGdnh3fv3uGXX37B+vXrC62/giIsLAwHDx4ESSQnJ+Pz58+wtLREo0aN4OzsXCALV3K5XPje586di4oVK6Jfv34YNWoUFi1alKP/6549ezB9+nS0atUKu3fvFhZPAaBs2bJITEwEACxatAjly5cHScyfPx+qqqp48+bNN8teFNy/fx9169bFyJEjYW9vj3nz5kFRUREGBgYICAiAvr4+9PT04OPjAyDtd5tuxouKihJs0xEREYiMjMTLly+xbds2NGrUCO3bt4eZmRni4+MRHx+PxMREaGtrw9LSEs2bN/9mM8i///6Lrl27IjU1NX82XSMjI9rZ2aFfv3745Zdf8tR5YGAgZs2ahatXryIyMhIuLi7Q0NBAixYtYGlpiQsXLuDt27d48uQJwsPDoaenh6SkJNja2sLW1hba2towNjZGamqqsA3Vzs4ONjY2MDc3FxYQSOLTp09QU1PD+/fv4eHhgTdv3sDa2hrbtm1DvXr1MHnyZFy4cAHq6uq4fv06LC0t0blzZzRp0gRaWlpZyv/XX39lWjAA0hZiJkyYgJMnT0IqlaJ169ZYs2YNdu3ahYcPHyI6OhqGhoaCF0OLFi0QEhKCS5cuoVKlSgAg2M+kUimMjY1Rrlw5VK9eHUpKSvDz80NgYCAaNmyIZs2aoWzZsvDx8UFqaipiYmKwbt06PH/+HH5+fujVqxc2bdqUycc3K2QyGapWrQp/f38kJSUBALZs2YJRo0bl6XvNDffu3UO9evUyHHNwcMDr168LvK9vITExEadPn8b9+/fx8uVLKCoq4v79+2jfvj10dXWhqqoKc3NzvH//Hhs3bgSQ5u/bu3dvmJiYfFPfISEhePz4MVatWoWgoCC8ePECALB582aMHj0aT58+hZ+fH8zNzVGuXDmYmppCQUEBtWvXRosWLbB06VKhrfXr12PSpEmQyWT4+++/IZFIMGDAgALdaFLUhISE4M8//4SXlxemT58OW1tbREZGwsbGBuHh4QgKCkKVKlWQmJiYq63jUVFRuHjxIs6fP4+YmBhoaGhAU1MT6urqiImJwaNHj1C9enVs27YNAQEBSE5OFr5/ZWVlJCUlwcDAAPfv38ehQ4dgZGQEExMTlC1bFikpKdDR0UGVKlVw9+5dDBo0CADyp3TNzMz4559/ol27drm6UE+ePMG5c+cQHx+Pv//+G61bt8bkyZNhY2OT46qhVCoVtpK+f/8efn5+iIuLQ1BQENTU1JCSkgJvb2+8f/8eHz58QGpqKlq3bg1zc3McPXoUycnJkMlkMDU1RatWreDg4ABPT080bdoUkZGRWLNmDQICAoSb1tDQEHfv3oWenh4cHR1x//59wZ1JS0sLQ4cORefOnSGRSBATEwMfHx/cunULQUFB0NXVRb169fD69WucOnUKADBs2DDs3Lkzw5iMjY1x4sQJbNy4UXhLaN++PZydneHm5oatW7fCw8MDGhoaiImJQXx8PJycnFC1alUYGhri+PHj+PDhA06cOIEWLVpkejj89ddfGDBgAAYMGIC9e/fm6vsZPXo0Dh48iLi4OMjlctSsWTNDzIWCICYmBqdPn8bIkSMF5V67dm1cvny50DdD5JawsDBs2rQJW7ZsgbOzM1xdXVG1alUAaRskbGxsMtVZtmwZFixYgKZNm+L27duwsrKCiYkJzMzM0LRpU7Rv3x5GRkZCealUis+fP8Pc3FyYiX6JVCqFt7c3zp8/jxYtWqBq1aqQSCTw9vZGYGAgBgwYAACoWbMmPn78CCUlJTRv3hweHh4ICgrCiBEjsHr1amhoaODgwYPo27cvgOwnCyI54+XlhT59+iAgIABWVlYoW7YskpOTERgYCKlUChUVFTg5OQmLc1OnTkVoaCgSExOhpqaGyMhIvHjxAsHBwXB1dYW7u3v+FtI0NDSoqKhIIyMjLl68mB8/fvyaHYMAaGxszEuXLlEulxe4rYQkP378yB07dnD+/Pl89OhRrvqRy+UZ7EGpqal8/fo1T506xRUrVrBnz56C/KamprS0tKStrS3t7e2F4w0aNKCpqSnNzc1paWnJWrVqcd++fVy+fLmwkKasrEwA1NPT4+3bt0mS0dHR1NDQ4MaNG6mkpMSjR4/S2tqap06dIpnmiA6Ay5cvF+TbtWsXlZSUqKmpSQ0NDXbr1o1RUVHC+RYtWgg24CFDhnD79u0Zxpcd27dvJwC6urp+88Lau3fvOHr0aDZu3Jg1atSgrq4u1dXV2axZM65atYqvXr36pvYLmvDwcI4cOZK6urocNmxYvuWLj4/no0eP6O7uzm3btrFbt27U0dFh/fr1uWPHDspkMp48eTLDAs/169d5+/ZtLly4kK1ataK2traw6IT/35Chq6vLevXqsWnTpixTpgxXrVpFMu3e/fDhAzdv3ix87wB4+fJlkmRQUBBNTEwIgC4uLgV2vX5EsltwS01N5b59+9itWzdu3bo12/oxMTH8999/v20hTSaT8erVqxwxYoTwZbdv3559+/bloUOHKJVKhQ5jY2O5d+9eodzSpUuZnJxcENei0JFIJBwyZAg7d+7MAQMGcPTo0Zw6dSp//fVXKioqcsOGDSRJqVTK8+fP89SpU5w1axZ79erFX375hePHj+etW7d47do1AuCtW7cy9SGXyzl8+HCqq6tzypQpfPfuHT9//syDBw8SANevXy+UHTlypHAdPTw8CIDr1q3L0F50dDSPHz/Obdu2sVmzZqxRowZ79epFDw+PbMcIgGvWrPmmaxUbG8sdO3bQyMiIs2fPpoeHBx88eMCwsLBMD8CUlBT6+/szLi6OZNpNLZfLKZFIeOvWLd68ebNI7hFvb2/a2Nhw/Pjx37yYuGnTJi5btoyXLl1iUFAQ5XI5k5OTeerUKWpra3Pz5s08deoUAbBp06YEwHLlytHY2Ji//fYbT506xfDwcJLks2fPePPmzQy/I5LCwlFWREVF8fXr1yTT7oGuXbsK98qvv/76TWMTKRjyrXQVFBRYtmxZnj59mnfv3uWKFSsIgIcPH2aPHj2ELzomJobNmzenubk5dXR0MrhF9e3bt1gGXZJ5+fIlNTU1WaZMGRobG7N169bct2+foHxkMhlfvHjBf/75hzdv3iQADhs2LMMP0dfXV5gh9e3bl35+foKHwv79+7Ptu1mzZhwxYkSmH3lO3Lp1i2vWrOHgwYPZuHFjamtrs127dvTy8sqx3pcuUWXKlGG3bt1oaGhIY2Nj6uvrs2bNmsJqe2G9FZHk33//TUNDQx48eDDbMi9fvuS4cePYvHlzjho1SlBq/0Uul7NcuXIZXPsMDAzo6OgoHFNUVBRcCY8ePcpZs2ZxwYIFuXKDyisbN24kAFarVo179uwp8PZF8ke+lS4A2tjYsG3btjQ2Nmbz5s25fft2zps3j+XKlWOfPn144MABxsbGUk9PjwC4bNky7tmzh1OnTuXYsWMJgP/880+xDLwkcu/ePZqZmbFMmTLU19enlpYWO3TowO3btwuvNh07dswUa+HOnTsZ2omNjWXz5s1pYGBAAFRVVaWRkRH/+uuvHBWYr68va9euzZUrV+ZK3nnz5tHGxoZjxozhli1beOXKlQxmjpxwd3cX5G/VqhW3b9/Od+/e0d/fn58+fWJKSgr/+OMPAuCZM2dy1WZeiImJ4ahRo2hnZ5ej211oaCgBcN68eRwzZkyuJguzZ8/moEGDhO+qUqVKBMBOnToxJSWFcrmcsbGxBTyirJFIJIX60BLJHatXr+bkyZPT47N8u59uREQE9+/fzy5dutDR0ZELFiyguro6+/Xrx5MnT1Imk/Hy5cucOHEiW7ZsyYYNGwo/uMGDBxft6EsYYWFhbNKkCVVUVGhmZibMlI4cOcI2bdpkUK7Hjx/nhAkTaGlpSWdnZ9auXZuWlpbU1dWlsrKyYFfv378/u3btyn///Zddu3Zl7969hVf4r3HmzBk6ODjw06dPOZZ7//49LS0tef/+/XyNWy6Xc+3atcLYevXqxcmTJ3PatGl0c3OjpqYmnZycuGfPngJ1Xv/48SOXLVtGY2NjDho0iNHR0VmWS0pK4s6dO2llZUUDAwPWqFGDNWrU4MGDB5mUlJRjH48ePaKzs7Ngbtu9ezd//fVXPnjwoMDGIVK6SDfzLFiwoHA2R3z5Y8IXTvcXLlygrq4ura2t2bZtW86YMYN+fn6FONSSS2JiIqdMmUIVFRW2bdtW+CFPnDiRHTp0oLW1teDcrqOjw06dOnH58uV89uwZZ86cydWrV/PMmTPcv38/jxw5QnV1daqrq7N79+60trbOsAjj5OSUJ9mWLl1KY2PjbCNErVixgvr6+hkW9/JLQEAAr1+/zv3793Pp0qWcN28ez507l60yzC/Hjx9n7dq1qa+v/9XNGPfu3aOJiQnbtWvHPXv20MDAgAcOHMjzjDEkJIQxMTHfKrpICSI5OZnLli3jnDlzclU+Pj6e586d45w5c2hlZUVtbe3C3ZEml8uFG9XLy4sAeOHChTwNMiYmhidOnBDsjN/Dq1JcXBwbNGjAbt260cfHJ1vPgk6dOtHBwYGnTp0SdhSNGDGCderU4bhx49i2bVt26dKF9erVo42NDVevXs158+axf//+wo6v/v378+3bt3mW8datWzQ0NKSnp2emc7/99hsnTJiQ5zaLi6dPn1JfX5/nzp2jRCL5ank3Nzfu3LmTJBkcHExtbe1cm01Evg/8/Pw4adIkduzYkS4uLrx27Rq9vb2F0ADOzs7Z1g0NDeWcOXPo4uJCTU1NIYTA+PHjC2+m+19evHjBbt26UVdXlwMHDmRgYCA/fPjw1XqnT58WZmzv3r1jv379CIC1atXi3LlzeeHCBXp4ePDRo0e5+jGVFIYMGcKBAwfm6bU5KSmJjx8/5pUrV1izZs1MDx93d3e6ublRX1+ff//9t3A8Li6OW7ZsyZcXwLFjx4RYs2pqapw4cSIvX75Mc3Pzry6UlSQ+fPjAcuXK5bp8t27d2KpVKwYEBFAikVBJSYlz587lmzdveObMGc6dO5eDBg1inz59+Oeff34XEwGRNORyObdu3UpDQ0NOmzaNJ06c4M6dO6muri7oIjU1Nd66dYv37t3j06dPuXXrVvbs2ZN//PEHf/75Z+ro6HDEiBG8detWlma9IlG66S4yANijR4/0KTZXrlzJCxcuMDIyklKplIGBgSTTFMyXQV46d+5MLS0twdZ5+fJlTps2ja6urmzSpAkrV67MsmXLsn79+jx48GCeVt+LAwMDA967dy9fP9abN29SRUUlW9emiRMncurUqUxNTWViYiLr1atHKysr9urVK1e+ulkhl8sZFBTEsWPHsmnTppw/f36+2ikupFIp1dXVBZOFXC7nX3/9xdmzZ2f5sP706RONjY0Fn8unT5/S1dU10wJm+qeoFsVE8sfz58+5Z88eLl++nJMmTeKkSZM4b948Ll26lK6urrS1teWAAQP4+vVrwf31yze8qKgo1qhRg8bGxly7di0tLS2ppKREAwMD6unpsW/fvty+fTtbtGjBUaNGMSwsLEd5clK6BRraMb2t9LB9c+bMwcKFCzOVk8lk8PDwQIsWLTB58mSULVsW5cuXR6dOnXJMORMfH49r165h0aJFiIyMRMuWLaGtrQ0NDQ0MGDAAlpaWuZa1sOncuTNu3bqFSZMmCUkqc0uDBg2gpKQEd3f3LFPFPHr0CH369IGKigri4uLQtGlTbN68GV26dIGenh7+/vvvEhPM+VuRSqUYMmQITp48ifj4eDx+/BhOTk5Zlu3atSvi4uLQqFEjnD9/HlKpFGpqamjVqpUQVhJIS4DZqlUr9O/fHxs3bhS2y7579w4VKlRArVq1sHXrViGVkpubW4kJCC+SkaioKMyaNQvHjx9Hy5YtYWpqCmNjYygqKgrZsJs0aQIg7f6wt7eHiYkJ4uLicPHiRYSEhODhw4eYO3cuOnTogKlTpwoxWlavXg0lJaV8hSEtttCOcrmcKSkpTExM5M6dO2lubk43NzeamprS0NBQmEUsW7Ysz+16enpyzZo1XLx4Mfv168cGDRp8NRxeUePj40MDAwMOHz6co0eP5v79+3MlY3R0NLt3786BAwdmW0Yul/PYsWMZXKGSkpLYokULjhgxogCkLxns2bOHDRo0YOvWrYX7JTt/17i4OO7atYtTpkxhgwYNhB1fBgYGGRZz582bx6lTpwp/Jycn86effqKSkhKNjIzyvCgpUvTI5XLu2rWLJiYmHDNmDCMjI/PcxoULFwiA3bt35/nz50mmLfrq6uqybt263yQfiiu0o4+PDzds2CCETATAmjVr8t9//+WJEyfYsGFDrl+//pt3JKWmpvKXX36hmZlZkebfyg1nzpzhli1buHr1anbu3Jk6Ojr87bffeP369RxNJP/++y8rVKiQ5/4uXrwobDv9Hvjjjz9Yo0YNhoeHc8aMGcJmnJw4duwYHR0dhd1g9evXZ926dfny5UsOGDCAJiYmGdzgzp07xzp16vDVq1f89ddfeePGjcIelsg38Pz5czZu3Ji1a9fOt4tefHw8dXV16e7unuF4bGws+/fvX7qUrkwm4759+2hmZpbBJla1alXu2rXrmwbyNYYOHcoqVaqU6B/N58+f+dtvv9HJyYmmpqZ0dnamm5sbjx49SpL8559/OGjQIBobG/PixYt5bj85OZlbt26ltbU1mzVrxt9//z3b3VUlGR8fHw4aNEiIiWFvb88VK1bQ1taWI0aMyPFBfebMGTo5ObFixYrCfaisrEwNDQ1Onz6dhw8f5pkzZ3jkyBG+ePGCffv2zXGlWqRkIJVK2a5dOwLgpk2bvmld59y5c2zUqFGGY3K5nOPGjaO2tnaWHj15oUiV7suXLwVFO3ToUD58+LBIVn6nT5/O+vXrc+jQoTQxMSkVK+/v3r2jl5cX+/bty9mzZzMxMZFAWpaJb52xx8fH88SJE5w+fTrNzMx49+7dApK68FmwYAENDAy4ePFi9u/fnzVq1KCDgwMVFBTo5eUlBPDOKs6Dt7e3EP8jPYC3mpoagbRMCS1atKCmpiabN28ubOAZPnx4qbhffnQiIiKooqJCFRUV9u7dm02bNqWzszOPHj3KsWPHUkFBIddtvX37lubm5hnuoQ4dOlBTU5OLFi36ZlmLzbxQlKTvBpk5cyZ37txJExMTvnz5srjFyhWnTp1irVq1KJVKefHiRdauXbtA21+5ciV1dXULZe9/YdC2bVshuE/Pnj05dOhQAqCSkhIfPXpEmUxGFxcX9u3bVwgstHXrVurq6tLOzo5NmjRhv379GBcXx0ePHvH69esMDAzk0qVLefjw4Vzv3BMpmYSHh3PhwoXcsWMHL1y4kMF8mdvsJHK5nBYWFnz9+jVv3brF5s2b09DQ8Ks7EXPLD6F0pVKpsHAikUg4a9Yswfe3pCOTyejq6sqff/5ZCPtYUMTHx/PQoUPU1dXNMRBOSSEuLo6mpqZs3bo1GzRoQHt7e8HE8OWmm4CAAALgoEGD+OTJExoYGJSah6xIwdKtWzfq6+uzc+fOgpkuNwwbNozTp09n3bp1OX/+fIaEhBSYTD+E0k0nISGBZNpuExsbG7q4uHDRokUl3s8yNjaWv/zyC62trfMUICghIYGvXr3i06dP6ePjw9jYWEokEr5+/ZqdOnWiurq6EOO3NCjdlJQUbtmyhbNmzeLZs2eZkpKSIXnoypUrOX78eA4aNEjwTDA1NS309QKRksmHDx+EhJfNmzfn2LFjc103PYLfxIkTcwylmR9+KKWbzpe2ZQDU0NDg6tWrC/ziFhcSiYRnz56lvr6+EH8h/RVcUVGRZmZmnDt3Llu1aiVcg5YtW/LQoUOl6hp8+PBBkL9evXp0c3Nj06ZN6erqSm1tbe7evVvcLfYDc/XqVTZo0IDt27fnjBkzqK6unqe328La+v1DKl2SDAwM5Ny5czMoX0tLyxK/m+1rBAcHZxpTmTJlhB19CxcuJEm2atWK9vb29PX15fr169msWTMCYIcOHYp5BHnDz8+PLVu2pIODgzDmFStW8P3798UtmkgxM23aNE6dOlXI2q2vr18idlP+sEo3HX9//wypUf4bm7Y08ezZM2Ec69evZ1RUFP/66y9WrVo1w1M7KCiIAGhoaMgePXqwbt26QhhJBQUFwQxTGoiIiCAAGhkZcdy4cfkK7lOYvHv3jqdPn+bnz59zVV4ikXx1G6nI1/nrr79oamrKN2/eCL+JhISEEvHm88MrXTIte0Dbtm3ZvXv3UhtqUi6XCzfXl2Pw8fGhqqqqEChoyJAhBMBz587x4MGDQjqg9E+5cuVKVfAgMi0S3fHjx2lhYZFjjqrC4t69e3R2duaAAQN45coVhoaGsl+/fhlm33/99VeObXz69IlDhgyhiYkJNTU1CYDbtm0rohF8Xzx69IhGRkZ88eIFSQrfwfDhw4tZsjREpcu0gCaVKlUqbjG+mS9NI1FRUVy/fj3Hjx+fZZCWkydPZqr/4sULBgUFFaHEBcuLFy+opaWVpzrPnj2jubk5gf8lc8wL165do6mpKRcsWEBFRUUC4NSpUzNc65zyzslkMo4fP56ampqcOHEivb29OW3aNFapUoXTp0/Pszw/OtevX6exsTEPHDhAMm0y4uDgwA4dOhRKBpL8ICpdpsWO/datfSWFkJAQ7tq1S1Ak6R8jIyOam5tz6NChpWozRFbI5fIsTSDv37+nlZVVjnVDQ0M5adIkrly5kqGhoVy3bl2Ga5WXH+b79+8JgGvXriVJXrlyJcsHnJ2dXZbJSMm0WRkAhoSEMDY2VqizevVqvnnzJteyiJD79u2jkZERL126JBx79eqV4Mny22+/cejQoV/NXF7YiEqX5Lp16zho0KDiFuObOXXqFHV0dNi1a1cePnyYe/fupYKCAl1dXfMdSrKk4enpSUdHRyorK1NXV5dGRkasWbMmnZycaGdnl+F7lMvl3LBhAzt06MAuXbpwyJAh1NLS4sCBA1mnTh127NiRQUFBbNCgAX/66ScuX76cCgoK/P3333NlV+3fv3+mdFO+vr708vISrvWjR4/o5uZGZWVlLl26NJN7YmxsLB0cHHju3DnK5XIh5VVuHfl/ZHx9fTllyhSeO3eOo0aNoqWlpWBSIMnLly/T2dmZ3bp145s3b4SMKn/++WcxSi0qXZLkwIEDuXr16uIW45upX78+y5cvn0G5RkZGfhfKNp1169ZRT0+PUqmU4eHhDAoK4v379/nw4UN6eXllsEd//PiRAHj06FEeOXKEq1evZkBAACtWrCjMQMePH8+4uDjWrVuXXbt25ezZszlq1Cgh+FB2MYgjIiKopKSUIYLVq1evuGHDBi5YsIDr16/PsF3by8uLnTp1YqdOnTK1NXfuXDZs2JAJCQlMSEjghAkTCICzZs0qkhT0pQ2ZTMZJkyaxTJkyHDFiBOvWrcsZM2Zk2sBQs2ZNurm5CS6R9vb2XL58ea4XNQsLUemS3LRpU4kxsueV8+fPZ3qd/Z5/qLdu3WLVqlVzLJOQkMDffvuNDg4O7NevX4ZzYWFhNDMzo4GBAcPDw9mjRw9aWFhw+PDhHD58OAGwZ8+evHHjBgEIMyeZTMZXr15x3bp13LlzJx8+fChc7w4dOrBt27Y0MTHh0KFDOWvWLA4YMIA6Ojps27Ytp0yZwuHDh7NOnTps06ZNJnllMhkHDBjAVq1aCYo3ve0WLVoU3MUrJURFRXHv3r3Z7gLbvXs3bW1tc8ziTFJ4w+jevTs3bdpEf3//QpA274hKl+SsWbNyjE9bUomPjxd+nKqqqgwJCfmuZrVZ4eXl9dWwln369GGTJk149erVTNcj3a1OWVlZeIX39vbm6NGjhaSVXz7A0l9Fa9WqJRxLD5KT/jl+/DgPHDiQKW5DVFQUjx49ysWLF3Pz5s08evRotp4hqampQnvk/1bc87KL6nvg+PHjNDExEWanmzdv5rZt2xgREcG4uDiOGTOGBgYGX/UGIcnGjRtz3rx5NDAwKFGbfkSly7SNEl+mcynpyGSyDOmMfqSMsz169OCsWbOyPR8VFUUA2QaujomJEa7bf18zDxw4IJxbunQp//33X2pra9PHx4fVqlUjkBYkfdmyZQSQ7/RHWZHueeHr60uSwvbmH8Vn98aNG7S2tqapqSmPHDkifA+DBg2ilpYWf/75Zx4+fJgAsl2U/BK5XE4XFxfu3LmTKioqjI+PL4JR5I6clG5anpIfADMzM4wYMQItW7ZESkpKcYvzVfbt24cPHz4AAE6cOAFtbe1ilqjosLS0xKFDh7I8J5VKsW3bNpiZmWWbRkVbWxuXLl2Ch4cHTE1N8ebNG6iqqqJ69ero27cvAGDFihWYNm0a2rdvjyVLlmD48OGYOnUqAOD58+ewt7eHgYEB4uPjC2xcN2/ehKOjI7S0tAAAx44dg4mJCWJjYwusj5KMn58f/Pz88Ntvv+H9+/dwcHDA3bt3sXv3bmhpaWH//v2wtbXFzJkzMXHiREgkkizbkUgkSElJwbRp0xAfH4+DBw9izpw5Waa2KpFkp435nc10ybTZY7169bhx48biFuWr/PdV9EcifXHM0dGRLVq0oJ+fH5cvX865c+fyp59+opOTE2/cuMFjx45x9uzZHDZsGHv27Mlu3brR3Nyc1tbWdHR0FK5fuptR+t8LFizIYJJISkqisbGxcP7x48dMTU1l//792bp1a27YsIH37t3j3Llzef78eSYmJnL79u0cN24cr169mutxRUZGsmvXrjQ0NOTAgQOFBIlPnjwpjMtY4pDL5Tx48CD19fVZoUIF4XrL5XJeu3ZNCDgvlUrZvHlz7t69O1MbSUlJtLe3p6GhITt16sSgoCDa2Njw6dOnRT+gHEBRJaYsDTx9+hQtWrSAu7s7nJ2di1ucHFmyZAk+fPiAHTt2FLcoRYpMJsONGzcAAGPHjsWrV6+gqKiIevXqwczMDKqqqrhz5w5sbW3RuHFjmJmZQUdHBwkJCXB1dUV8fDykUin09PRgb2+P58+fo0qVKggICMDx48cxYsSITLOi27dvY+LEiShXrhzatm0LXV1dtG7dGps2bcKjR49w+fJldOnSBTdv3kRAQAA0NDQQHR2NGjVqwNvbO0/je/PmDW7duoXk5GTUqFEDDRs2zFfyw9LK2bNnMXr0aHTo0AG9evUSEkdu2rQJixYtQlBQEGbPng1FRUXMnz8fb968wdmzZ/H27Vt8/vwZMpkMs2bNQt26dfHx40dYWVlBLpeXqGtYbIkpSyqHDh1i+fLlefz4cSYmJha3OJmQyWRcvXo1bWxsimXLa0khOTmZlSpVYr9+/di2bVs2btyYtra2XLJkSa5yYwUGBlJHRydPM0l8sXh26tSpTOe/nDFPnz6d48ePz9OYfnQkEgk1NTWzvG7Hjh2jpqYmW7RoQS0tLV67do3bt2+npqYmDQ0NqaOjw6pVq/LMmTMcNWoU+/XrR1NTU/7yyy/FMJKcgbiQlhGZTMbFixezfv36VFdX54kTJ4pbJAEvLy/a2NiwSZMmvHLlynfvqZAdx44dY/Xq1dm3b998X4P0xRoPD488tREZGcnw8PAs6zx79ox//fUXw8PDaWFhka9txT86N27coJ6eHgMCAjIcDwgI4NixY7l3714+ffqU3bp1y+QqqaqqSkNDQy5btox79uzhq1evimkUOSMq3Rzw8PCgjo5OsTtTk+TGjRtpYGDAPXv2UCaTFbc4xcbu3btpZ2fHM2fOfNNDRy6Xc/PmzbSxsWGZMmXo6OjIqVOnZvBgSU5O5ujRo2lqaspq1apx9erVuboXdu/ezVatWuVbth+d6dOns3Pnztne57t27SIAdu7cWQgONGjQIB49erRUZAgRle5XGDNmDKdMmcL+/fvTzs6Ohw8fLnIZ9u/fz4oVKwruRD8yTk5O+cqEnB1yuZyRkZH08vJix44d+dNPP/Hs2bNMTEzk7t276ezszA8fPtDd3Z0tW7akhoYGq1SpwrZt23L16tX08PDgkCFD2Lp1a/bs2ZOXL1/+4U0/30pSUhJr166d5WIZmfY2Wq5cOQLghg0bvjlRa1GTk9L94RbSsiIwMBDVqlWDjY0N1NTU8OLFC4SEhEBNTe2b2o2NjUXZsmWhrKyMhIQEzJw5E5GRkVBXV4eysjKGDx+OgIAAtG3bFoMHD4abmxuGDRtWQKMqnZCElpYWAgICoK+vX+DtR0dHY/369bhy5QqeP38OMzMzuLm5YePGjUKZmJgYPH36FKGhoXB3d8e9e/fQvXt3WFlZYcWKFdDT00OVKlWwceNGqKio5KpfiUSCY8eO4cOHD2jUqBFcXV0LfGylifj4eGhpaWHPnj0YOHBglmWOHTuGe/fuYcGCBVBXVy9iCb8NcSEtFyxevFiwG1WsWJGzZ8/Od1vPnj2js7Mzy5YtSw0NDRobG7Nfv34EwC1btnDz5s2cOnUqVVRUWKFCBfbt25fr169nr169CnBEpZOjR4/SwMCAwcHBhd7Xhw8f+Pfff+dpJ5OWlhZ/+eUXrlq1iqtWrcp1PS8vLwJgs2bNWKZMmVI3cysMWrZsyQkTJhS3GIUCRPPC15HJZBw9enSGbaBbtmzJU2qflJQUDh8+nHp6ety9ezelUikjIiLo6enJOXPmcO3atRlslHK5nImJiTQ2NqanpycNDQ1LXFaEouLs2bPs27cvK1SoQE9Pz+IWJ1u+XNxRUVHJdb3k5GSamJjw+fPnnDBhAnv06JHtjrofgY8fP9LBweG7NdGISjeXVKlShQDo4OBAe3t7mpiYcNWqVblezNm5cyebNGmS563Gjo6OPH/+PPv378/Vq1dzz549XLlyZYl0ZysM3N3dWa5cOa5cuZIRERHFLU6OJCcnC0rXwMAgQ1r4r7F+/XqamprSy8tLSBu+fft2vnjxgnPmzOGsWbMKdNtxSUUul7N58+YcNWrUdxu4SVS6uSQ0NFSIQgWAv/76K1VVVbljx45c1V+9enW+IpmdPHmSDg4OXLlyJTt06CD0P2PGjDy3Vdpwd3envr5+gS6cFTa7du0SXPrMzc3zlFF23rx5VFFR4cyZM/n48WO2bt06w264NWvW8N27d1k+6OVyealPqkqSe/bsYa1atb7rB4yodPPIuXPnCIAWFhZctWoVdXV16e7u/tV6c+bM4ahRo/Lcn1wu5+DBg4Uf3c6dO9m6detij35fFLRp04Z79uwpbjHyRGpqKp2dnfnnn39yyJAhHDFiRK7r+vn5CQo2PDycKSkp7NOnDwHQxsaGLVu2pLm5OXV0dFinTh3WqlWLdnZ2QsZnU1PTUh38/M2bNzQyMuLRo0dpZ2eXp23UpQlR6eaRqKgo1qlThwBoa2vLy5cv09DQ8Kuh5tzc3HKlnLMiNTWVnp6eTE1NZevWrX+ImAurVq2itbV1iTcpZMWTJ08IgBcuXKCenh6XLl2a67p79uwhAGppadHMzIwdO3akp6cnDQwMOHXqVD5+/JhhYWG8desW7927Rx8fH75//57x8fHcsGEDq1evTi8vr0IcXcEjl8u5d+9eGhkZcfv27UKQ+SNHjhS3aIVCsStduVxe6mw3MpmMY8aMIQBeunSJL168oJmZGc+dO5dtnapVq9Lb2zvXfcTHx/OXX34RZj6WlpYcMWIE9+7dyyVLlhTEMEosCQkJVFdXzzYzs1xOlvT9IRUqVOD69ev58eNHmpqafjXg9pe8e/eOb968oY+Pj2Ay8Pb25qxZs2hkZJTtYmJqairXr19PAwMDbtmypSCGQZLcu3cvx40bV6ChT1NTU3ngwAHOmzePjRs3ppOTEx8+fCikTNfV1c0yD973QLEr3a1bt7JDhw7cuHFjqXo1Sk5O5vnz5wX72qVLl2hjY5Oti1GlSpV4/fr1LM+lpqbyxo0b3LVrF//880/Gx8ezfPnymbY5pn/SZ9jfgw0vK2bNmsWmTZtmex5I+7RqRQ4ZQs6ZQ27fTp49Sz55QkZEpCnm4uTJkye0sbHhsWPHOHz48AKLXte5c2f2798/xzJjx44VInKFh4czMDAwz/3s37+fo0aNYtmyZQmkJTadPHlyvmR+8uQJp0yZwl27dvHu3btcvnw5a9SoQQAcOXIkDxw4INzLMpmMqqqq30XOwuwodqWbkpLC9+/f087OjgDYtGnTUjfzJSmkWMluv/fq1avZs2fPTMdlMhkdHR1Zs2ZNwV/3xIkTfPDggRCMRS6X09fXl8uWLWOZMmWEVN8AuGPHju8uBkO7du04c+bMLM/JZKSKStrd2aULuWMHOXcuOXQo2bo1WbUqqatLqquTFSqQbm7kzz+TU6eSGzaQx4+n1c3D+la+mTVrFocMGcKtW7eycePGmZJS5gW5XM7FixfTxMTkq66DMTExdHNzY5UqVaiurs527drlqa+YmBgqKChw6dKl/Pvvv3n16lXeu3ePNjY2eQ4Gvnz5choZGXHGjBnCPTtu3DhevHgx20lDQkLCdzuhIEuA0v2St2/fCvas7NKalFSCgoIIgA8fPszyfEREBCtUqJApOpWPjw+NjY1Jps2WLSwscnQHe/DgQbYzYEtLS/bo0YM3b94suIEVAw8fPqSRkVGW544eJWvXJmvWTLtDs3vexMeTb96QV66Qe/eSixeTY8b8b5YMkMbGZMOG5ODB5NKlaQr52TOyoLzxNm/ezMGDB1MqlbJv376sVq1avhXvgwcPqKWllausCWTaZOb69eu8evUqq1Spkut+NmzYQFNTU06cODHDcblcziFDhtDY2DjXM+fjx4/Tzs6Onz9/5urVq4U0R6XN5lzQlCilS5J2dnbs2LFjqVO6ZFpYSENDQ86YMSNTlCQyzQVKT0+Pffv25cyZM9mhQwfq6upy5cqVlMvlBMD27dvn2Mc///zDrl27csOGDfT19WX16tUFpTt37lzh/ytXrmT79u05ffr0UjcTTkxMZMOGDTO5DcnlZI0a5OnTZHJy2h06ZEju242Pz6h0AwPJa9fSTBOTJ5MdO5KOjqSaGmlpSbZokaao16xJM134+pJ58WR69eoVDQ0N+erVK8rlcsGMllfevn1LPT29LJNafg0/Pz+amJh89R6Qy+VcsGABK1euzGfPnmVZRiaTsU6dOpw/f36u+p4+fbqQot7W1pZ169alnZ3dVx88//zzD0eMGFHsqdILixKndEs7gYGB7NWrF3V0dNizZ89MQWr8/Pz4+++/c+rUqTxy5AiDgoJIUlC6OS3GkRm3JPv4+Ai2sfTP4MGDOXbsWG7ZskVwbStJSflyg1wup7q6eqZEj//8Qzo5/W92O3582l2a2/UWI6O08tevp/2bndddair57h15/jy5fj05blya/djaOk0hpyvt3DzLtmzZQlNTU8bHx/Px48c0MDDg3bt3cyVvaGgo58+fTwD8/fffczfI/yCXy2lmZsb3799nWyY6Opo///wzq1Wr9tVZ7Js3b2hsbEy5XM6kpKQc762oqChaWVnx9OnTPHv2LOfMmcM3b958VWYrKyshu8f3iKh0C4m4uDjOnj2bRkZGrFatGtetW8ePHz/y/v37XL9+faYQdC9evCAA+vr68uHDh7xz5062M4Lo6OhMNq/ExERBgZd27t69S1tb2wzH5PI0s8KxYxmPAWSlSl9v09MzrWz6JjGALF8+77J5ePxP6eZ26aFz587ctm0bybSF40aNGuVYPjQ0VAjW7eLikmuTQk79Hzx4MMtzd+/epaurK1u1apXpIZcVb9++pZKSEq9cuUIFBYWvLurdvHmThoaGPH36dK5kTc/W7OXlRS0trVL3lpYbRKVbyKTb1vr160cdHR1aW1tz2LBhNDY25vr164Vyy5cvF1aJraysWLVqVRoZGWWZoeB7Ri6X8/fff+fPP/+c4bi7O1m5cmZXsX370u7UbLzLBNIVZTqbN6f9nZSUe9n8/NLqmJiQLi5kbk3n7u7uNDMz46VLl+jp6ZntDC46OpqHDx9mnTp12LVr129aePuS7du3s1GjRpni054+fZpaWlqcN29ermM9+Pr60tbWlpaWlty0aRMNDAy+Wuf27ds0MjLKlcvkhg0bCIA1a9YsFfkK84OodIsQqVQqPLn9/PxoZ2fHcePG8fz581lue5w6dSpbt25d1GIWKz4+PgTAGzduCMfk8rQFr7//zrrOfxXqf5k2Le38f/dZAGTnzrmTKy4uo1lh0qS0xbnccunSJZYvX55TpkwhgAybPvz9/blq1SoaGxuzbdu23L59e4H6qEqlUtatWzdTLOgtW7Zw6NCh+W5XLpfTwMAgV7sjZ86cmaut6x8+fBBMZQ4ODly7du13F2ckJ6X7w6RgLyqUlJSEBHlWVlbw9PSEiooK5syZA2NjY5ibm6NVq1ZC+eTkZDRq1Ki4xC0W7O3tMXLkSHh4eAjHrl8HQkOBXr2yrpMe1vnatczn4uOBZcuAYcOA/4bgHToUOHUqTZXmhEwG/H9mdEgkgIIC0KQJ8P/5MXNFixYtcP/+fZw8eRIAsH//fsyePRtVq1ZFtWrV4OXlhePHj+PcuXMYPnw4ypYtm/vGv4KSkhJ69+6Na/+5QDY2NvDz88t3uwoKCmjfvj22b9+OPn36QEVFBZGRkVmWbdSoETw9Pb/aprW1tfAbefPmDSZMmICVK1dCLpfnW85SRXbamOJMt0CRy+V8//49R40aRQCC/atbt27csGHDV+t/+vSJzs7OrF+/PkePHl0qt85+yZo1a9i+fXvBbt2sGZlNEgEBBYWsZ7t6etkveqWmpp1btCjnttNnuF9e1vBwUls7b94MZMaZXNu2bXn79m3GxsZSLpczKCiIq1atyrX9My94e3vT3Nw8g5/t/Pnz8xUP5EuOHDnCTp06UUNDgwCop6fHAwcOZCp39uzZXHtfzJo1iwA4f/58GhoaEkhLzfO9ANG8ULK4e/cuAfDYsWOsVKkSz5w5k2U5uVzOZcuWCZlQ03/IAFi2bFkuWbKEV65c4efPn/n69WseOXKEU6dO5aJFi/jixYsiHlXeiIqKYo0aNejs7MwTJwJobU1+zYMwJCTtjl237n/Hbt9OO5ZTkDInp5xNE1Wrpp1//Trrc3l1OY2NjSUADhw4kNevX+emTZvYo0cP6unpCd9jYe3G6t+/P6dNmyb83a9fP+7du/eb2rxz5w4B0NHRkXK5nPfu3aO5uXkG++2dO3dYpUoVLv6KPUYmk1FbW5s3b97kgAEDMvmhfy+Rx0SlWwJxcnIiALZs2ZINGzZkTEwMSfLatWu8desW5XI5vby8aGZmxqtXrzI0NJTq6uoEwPj4eN64cYNOTk5s3Lgx9fT0qKysTCsrK+HmXbFiRTGP8OvI5XKOGTOG+vpuXLs2dwtKjRql3bWyLdtIV1d6IO1D1+w/kgZpZcKqZj73ytSVw7GN2SX1HTOGzEOCCIHPnz+zdu3adHFx4YgRI7hjxw76+/uTJAcPHsw//vgj743mgjdv3tDExIRSqVTYZPOtIUJPnz5NABnigSxbtozly5fn5MmTuW7dOpqbm3P//v1f9UQYNGgQgbQMzUePHs2w89La2vq72aUmKt0SyMuXL6mlpUUVFRU2bNiQSkpKrFq1KgFQSUmJbdq0oZmZGX/77TehTvPmzamqqprpxg4KCuKHDx9IkidOnGCdOnVYt27dUpGZYNKkZAL92aHDIObm9yaRpN21L4xdmaimQw+4UtLQNUelm51y/mjnyijo8LO9a7b9HTpEdur0bWPMKL+EJiYmhZqAtGbNmtyxYweBtHCRALhz5858t5fudaOsrJzhntq/fz8B0N7ePtvARV/SqVOnDMo7NTWVWlpaNDQ0ZK1atQiAy5Yty7ecJQlR6ZZg1q1bl+H1Kj2g9ZgxY/j3f5byk5OTcxUFysfHh127dqWxsTHv3LlTWKIXCGm21M8E9AmEZNhNBpAGBmnxFmbNSts4ERiYFmMhXYmOHJm7fp4+TWsv3XX63Lm0v33MXdOUcDZ8/kzq6xdcxLP9+/fTzc2tYBrLhhs3bhAAhw4dyiZNmnDp0qU0MzPL9w7Q2rVrC54GXwb0j42N5aJFi3Lt9la9enVu375d+Ds92hiQFuBp0qRJ7N69e75kLGmISreEI5VKhVnDvn37uHnzZrZq1eqb7Vt//vknVVRUMm3SKEkkJ6cFrRkwYCGNjU04ZcoeLl1Kdu1KWlgwkxJO/6QrXeAt9fTusm1bL+7ZI+fnz9n3BZDKyuTz52n/r1OH/5v55kCFCmnxGr6V1NRUOjo65jvmcl4ICQmhTCZjz549uXHjRiorK+c7GWZkZCQXLlxIANTR0eGaNWvyvKHhn3/+oYuLS4Z78csJR9u2bYX2vwdEpVsK0dbW5rt3776pDYlEwvXr11NTU5NaWlpUU1Ojm5sbt27dmsmJvjg5c4Z0cCDv339MGxsbDhkyRPDblMvlDAwM5P379xkcHMyoKDkBf3qgPD2gSsCCQB0ClgQmZamg3dzS/G2nTs14nGSulO6QIeSmTd8+zg0bNrBp06ZFugPr3LlzVFVVZZ8+ffLUb0REBJcuXcpdu3bxxYsXlMlkrF+/PqdPn84aNWpwzJgxuW7r/PnzNDIyYseOHWliYiLEAU5NTWVUVBRPnTolKN/vJai5qHRLIQWZGTg6OpqvXr1idHQ0T58+zUaNGrF169YlJrymXJ5mQli1Ku2VtU+fPnR0dOSMGTNYo0YNGhgYsGbNmtTS0iFgQkCHPubl6YHagvKMioqioaEhV65cxV69xtHQsCLd3GKynSmnf57quzLQwZU5TQL37CF79fq2MT5//pyGhobFEk86P4tTz58/JwA2btyYhoaGvHnzJidOnMgpU6YwNjaWlpaWfPTo0VfbWbFiBS0tLXnt2jWmpKQIyvW/vHv3jrdv386znCUVUemWMmQyGYGvB8bJLykpKWzYsGG+A1YXBq9epdlvg4PTZrfnzp3j77//zosXL1Imk/HixXRF+Y5RUXGkqysT6rgSINMnRx4eHuzbty/nz5/PevXqsVatWoId88aNjMo2PTDO/8wUGc/XrEkuWJBmVnj3jjQzy3/Q9NDQUNra2pa6XHAbN25k+fLluXz5choaGnLJkiUsV64cw8LC2KRJkxwzIYeHh3PTpk20sLDgp0+fSKb55tavX1/w4vieEZVuKUImk9HV1ZX169cvVJ/F58+f08zMrET5RU6cSA4blvn42rVpd2r16l8c/H+zgI1N2rn/KsT0yFuvX7/mtm1pZSws0sqlb7KQy//XTkpKWqCcUaPS4i5kNSseNy7vY/L19WWVKlU4a9asvFcuAezZs4cVK1bkyZMnaWZmxlq1arFRo0bs3Lkzx44dm6n8u3fv6O7uzjJlytDNzS3DzP6XX375qn+yXC4v9Rt/SFHplhqkUiknT55MAIX+6i+Xy+nk5JQpkHVxEhVFmpqSDx7871j//ml36ejR/yn8/8oyISHt/H9//x8/fqS6ujqHDEklkNZOOrGxaXX69uVXbbqxsaShYVr5rDZPZIdcLueGDRtoYGDAzZs3l9pIWnK5nCtWrKC1tTU9PT3Zo0cPqqurU1VVlQAyhH28cOECy5YtS1VVVe7OYnthenSxy5cv8/Tp01y2bBnd3d3p4eFBW1tbbt++nVeuXCEATpgwoQhHWfCISreUsGvXLmpoaHxzmL/cIJPJWL58eXbs2DFXPpZFxY4daYFv5PK02LZAWgDyTKRPP11d+cY8zUQgbewqKNFoJydeU1CnB1z5qYJrJr9df5u0OjItnWyVblQUWa8eOWJE3lzGZDIZBw4cSGdn51zFli0NNGjQgIcOHSKZtvtMW1s70wN78uTJXLJkSbY25DVr1hAAe/XqlcFNMv3z+PFjHjhwgABY/YvXmpK06JtbclK6YsCbEkJKSgrWrVuHbdu2oWHDhoXen4KCAnr37o2IiAhYW1ujSZMmWLJkyTcFRykIBg8GAgMBRUXAzy8t4Mzw4TnXsbdP+/eBV9q/cgKPvcNBGqBGDaBcucx1LC3T/r0R5wT07ZvpfHg40KwZULcusHVrmjy5Zdq0afjw4QNu3ryJihUr5r5iCWbp0qX45Zdf4O3tjfr166Nz5854+fJl2szt/ylXrhyeP38OJSWlTPWlUinOnj2L3bt34/79+wCABg0aCOd9fX3h5OSEJk2aAADatm2Lx48fo3379jAyMoK1tTXWrl1buIMsKrLTxhRnukWKv78/AfDp06dF2m9SUhK7d+/Of/75h6NHj6aBgQGXLVuWZSqiosLR8X8TWUNDslatNL/diRPT7LsnTpAPH6YFpEl/az94MK18eiwGYAL79s050MuHD2llly7NeDwwMC2u74wZeVs8k8vlXL16NY2MjL4Lu+R/2bJlC5s1a0a5XC54N/z555/CTDQ6Oprly5fP0rSwatUq1q1blyEhIdTU1OSwYcOEGe7KlSszlHV3d2dgYKAQ88HBwYHdunWjsrIyZTIZt2/fThcXF7Zo0YJHjx7NdSLNojTxQDQvlHzCwsKydacpSo4dO0YANDAwoJ2dHdu2bcvu3bsXeaJBuTztlT4wMC0jxOHD5IoVabbbn34iq1VLiwCmoZGmINu2zbjodfFiOC0sLLPNppBO795p5f8/9AX9/dM2Q3wtKllWbN68mUZGRoW6xbc4kUgkrF69uhB4/OLFi7SysuLUqVOFMmvXrmWNGjU4fvx4btmyhe7u7kxMTOTYsWO5atUqnjhxgnZ2dkKqnk5f7LH28PDgkiVLOGTIEAJg1apVheBQ6Z+uXbuyZs2avHz5Mo8cOcImTZqwbNmybNOmDY8cOZKtAu7evTsB8OzZs4V6jdIRlW4p4NmzZ9TT0/uqkihs5HI5k5OTKZPJ6O3tzTNnznDTpk00MzPjnDlzStyCUFQU+eRJWiLLdE+Gn35K22yhouJNJSVT1qhximPHpkUnO3+efPv2f+Ea09MBKSikHbe2JlevzrscEomEFhYWvHbtWoGOr6Th6+tLU1NT/vPPPyTJgwcPskuXLsL5iIgI7ty5k3369GGPHj1YrVo1WlhYEADv3LkjZAMH0mKMABByu02aNIkAhDgMAFi5cmX27dtX+NvAwCBTBL3ExET+9ddfQvqjUaNGMek/6UI+ffrEMmXKEACvXr1ayFdJVLqlgmnTpnFcfnySiojg4GDWrl2b9erV4/jx44WoaCWZ1FTyxIn71NEx4rx5nzhmDNmyJWlllZZ8slKltOzADRv+b4b8/5ul8szChQvZqlWrApW/pHLr1i1aWFhw3LhxXLJkSY7+3lKplEeOHOHdu3eFB7arq2uG2etPP/0klP/Sayc1NZXR0dGMi4vjsWPHuGTJEtavX5+6urocMWIEg4ODM/UXERHB7t27s1u3bpRIJLx9+zY3bNjAJk2aEAAVFRW5fv169uzZs1A9hESlWwrYvHkzlZWVMz2hSxISiYTnzp1js2bNuHbt2uIWJ9cMHjw4U0rxxMS0jQ8nTpDLlv1P6eaX6tWrF0lMhZJCTEwM3dzcqKSkRG1tbdaqVYsnT55kTEwM3d3d+fTp02y9DqZOncqaNWsyICCALVu2zFWOwNDQUALg3r17GRwczMGDB7NXNtsEo6Oj2bhxYwKggoJCBgXv7u7OyMhIAmC9evW+eat9dohKtxQQEhLCDh060NXVtcS9wv+Xw4cPZ5idlHRu3bpFY2PjfEfZyg3Dhw9ny5YtC639kohMJqOpqSkBsHfv3hmUm62tLZ2cnHj9+vVcRcb7GhEREVRVVRV2sz1+/JhA5qDnnz9/pqenJ319fQmkhZHcvXs3t27dmuHtbPHixYKsv//+O7ds2VKgv7uclK7oMlZCMDY2xqlTpxAbG4s///yzuMXJEalUChUVleIWI9c0bNgQZmZmuHnzZqH1Ua1aNZTLyjftO0ZRURGBgYH466+/QBL169eHlZUVrK2tUaNGDXh7e8PV1RW6urrYvn07fv/9dygoKGDKlClo2LAhGjZsCAUFBVy/fh2+vr5YsGABKlasiDt37mTqS19fHykpKbD8f1+/Dh06AEj7bo8cOQIA2LZtG6pWrYrBgwejffv26NevHw4fPoy+ffti5MiR0NbWFtqbMWMG5HI5du/eDalUitGjR8PJyQkLFiwo/AuXnTamONMtFh4/fkwzM7MSa2aQSCRs1qwZN2/eXNyi5AlbW1tezi49RAFw/fp12trafjeZD/JLujvZkSNH+Oeff/L3339nkyZNuHfv3gyLYfhiVvz7779n+Ds3oUhPnTrFrVu38vjx4yxfvjzLlStHQ0ND7tq1iyQ5ZMgQdujQgY0aNSIA3rt3L8f2tm/fTk1NTQIokA0tEM0LpYv27dtzU0HEEixgJBIJu3Xrxnbt2pWomA25Yfjw4RlcmwqDhg0bcuHChYXaR2lGLpczLi6OWlpaBMCjR49SIpEIngzZRS372oMsKSmJHh4eGXLBpaSkcNCgQUIyTQC8f/9+tm08ffqUQFo83wYNGnzzLjhR6ZYyHjx4QDMzs1w7fRcVc+bMYatWrUpMSMi88OjRI1aoUKFQ+zh79ixbtGiR7Xm5XM6QkJBClaG0EB4eLig2X19fIX7yf0nPLmFubs7Tp0/zyZMn/OeffzIpxerVqxNAJgUdFhbGS5cucenSpYyLi/uqXNu3b6eDg8M33+Oi0i2F9OrVq0RFpvLw8KChoWGpDcs3Y8YMdu3atVD7uHnzJuvVq5ft+YMHDxIAX716VahyfC8cOXKEampqBMCOHTuyYcOGwsLd4cOHhXIREREEQAsLCyE+RHEjKt1SyOfPnwkg3ylWCpL4+HiqqakV2W6egsbT05Ply5fP0q+zIHnx4gUdHR2zPPf06VOqqanRysqKo0blvD35R+Xx48dcunQpHzx4wODgYALgwYMHM8xq0xNuBgUFCcdu3brFatWqcevWrezYsWOJSMiak9IVvRdKKObm5mjUqBE2bdqU9nQsRqKiomBgYIB27doVqxz55eDBgxg5ciRMTEwKtR8dHR18/Pgx0/GkpCTMmzcPJiYm2LVrFx48eFCocpRGdu/ejZo1a+LGjRto27YtTE1N0b59e/Tu3RuKX0Qb2rJlCzZt2gRTU1PhmKKiIpSUlNCoUSOcPn0azs7OiIqKKjDZunTpgtu3bxdYezkq3djY2ALrSCTv/P333zh//jxcXV3x/PnzYpNDVVUVKSkpxdb/t3LmzBm0bdu20Ps5f/48atWqJfzt7++PNWvWoFatWlBQUMDz58+xbNkyPHjwAHK5vNDlKQ2kpqZi7dq1GD58OB4/foxz587hypUrWLx4MaZMmZKpfMuWLbFgwQKsWbNGuIYhISHQ1tZGlSpVEBcXBw0NDSGSWUFw6tQpjBs3LldlZTLZ1/vObgpMEiYmJsUxMxf5AqlUKgRS6devHz98+FDkMty5c4cuLi5F3m9BkG4aKcwNJz4+Pvz1119pZGTEx48fC8e7dOnCevXq8cyZM8ICT/PmzdmuXbtCk6U0kZqaKsRlyEsw/WfPnrFBgwZ0dnbm9evX2atXrww7JKdMmfLVDBV54eTJkwTA8PDwHMsFBQXRxcUl3VtCtOmWdmJiYjhu3DhaW1vT29u7SPtetmwZBw8eXKR9FhRyuZxlypT56g8mv2zfvp2GhoacPn16hnCYcrmcysrKGUI8yuVyGhoafrdRyPLKvXv3qKury6FDh+Y5FGZKSgp37NhBY2NjVqhQIcP3GxgYSFVV1QJ90H6tLblcLrim7dmzR1S63wtyuZyLFi0q8plS/fr1efHixSLtsyAZO3Ysu3fvXqBtSqVSjhs3jjY2NtkqUXt7+wxO+Q8fPqStrW2J3+ZdFCQkJNDJyYnLly//pnakUmkm9zGpVEozMzMuWbIkW1e0wmDLli2cM2cOk5OTxYW07wUFBQV07twZ79+/L7I+vb294ePjA2dn5yLrs6BZuXIlPDw8CvS6LVy4EPfu3YO3tzcqVKiQZZnq1avD29tb+PvAgQPo06cPFBQUCkyO0kJqaioePHiAP/74AzVr1oSZmRlq1qyZpd02LygpKWVYaEs/duXKFTx48ADVqlXDkydP8tzuq1ev4ObmhkePHuW6zqhRozB//nyoqanlXDA7bUxxplsi+fjxI83NzYusvzp16nDfvn1F1l9hMXXqVA4dOrTA2lu6dCl79uyZ7flr165RRUWFYWFhJP+Xk+7L7Lg/AvHx8XR3d6e1tTVtbW3Zr18//vvvv4Vm7vkv48aNY82aNfP8dpHuson/z46RVyCaF74fXr58SXt7+0LvJyUlhatXr6aenh4TEhIKvb/CJjo6mtbW1rxw4UKBtBcXF0cA2fr+DhgwIMOWYE9PT1aqVKlA+i4tpKSkCIrr9OnTxSKDTCZj2bJlWaVKlTz7ab969UoIDXngwIE81c1J6YrmhVLG27dvYZ+eibGAiY6OxqZNm+Ds7AwtLS0sWLAA9+7dQ9myZQulv6JER0cHffr0KTBXIk1NTQBAXFxcluctLCwQGRkp/H3u3Dl07NixQPouLYSEhAAA/Pz8hKhgRY2ioiKuX7+OFy9e4NChQ3mqW6lSJSQnJ+Po0aNo3bp1wclUYC2JFAk+Pj4FrnSlUinmzJkDKysrnD9/HmvWrEFcXByioqIKTcEXB/r6+ggLC/vmdhITE7Fjxw4A/1O+/6VNmza4fv06SEIikeD48eP46aefvrnv0sTly5fRrFkzWFlZFascLi4uuHfvHpYuXYrdu3fnqa6qqiq6d+8OfX39ApNHucBaEikSXr9+ncEBvyD45Zdf8OrVK7x+/RpmZmYF2nZJonbt2jh69Og3teHh4YEePXqgQYMGuHXrVoadUV9Sq1YtpKamwsXFBQEBAahTpw4aNWr0TX2XNszNzXH16lUEBgbC3Ny8WGWpU6cObty4gaZNm6JSpUqoX79+sckiKt1Shr+/P7p161Zg7UVGRmL//v34/PlzhiDP3yP169fHmzdvEBYWBiMjo3y18c8//6BKlSo4ffp0juXU1dXx+PFjXLp0CTY2NnBwcMhXf6WZP/74AwDw4MGDEmFaqVixIoYMGYKzZ88Wq9IVzQuljI8fP2Y7u8oPysrKkEgk0NLSKrA2Syqqqqpo1qwZ9u3bl+82FBQU0LJly1yVVVJSQps2bX5IhQtAsKE6OjoWsyT/Q0tLC58/fy5WGUSlW4rw9PSERCJB1apVC6zNffv2wdnZ+YfxHV22bBlWrVqFq1ev5rluUFAQ/v33XzRu3LgQJPv+MDQ0xJgxY7BkyZLiFkWgS5cuuHjxYrHKICrdUkRAQABq1qwJZeWCsQp5eHhg2bJl2LVrV4G0VxqoWLEiNm7ciAkTJkAmk+W6XmRkJOrXr4+ff/4ZTZo0KUQJvy8WLFiAw4cPIz4+vrhFAQA8f/4clSpVKlYZRKVbivj06RP09PQKpK3IyEiMHDkSmzdvLlGvf0VBly5doK+vjw0bNuSqvFwux9ixY9GuXTvMnTv3h3krKAgMDAxga2ub53CWJBEfH4+QkJACDW3q6ekJNze3AmsvP4hKtxQhlUoLZLHr5cuXaNCgAbp06VIiFjiKGgUFBezYsQPTp09HcnJyjmVTUlLQr18/BAYGYuXKlUUk4ffFr7/+inXr1uVYhiTu3r2LzZs3Y/jw4bC0tISxsTEcHBxQu3ZtTJ48GR8+fPhmWW7duoUaNWp8czvfgqh0SxEqKipISEj4pjYkEgmGDx+O4cOHY/ny5QUkWelDSUkpR4WbkJCAf//9Fw0aNEBqaiouXLjwXWwSKQ60tbWz3UQCpClCGxsbDBgwAF5eXqhSpQouXbqEhIQEREZGYtGiRQgPD4etrS0CAgKQkpKCjh07wtfXN09yjB07FgkJCcUfjD+7rWoUtwGXOJo3b85jx47luvybN29ob2/PI0eOMDIyknFxcezZsyc7dOhQ6rL5FjTpofjWrFnDq1evctKkSbS3t6eurq4Q47VevXrcv3+/GBXsG9HR0WG3bt1IkpGRkZw9ezYdHR1pbGzMypUrU11dnTt27MjxOsvlcs6dO5eOjo6sWbMmAWSIofs1bt++zXLlyhVYsteEhAReuHAhW5khxl4o/bx69YomJiZ5ylIaEBAg7H1XUlKisrIyu3fv/l3EUigIduzYwcqVK7NChQqcP3++kJvr5cuX/PTpU3GL991w+PBhWlpaslKlSjQ0NGTnzp157949BgQE0NvbO0O+s5yIj49njRo1hHsaAJOSkr5aTy6X86effuLmzZu/dSgCXl5eBMA6depkmeFZVLrfARMnTuT06dPzXO/JkyfU0dHhp0+fcpWCWkSkMEhMTOTTp0/57t27b27r5cuXnD59OjU1NVm7du1Madf/y4MHD2hlZVXgk41hw4YRABs1apQpgayodEs5np6eBEAfH588142JiaG2tjZv3bpVCJKJiBQfcrmclStXzhBgXy6XMyoqSvi/l5cXBw4cyOHDhxd4/zKZjOXKlSMAOjo60tPTk/v376dMJstR6YrbgEsBMTExqFWrVq6Czzx9+hTx8fGIioqCj48P9uzZg969e6Nhw4ZFIKmISNGhoKCAZcuWoUePHqhSpQoaNWqEy5cv49GjRzh06BD++OMPxMfHo0KFCli8eHGB96+oqIjx48fj2rVr0NDQQNeuXREYGIjOnTvnLDdz8IFzcXGhmC66+Hn9+jXq16+fq7TSZmZmCA4ORps2bWBlZYXOnTujZcuWUFJSKgJJRUSKnvj4eNy7dw/Xrl2DsrIyTExMsHv3bgwYMABjxowpNL/q169fw9HREWvXrkWTJk3g7OyM3r174+DBg1BQUHhI0iWreqLSLQUsXboUAQEB2LJlS5bnY2NjcerUKdSoUQONGjXCuXPnxK2qIj8kwcHB2LZtG+rWrYuUlBQkJyejV69eBd7P2bNncebMGUgkEpw+fRodOnSAuro6Nm3aBAUFhRyVrmheKAV4e3ujcuXKIJnlU/vQoUMYOXIkAKB3796oXLlyUYsoIlIiePfuHebNm4eaNWvCxMQE7u7u6NixI9TV1Qusjzdv3qB79+4Z/Lzv3LmDBw8e5GpWLSrdUsCRI0cAAFZWVhg0aFCm81+Gqdu3bx9UVFSKSjQRkRJFgwYN0LhxY4wZMwb+/v5wd3cvUNPa58+f0adPHyxatAjx8fH4559/sGTJEjRq1CjbgPb/RVS6JZwvzT92dnZZljl//jzq1q2LNWvWiApX5IdGQUEBs2bNQpcuXZCYmIgVK1ZAVVX1m9sNDAzExo0bsX37dgwZMgQPHjzAoUOH8Pnz5zwHaBe3AZdw3r9/D3V1dTg6OqJOnToZzkmlUuzYsQNbtmzB5MmTizUws4hISaFVq1bw8/NDdHT0N6d4B4C7d+/CxcUFsbGxuHfvHqKiopCYmIi///47X5lWxJluCeevv/5Cu3btcOzYsUzn5s2bBw8PD4wfP77YEv+JiJRE8psZJCvc3NzQrVs3LFiwABcuXMCpU6fw6tUrGBoa5qs9UemWYD59+oT58+cDSAvAoqGhkeG8srIyQkJCIJFICuQVSkREJDOLFi3C9evXUb58eVSuXBmnT5/Ot8IFRJexEo1MJkP58uXRsWNHbN26NdPKqEwmw8iRI3H+/Hl8+vRJjPP6AyGVSqGgoCD6X5dQcnIZE226JZhnz55BS0sL27Zty1KhLlq0CA8fPsShQ4dEhfuDoaKiAmVlZaxevbq4RRHJI6J5oQSjra2NpKSkTP65crkcEyZMwIEDB/DgwQNYW1sXn5AixYK9vT3evn0LXV3d4hZFJI+ISrcEY2VlBWVlZXh4eKBZs2bC8cDAQOzcuRP+/v5ZLhi8ffsWM2fORHJyMsLCwnDo0CFYWloWpegihYyPj09xiyCST0TzQglGSUkJDRs2xMaNG+Hn5wcgTaEOGzYMSUlJmDp1apb1li9fjiNHjuD06dO4e/duiUkKKCIiIi6klXiCg4MFX8DmzZvj9u3bUFdXR40aNSCRSHDp0qVMaWSSk5MRExMDJSUlGBgYiPZeEZEiRlxIK8WYmpoKmVSvXLmC5ORkHDt2DJcuXYKZmRk0NDTQtGlT3Lx5U6hTpkwZmJiYwNDQUFS4IiIlDNGmWwqoVasWJBIJ/v77b+zYsQNdunRBlSpVhFCP165d++aElSIiIkWDaF4ohSQlJeHu3btQU1ND/fr1oagovrCIiJQkxNCO3xnq6uoZvBlERERKD+IUSURERKQIEZWuiIiISBEiKl0RERGRIkRUuiIiIiJFiKh0v2MePnyIGTNmwN3dvbhFERER+X9E74XvCD8/P3h4eGDIkCEZjj98+BBt2rQpJqlERES+RJzpfgdERUVh0qRJcHBwEBTulClTsGnTJjx79kyc6ZZQZDIZ/P398ezZMwQGBkIulxe3SLkiICAA+/fvx+fPn3MsJ5PJEBERUWD9+vn5YdKkSYiOji6wNosDcaZbiomLi8OyZcuwefNmWFpaok2bNjh9+jSeP3+OKlWqFLd4IllAEleuXMEff/yB27dvQ1dXFzo6OggPD4eCggL+/vtvNG/evEjk8PX1xa+//opVq1ahcuXKX60TGRmJpUuXYvfu3WjYsCHGjx+Pzp07Y+zYsdDV1UVCQgICAgJw69YtHDlyBKGhoQCAhg0bonv37jA3N0fTpk2FWCGvXr3CoUOHkJqaCisrKyQmJiI2NhZGRkbQ0NDA69ev8enTJ0gkEiQlJcHb2xsfP35EUFAQZs6cCalUitTUVMTHx0Mmk0Eul0NZWRna2tqoUqVKgaZdL0hEpVuK6dq1K8qUKYPevXvj+PHj6Nq1K0aOHAlHR8fiFk0EaYrt+fPnuHjxIhITE+Hu7o47d+7A1NQUCxcuxOHDhzPEw929ezcGDRoEXV1dVKlSBb1794a1tTUuX74Mb29vVKtWDdbW1jAxMYGWlhacnZ2/GltDKpXi/v372Lx5Mzw9PaGqqoqwsDBERkZCU1MTTk5OaNCgAVRVVZGYmAhlZWWoqKggPj4e9vb2kEgkSExMREREBJSVldGvXz88fPgQVlZWiIqKwvr16/Hzzz8jJSUFZcuWhYWFBZycnHDy5EnY2NhAXV0du3btwt27d/H27Vv07NkT+vr6UFBQgEQiwYABA6ClpYUHDx6gTJky0NXVxdOnTxEbG4tKlSqhRYsWUFNTQ5kyZWBmZoaKFSti4sSJ6N69O1RVVaGqqgpNTU0oKSlBSUkJEokEoaGhePXqFZ49e4bKlSuXuB2b4jbgUgRJPHjwAHK5HC4uLnBycsKCBQswc+ZMVK9eHdOmTYOTk1Nxi/ldQxLbt2/HrVu30KlTJ7Rv3x5KSkpQVVVFbGwsbt26BXd3d7x8+RLPnj2DhoYGWrduDS0tLTRo0AA//fQTlJSUslWWUqkU3t7eePToEY4ePYqgoCC4uLigYcOGePPmDfz9/RESEoIPHz5g1KhRmDlzZpbtyOVyTJ06FTt27ICFhQUGDhyI9u3bQyqVwtjYGAYGBiAJFRUVyGQyhIWFoWzZssLsUVNTEy9evICmpibU1dVhYGCA1NRUGBgYfNP1S0lJQUhICORyOcqVKwcVFZVvai8rUlNT8euvv8Ld3R3R0dFwcnKCjY0NrKysYGNjg8jISAQHB6N3796oXr16oQSFymkbsKh0SxGrV6/G+vXrkZqaitq1ayM8PBwXL15ETEwM1q1bhwMHDmDAgAFYtGhRcYtabJDEtWvXhBlb3bp1AQBPnjyBv78/rK2tERoaik+fPiEpKQkqKiowMTGBqakpateunekHKJfLERQUBA8PDzx79gyPHz9GcHAwxowZg+3bt+P58+fQ0dFB586dce7cOdjZ2aFdu3ZwcnJClSpVUL58+QL/UcfExGD+/Pk4fvw4/P39hWM7d+5EaGgowsLC8PjxY+jp6WH//v0wNzcv0P5LE8HBwXj27Bk+fPgAPz8/+Pv7Q0VFBWZmZjhw4ABSU1NRrlw5WFpaYvTo0WjYsGGBmCVEpVvKSUhIwIsXLzBt2jT0798fderUET5XrlwRXp8iIiJQo0YNzJw5E6NHjy5mqfNHSkoKIiIiYGRkhNDQUJw9exZ169ZF9erVERkZicDAQEilUjg6OiI8PBwvX76En58ffH198fDhQ7x48QIGBgbQ0NBAZGQkrK2tIZFI8PnzZ1StWhXnzp2Di4sLKlWqBHV1dUgkEoSEhAgptWUyGeLi4iCXyyGVShEcHAxNTU00btwYLi4usLe3R7t27TJkZvb19cW5c+dQvXp1NG3atNCvUcOGDXHnzh1s374dw4cPx/PnzzFo0CDY2NjA2dkZRkZGsLOzQ6NGjQplJvm9QBL+/v4IDg7Gw4cPsXfvXrx//x5NmjSBlpYWVFVVoaysDGVlZRgZGcHe3h516tSBra3tVx+kotItJCZOnIiXL1/CwcEBWlpaKFeuHFJSUjBixIhM6dLzy5o1azBp0iTh73fv3kEqlWLixIl4/vw5xo8fj99++0047+Pjg9q1ayMqKqrE2bJygiSOHTuGMWPGCPa5wMBAtGrVCh4eHlBSUoKamhrKly8PmUwGX19f6OnpoVq1arCxsYGtrS1cXFxQuXJlWFhYAEh7zfz777+hoaGBjh07Qk1NLVO+uXRSU1Nx5coVYWFLSUkJysrKMDExKbDvsqCYMGEC1q1bhyNHjiAoKAgLFy7E9OnTMWHChFL1nZdE/Pz84OXlhYSEBEgkEshkMqSmpgoPZi8vL4SHh+Phw4c5Lj7+0FHGvL29UaZMGTg4OEBBQQHh4eFISUkRsjGQzHMa62fPnqF69eowMDBAREQELl68mOF8ZGQkRowYgffv3yMsLAx2dnaIjo7G9OnT0ahRI1SrVg0SiQStW7eGiYkJ1NTUAAAvX75EYmIirK2toaqqCm1tbVhbW8Pc3ByKior49OkT7OzsYGhoCC0tLSgqKiI5OTlD3xUrVkTFihVx4MAB/Pzzz99w5QqfT58+4fjx43j48CGuXbsGPT09nD59GvXr18fTp08RERGBJk2aAEjLhpGu/Eji/fv3sLGxyVHJqKioYNCgQRmOZTdDUVFRKTW+zF26dIG3tzdWrVoFS0tLXL16FdWqVStusb4LrK2ts030ShKzZ8/Gjh07oK+vn+8+SvVMNyoqCs+ePYNcLhdeJ01MTFC2bFnExcUhOjoaLVq0EMr36NEDt2/fRmBgoHDM2dkZbdq0gaWlJfz8/HD9+nX07t0bAwcORFJSEkxNTTP1Gx8fDy0tLdSoUQOOjo7o2bMnfHx8sGXLFoSEhEAmk8HAwAAVKlSAgYEB3r59CxUVFQwdOhTBwcHw9/eHTCbDhQsXEBcXB3V1ddja2uL9+/fQ0dFBfHw8JBIJqlevDnNzc7x+/RoBAQFwcHCAqakpTExMYGlpiYYNG6JZs2aCIpFIJPj06RNGjhyJatWqlej03GFhYTA2NoaxsTEWL16MRo0aCQ9GEZGSQlxcHK5evYpTp07h1q1b0NTUxMWLF7NMCPslpdK8cO/ePWEKHxsbi0ePHiEsLAzJyckICQnBixcvEB4ejmrVqkFZWRkJCQnQ19dHSEgIkpKSoKmpCV1dXURHRyMsLAyJiYmCgTwhIQFyuRx6enro0KEDDA0N8fHjR1hYWKB27dpYsGABnj17BjU1NZiZmcHCwgI1a9aEoqIiAgMDER0djQ8fPuDp06cAAAsLCwQEBIAkEhMToaKiAlVV1VyNkySioqLg4+ODihUrCk9QqVQKd3d3JCYmokKFCqhWrVqW9jmS+PjxIy5duoS5c+dCSUkJbdq0wR9//AFtbe0C+jYKhnTfUE9PT5w/fx6HDh3CnDlzMH/+/OIWTUQkA3K5HEePHsWsWbNgYWGBFi1aoFOnTrC3t8/Vb7tUKV2JRIK7d+/Czc0NnTt3hr+/P0xNTVGjRg2UL18eampqMDIygqOjIypUqFAoNiySSE1NhYKCAl69eoWAgAA8fvwYioqKMDU1hb6+PhITE4VZq4uLCwYMGFCgMqSmpiIxMRFSqVRIxaOsrIzk5GT8888/uH//Pl68eIG3b99CW1sbjRo1wvjx44XX8eIgLi4OPj4++PjxIwIDAxETE4OEhAQEBwfDx8cHT58+hY6ODurVqwc3Nzd07979qzMGEZHiYNasWfj333+xZMkStGvXLs/1S4TSDQsLg6mpKYYOHQp1dXVERkYiJiYGbdu2haKiIj58+IDHjx/j3r17sLCwgJaWFo4dO/bDurv07dsXBw8eFP4uV64cZDIZVFRU0KhRI7Rt2xaOjo6oVKkSNDU1C6TP7BaZsiMxMRG+vr64e/curl27Bnd3d1hbW8PS0hLm5ubQ09ND2bJlYWJiIszWRSUrUhqoVKkS5s6diz59+uSrfrEspM2YMQPBwcHC1r/GjRtDLpfj4MGDaNOmDX766ScoKyvj/PnzUFdXh7W1NcaOHYv9+/f/8D/MwMBAyGSyDMd+//13jB8/vkD7kcvl2Lt3L3bu3Ik3b94gLi4OFSpUwE8//YQZM2ZAR0cnU52EhAQMGzYMd+7cQUhICGxsbFCnTh20atUK69evh7GxcYHKKCJSHKxevRoDBgzAkSNHoKWlhS1bthSYF0uhzXS1tbURFxcHbW1txMbGwsTEBB4eHti1axfOnDmD5ORk6OvrQ1dXFwYGBlBXV4empiaqVKmC2rVrw8nJKdd20e+NDRs2YPz48Zg2bRrq1q0LAwMD1KtXL9c+l1FRUXjx4gUaNGiQpfmFJN6+fYuJEyciNDQUs2fPRp06daClpYWXL19i+fLlMDQ0xJYtWzLVvXHjBlxdXdGvXz+Ym5sjOjoaMTExSExMxOjRo/P1KiYiUhKQSqVYsGAB3N3dkZKSIqzZAMCdO3dQv379XLdVLOYFmUyGU6dO4eLFi5BKpTAxMcGSJUuEc+/evUNcXBxCQkIQGRkJmUyG2NhYnD59GpcvX0aVKlXw/PnzfPVd2omJicHevXsxZ84c2NrawsLCAsbGxqhQoQKqVq2KOnXqCG8DERERMDQ0hKamJgwMDKCqqgpfX18AwLhx47Bhwwah3dDQUNjZ2SE+Ph5KSkoYN24cRo8ejYiICPj7++P9+/d4/fo1zp07h6pVq+L69euZZEtNTcWKFSvw8uVLxMbGQlFREVKpFOfOnQMAnD17VlS8IqUOuVyOKVOm4Pr161i/fj3U1dVhbm4OY2PjfHnUlAibbjq1a9fGf9ssW7Ys7Ozs8OnTJyGt+NChQ9G+ffsC7bu0IZFI8PDhQ4SEhCAkJAS+vr548uQJvLy8oK6ujgoVKkBDQwOenp6IiYnJUNfMzAynTp1CnTp1hGPBwcHo2rUrnjx5AhUVFUgkEqirq0NbWxv6+vowMzODra0tatWqhVq1auHEiRPw8vJCfHw85HI5YmJi8OnTJ8THx6NcuXIwNDSEnp4eDA0NYWRkBAMDAwwfPhwmJiZFfalERL6Jd+/eoUKFCihfvjwOHTqEBg0afJP7YolSuikpKXj48CEOHTqEixcv4s2bN4iMjMS7d+9gaWkp2gRzAUl8+vQJ79+/R3R0NCIiIvD582d8/PgRr1+/FtzdWrRogYkTJ8Lf3x+PHz/Gs2fP8OzZM3z69AnGxsawt7eHtbW1YLZITExETEwMgoOD8fnzZ7Rv3x5t2rSBnp4eFBUVoampCQsLCxgZGYn+tCLfHampqThw4AAWL16MpKQkuLq6ol69enBxcYGDgwP09PRy3Va+lW6tWrXo7u4OIyMjBAYGQlVVFYaGhvDx8YG/vz9iYmKQlJSEmjVr5iuE2ogRI7Bjxw6sXbsWDg4OaNWqlbiNsQBIV8r79+/H2rVr4ezsjNq1a6NatWqoXr06bGxsflh7uYjI1yAJHx8f3Lp1C56ennj48CHevn0LNTU1TJw4Eba2tjA0NETt2rWhra2N6OhoJCcnw8TERNBf+Va6Ojo6JAllZWUoKChAKpVCS0sLQJpLhY6ODlRUVODl5YWoqCg4OTnBzMxM2DFVoUIFmJubC/vZNTU1ERUVheDgYMTExODBgwfw8vJCSkoKXr9+jffv30NfX1+wTYaEhCA0NBT6+vp4/vz5D+/VICIiUjyQxKNHj/Dnn38iIiICISEhePToESQSCcqUKQNVVVXExcXB1tYWdnZ2OHPmTP6UbuXKlfnixQuEhobCwMAAcrkc79+/R8WKFTPNSIODg/H06VPB/ujv74+3b98iODhYWOGOi4uDnp4ezMzMoKOjg8TERMTHxyM+Ph6RkZFQVlaGqqpqphQfderUwc2bN8XZmYiISIkhLi4OysrKwk7X+Ph4vH//Hv7+/ujYsWPJselmB0mEhIQgKCgIxsbGMDU1zXMgGhEREZGSQKmIMqagoABTU9MsA8yIiIiIfC+Iq1YiIiIiRYiodEVERESKkBxtugoKCiU3mK6IiIhIySWcZJZR8XNUuiIiIiIiBYtoXhAREREpQkSlKyIiIlKEiEpXREREpAgRla6IiIhIESIqXREREZEi5P8AtDW1TTBU1UEAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {
@@ -159,11 +159,19 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85b6dffe-c828-4de6-b8eb-005218144b57",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/examples/example-viz-3.ipynb b/examples/example-viz-3.ipynb
index a763f5b..7f19559 100644
--- a/examples/example-viz-3.ipynb
+++ b/examples/example-viz-3.ipynb
@@ -1,8 +1,16 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bee2fd42-01de-4c90-9246-ad04f1e97c31",
+   "metadata": {},
+   "source": [
+    "# Example Vis 3"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "annoying-fetish",
    "metadata": {},
    "outputs": [],
@@ -20,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "polish-performance",
    "metadata": {},
    "outputs": [],
@@ -85,13 +93,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "modular-cleaners",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "triangulating1...\n",
+      "triangulating1 done.\n",
+      "hull0:  1416\n",
+      "triangulating1...\n",
+      "triangulating1 done.\n",
+      "triangulating1...\n",
+      "triangulating1 done.\n",
+      "triangulating1...\n",
+      "triangulating1 done.\n",
+      "triangulating1...\n",
+      "triangulating1 done.\n",
+      "make_nc_hull hull size:  1610\n",
+      "triangulating1...\n",
+      "triangulating1 done.\n",
+      "make_nc_hull hull size:  889\n",
+      "triangulating1...\n",
+      "triangulating1 done.\n",
+      "hull6:  0x0\n",
+      "make_nc_hull hull size:  1600\n",
+      "triangulating1...\n",
+      "triangulating1 done.\n",
+      "hull6a:  0x3d2aa80000000008\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Spatial ID 0x3800000000000000\n",
+      "triangulating1...\n",
+      "triangulating1 done.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "\n",
-    "\n",
     "# figax = add_coastlines(plt.subplots(subplot_kw={'projection':proj,'transform':transf}))\n",
     "\n",
     "# resolution = 7\n",
@@ -241,7 +309,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/examples/examples.ipynb b/examples/examples.ipynb
deleted file mode 100644
index 174fb6e..0000000
--- a/examples/examples.ipynb
+++ /dev/null
@@ -1,110 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "significant-reading",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy \n",
-    "import pystare "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "rational-least",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 0x8 0x7ffffffffff\n",
-      "1 0x30000000000a 0x67ffffffffff\n",
-      "2 0x70000000000a 0x907fffffffff\n",
-      "3 -0x3e6 -0x3e7\n",
-      "4 0x907fffffffff 0x30\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = numpy.array([0x0000000000000008, 0x000030000000000a, 0x000067ffffffffff, 0x000070000000000a, 0x0000907fffffffff],dtype=numpy.int64)\n",
-    "starts,ends = pystare.from_intervals(a)\n",
-    "\n",
-    "for i in range(starts.size):\n",
-    "  print(i,hex(starts[i]),hex(ends[i]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "japanese-skating",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[4151504989081014892 4161865161846704588 3643626718498217164]\n",
-      "[30.00000012 45.00000003 59.99999986] [44.99999991 60.00000013  9.9999999 ]\n",
-      "[30.00000012 45.00000003 59.99999986] [44.99999991 60.00000013  9.9999999 ] [12 12 12]\n",
-      "[12 12 12]\n",
-      "[8.66507750e-08 8.74786654e-08 7.97819113e-08]\n"
-     ]
-    }
-   ],
-   "source": [
-    "lat = numpy.array([30,45,60], dtype=numpy.double)\n",
-    "lon = numpy.array([45,60,10], dtype=numpy.double)\n",
-    "\n",
-    "\n",
-    "indices = pystare.from_latlon(lat, lon, 12)\n",
-    "print(indices)\n",
-    "\n",
-    "lat, lon = pystare.to_latlon(indices)\n",
-    "print(lat, lon)\n",
-    "\n",
-    "lat, lon, level = pystare.to_latlonlevel(indices)\n",
-    "print(lat, lon, level)\n",
-    "\n",
-    "level = pystare.to_level(indices)\n",
-    "print(level)\n",
-    "\n",
-    "area = pystare.to_area(indices)\n",
-    "print(area)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dietary-amsterdam",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/expand_intervals.ipynb b/examples/expand_intervals.ipynb
new file mode 100644
index 0000000..348e283
--- /dev/null
+++ b/examples/expand_intervals.ipynb
@@ -0,0 +1,178 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c003e56e-b205-4138-a897-3cc36ac3eb17",
+   "metadata": {},
+   "source": [
+    "# Expanding Intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "d07e22af-5cc4-4e83-8789-ece24e12b337",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pystare\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.tri as tri\n",
+    "import cartopy.crs as ccrs\n",
+    "import numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "1d5a8a3d-940a-4cea-b384-8d4d15bac6de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sids = numpy.array([2521902129312956427, 2521902404190863371, 2521899930289700875,\n",
+    "       2521900067728654347, 2521900205167607819, 2521991876949573643,\n",
+    "       2521992701583294475, 2521992839022247947, 2521992976461201419,\n",
+    "       2521997099629805579, 2521889484929237003, 2521991739510620171,\n",
+    "       2521902679068770315, 2521908726382723083, 2521894432731561995,\n",
+    "       2521901579557142539, 2521989952804225035, 2521990365121085451,\n",
+    "       2521991327193759755, 2521997237068759051, 2521997511946665995,\n",
+    "       2521898418461212683, 2521898555900166155, 2521898693339119627,\n",
+    "       2521889759807143947, 2521903091385630731, 2521901716996096011,\n",
+    "       2521897456388538379, 2521902266751909899, 2521897868705398795,\n",
+    "       2521898968217026571, 2521990227682131979, 2521889622368190475,\n",
+    "       2521901167240282123, 2521887973100748811, 2521902816507723787,\n",
+    "       2521892920903073803, 2521894020414701579, 2521890721879818251,\n",
+    "       2521993526217015307, 2521901304679235595, 2521901854435049483,\n",
+    "       2521902953946677259, 2521898006144352267, 2521890859318771723,\n",
+    "       2521886461272260619, 2521888110539702283, 2521898830778073099,\n",
+    "       2521903228824584203, 2521897044071677963, 2521903366263537675,\n",
+    "       2521899105655980043, 2521903503702491147, 2521886048955400203,\n",
+    "       2521991602071666699, 2521899380533886987, 2521890584440864779,\n",
+    "       2521992014388527115, 2521886873589121035, 2521887011028074507,\n",
+    "       2521889347490283531, 2521890447001911307, 2521886736150167563,\n",
+    "       2521893745536794635, 2521893882975748107, 2521894157853655051,\n",
+    "       2521897181510631435, 2521897731266445323, 2521899792850747403,\n",
+    "       2521887698222841867, 2521900892362375179, 2521901442118189067,\n",
+    "       2521906389920514059, 2521908588943769611, 2521901991874002955,\n",
+    "       2521902541629816843, 2521909001260630027, 2521989540487364619,\n",
+    "       2521897318949584907, 2521898143583305739, 2521898281022259211,\n",
+    "       2521897593827491851, 2521993113900154891, 2521993251339108363,\n",
+    "       2521993388778061835, 2521993663655968779, 2521886598711214091,\n",
+    "       2521886186394353675])\n",
+    "\n",
+    "expanded_stare_test = numpy.array([0x22ff8a000000000b, 0x22ff8a200000000b,\n",
+    "        0x22ff8a600000000b, 0x22ff8a800000000b,0x22ff8aa00000000b,\n",
+    "        0x22ff8ac00000000b,0x22ff8ae00000000b,0x22ff8b800000000b,\n",
+    "        0x22ff8bc00000000b,0x22ff8be00000000b,0x22ff8d000000000a,\n",
+    "        0x22ff8e000000000a,0x22ff90400000000b,0x22ff91000000000a,\n",
+    "        0x22ff91a00000000b,0x22ff940000000009,0x22ff96200000000b,\n",
+    "        0x22ff96800000000a,0x22ff97800000000b,0x22ff97c00000000b,\n",
+    "        0x22ff97e00000000b,0x22ff980000000009,0x22ff9c800000000b,\n",
+    "        0x22ff9e800000000b,0x22ff9ea00000000b,0x22ff9ee00000000b,\n",
+    "        0x22ffe8200000000b,0x22ffe8800000000b,0x22ffe8c00000000b,\n",
+    "        0x22ffe8e00000000b,0x22ffe9c00000000b,0x22ffea000000000a,\n",
+    "        0x22ffeb000000000a,0x22ffeb800000000a,0x22ffef000000000b,\n",
+    "        0x22ffef200000000b,0x22ffef600000000b\n",
+    "        ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "9a0d02f7-2129-43c0-9d2c-076a61807170",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compressed = pystare.to_compressed_range(sids)\n",
+    "expanded = pystare.expand_intervals(compressed, -1, False)    \n",
+    "expanded_wrong = pystare.expand_intervals(compressed, -1, True)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "cd29402e-9567-46ec-9181-d3a182b82793",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sids2triang(sids):\n",
+    "    lons, lats, intmat = pystare.triangulate_indices(sids)\n",
+    "    return tri.Triangulation(lons, lats, intmat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "d8acc5fd-7202-46f6-adcd-12d228ebb195",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Expanded SIDs (correct)')"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "proj   = ccrs.PlateCarree()\n",
+    "transf = ccrs.PlateCarree()\n",
+    "\n",
+    "fig, ax = plt.subplots(nrows=2,ncols=2,subplot_kw={'projection':proj,'transform':transf})\n",
+    "\n",
+    "triang = sids2triang(sids)\n",
+    "ax[0, 0].triplot(triang, '-',transform=transf, lw=1)    \n",
+    "ax[0, 0].set_title('Original SIDs')\n",
+    "\n",
+    "triang = sids2triang(compressed)\n",
+    "ax[0, 1].triplot(triang, '-',transform=transf, lw=1)    \n",
+    "ax[0, 1].set_title('Compressed SIDs')\n",
+    "\n",
+    "triang = sids2triang(expanded_wrong)\n",
+    "ax[1, 0].triplot(triang, '-',transform=transf, lw=1)    \n",
+    "ax[1, 0].set_title('Expanded SIDs (wrong)')\n",
+    "\n",
+    "triang = sids2triang(expanded)\n",
+    "ax[1,1].triplot(triang, '-',transform=transf, lw=1)    \n",
+    "ax[1, 1].set_title('Expanded SIDs (correct)')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/intersect_single_res.ipynb b/examples/intersect_single_res.ipynb
new file mode 100644
index 0000000..1b4646d
--- /dev/null
+++ b/examples/intersect_single_res.ipynb
@@ -0,0 +1,222 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Intersecting SIDs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.tri as tri\n",
+    "import cartopy.crs as ccrs\n",
+    "import numpy\n",
+    "import pystare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "proj = ccrs.PlateCarree()\n",
+    "transf = ccrs.PlateCarree()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def plot1(lon, lat, lons, lats, triang, c0='r', c1='b', transf=None, lw=1, ax=None):\n",
+    "    if lon is not None and ax is not None:\n",
+    "        x = numpy.zeros([lon.size + 1], dtype=numpy.double)\n",
+    "        x[:-1] = lon[:]\n",
+    "        x[-1] = lon[0]\n",
+    "        y = numpy.zeros([lat.size + 1], dtype=numpy.double)\n",
+    "        y[:-1] = lat[:]\n",
+    "        y[-1] = lat[0]\n",
+    "        ax.plot(x, y, True, transform=transf, c=c0)\n",
+    "    ax.triplot(triang, c1 + '-', transform=transf, lw=lw, markersize=3)\n",
+    "    ax.scatter(lons, lats, s=10, c=c1, transform=ccrs.PlateCarree())\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resolution = 6\n",
+    "resolution0 = resolution\n",
+    "lat0 = numpy.array([10, 5, 60, 70], dtype=numpy.double)\n",
+    "lon0 = numpy.array([-30, -20, 60, 10], dtype=numpy.double)\n",
+    "hull0 = pystare.cover_from_hull(lat0, lon0, resolution0)\n",
+    "\n",
+    "lons0, lats0, intmat0 = pystare.triangulate_indices(hull0)\n",
+    "triang0 = tri.Triangulation(lons0, lats0, intmat0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resolution1 = 6\n",
+    "lat1 = numpy.array([10, 20, 30, 20], dtype=numpy.double)\n",
+    "lon1 = numpy.array([-60, 60, 60, -60], dtype=numpy.double)\n",
+    "hull1 = pystare.cover_from_hull(lat1, lon1, resolution1)\n",
+    "\n",
+    "lons1, lats1, intmat1 = pystare.triangulate_indices(hull1)\n",
+    "triang1 = tri.Triangulation(lons1, lats1, intmat1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "r01 n01:  79\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(4, subplot_kw={'projection': proj, 'transform': transf})\n",
+    "\n",
+    "ax = axs[0]\n",
+    "ax.coastlines()\n",
+    "\n",
+    "plot1(None, None, lons0, lats0, triang0, c0='r', c1='b', transf=transf, ax=ax)\n",
+    "plot1(None, None, lons1, lats1, triang1, c0='c', c1='r', transf=transf, ax=ax)\n",
+    "\n",
+    "intersectedFalse = pystare.intersection(hull0, hull1, multi_resolution=False)\n",
+    "intersectedTrue = pystare.intersection(hull0, hull1, multi_resolution=True)\n",
+    "\n",
+    "ax = axs[1]\n",
+    "ax.coastlines()\n",
+    "\n",
+    "lonsF, latsF, intmatF = pystare.triangulate_indices(intersectedFalse)\n",
+    "triangF = tri.Triangulation(lonsF, latsF, intmatF)\n",
+    "plot1(None, None, lonsF, latsF, triangF, c0='r', c1='b', transf=transf, ax=ax)\n",
+    "\n",
+    "ax = axs[2]\n",
+    "ax.coastlines()\n",
+    "lonsT, latsT, intmatT = pystare.triangulate_indices(intersectedTrue)\n",
+    "triangT = tri.Triangulation(lonsT, latsT, intmatT)\n",
+    "plot1(None, None, lonsT, latsT, triangT, c0='r', c1='b', transf=transf, ax=ax)\n",
+    "\n",
+    "r0 = pystare.core.srange()\n",
+    "r0.add_intervals(hull0)\n",
+    "\n",
+    "r1 = pystare.core.srange()\n",
+    "r1.add_intervals(hull1)\n",
+    "\n",
+    "r01 = pystare.core.srange()\n",
+    "r01.add_intersect(r0, r1, False)\n",
+    "n01 = r01.get_size_as_values()\n",
+    "\n",
+    "intersected = numpy.zeros([n01], dtype=numpy.int64)\n",
+    "r01.copy_values(intersected)\n",
+    "# See examples/test_intersect_single_res.py\n",
+    "\n",
+    "r01.purge()\n",
+    "n01 = r01.get_size_as_values()\n",
+    "\n",
+    "r01.reset()\n",
+    "r01.add_intersect(r0, r1, True)\n",
+    "n01 = r01.get_size_as_values()\n",
+    "\n",
+    "print('r01 n01: ', n01)\n",
+    "\n",
+    "intersected = numpy.zeros([n01], dtype=numpy.int64)\n",
+    "r01.copy_values(intersected)\n",
+    "\n",
+    "###??? Would intervals be different?\n",
+    "\n",
+    "lonsRT, latsRT, intmatRT = pystare.triangulate_indices(intersected)\n",
+    "triangRT = tri.Triangulation(lonsRT, latsRT, intmatRT)\n",
+    "ax = axs[3]\n",
+    "\n",
+    "ax.coastlines()\n",
+    "\n",
+    "lonsRT_, latsRT_, intmatRT_ = pystare.triangulate_indices(intersected[51:55])\n",
+    "triangRT_ = tri.Triangulation(lonsRT_, latsRT_, intmatRT_)\n",
+    "\n",
+    "plot1(None, None, lonsRT_, latsRT_, triangRT_, c0='g', c1='g', transf=transf, ax=ax)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/spatial_conversions.ipynb b/examples/spatial_conversions.ipynb
index a4c3b52..334fa0a 100644
--- a/examples/spatial_conversions.ipynb
+++ b/examples/spatial_conversions.ipynb
@@ -23,7 +23,6 @@
    "outputs": [],
    "source": [
     "import numpy\n",
-    "import geopandas\n",
     "from pyhdf.SD import SD"
    ]
   },
@@ -31,7 +30,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Conversions"
+    "## Conversions"
    ]
   },
   {
@@ -144,7 +143,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Covers"
+    "## Covers"
    ]
   },
   {
@@ -251,7 +250,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Overlay"
+    "## Overlay"
    ]
   },
   {
@@ -347,7 +346,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -365,5 +364,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/temporal.ipynb b/examples/temporal.ipynb
index 84df7c5..fccb86c 100644
--- a/examples/temporal.ipynb
+++ b/examples/temporal.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 49,
    "id": "e5eb3551",
    "metadata": {},
    "outputs": [],
@@ -18,97 +18,143 @@
     "import pystare\n",
     "import datetime\n",
     "import numpy\n",
-    "import erfa\n",
-    "import astropy"
+    "#import astropy.time\n",
+    "\n",
+    "from astropy import time"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1a637f57",
+   "id": "d093caaf-8996-4e20-aad9-366ed9daac3f",
    "metadata": {},
    "source": [
-    "## Conversions"
+    "## Full circle"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "15dabf47-f3b8-40e9-9055-f5e511e25abb",
+   "execution_count": 57,
+   "id": "8217b5b0-c91a-4a32-9834-265faf2cc307",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2276059438861267137, 2275939265676325057])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "import re\n",
-    "\n",
-    "regex_iso8601 = r'^(-?(?:[1-9][0-9]*)?[0-9]{4})-(1[0-2]|0[1-9])-(3[01]|0[1-9]|[12][0-9])T(2[0-3]|[01][0-9]):([0-5][0-9]):([0-5][0-9])(\\.[0-9]+)?(Z|[+-](?:2[0-3]|[01][0-9]):[0-5][0-9])?$'\n",
-    "match_iso8601 = re.compile(regex_iso8601).match\n",
-    "\n",
-    "\n",
-    "regex_stare = r'^(-?(?:[1-9][0-9]*)?[0-9]{4})-(1[0-2]|0[1-9])-(3[01]|0[1-9]|[12][0-9])T(2[0-3]|[01][0-9]):([0-5][0-9]):([0-5][0-9])\\.([0-9]+)?(\\s\\(([0-9]+)\\s([0-9]+)\\)\\s\\(([0-9])\\))$'\n",
-    "match_stare = re.compile(regex_stare).match\n",
-    "\n",
-    "\n",
-    "def validate_iso8601(str_val):    \n",
-    "    if match_iso8601(str_val) is not None:\n",
-    "        return True\n",
-    "    else:\n",
-    "        return False\n",
-    "    \n",
-    "def validate_stare_timestring(str_val):\n",
-    "    if match_stare(str_val) is not None:\n",
-    "        return True\n",
-    "    else:\n",
-    "        return False\n",
-    "    \n",
-    "    \n",
-    "def stare_timestring(timestamp, forward_res=48, backwards_res=48, stare_type=1, time_standard='UTC'):\n",
-    "    if isinstance(timestamp, datetime.datetime):\n",
-    "        time_string = timestamp.isoformat()\n",
-    "    elif isinstance(timestamp, numpy.datetime64):\n",
-    "        time_string = numpy.datetime_as_string(timestamp)\n",
-    "    elif isinstance(timestamp, tuple) and len(timestamp) == 2:\n",
-    "        # SOFA/ERFA tuple        \n",
-    "        time_string = erfa.d2dtf(scale=time_standard, ndp=6,  d1=timestamp[0], d2=timestamp[1])\n",
-    "    elif isinstance(timestamp, str) and validate_iso8601(timestamp):\n",
-    "        # ISO 8601 timestamp\n",
-    "        time_string = timestamp\n",
-    "    elif isinstance(timestamp, str) and validate_stare_timestamp(timestamp):\n",
-    "        # Already stare timestamp\n",
-    "        return timestamp\n",
-    "    else:\n",
-    "        raise ValueError('unknown input \"' + timestamp + '\"')\n",
-    "    suffix = ' ({f_res}) ({b_res}) ({type})'.format(f_res=forward_res, b_res=backwards_res, type=stare_type)\n",
-    "    return time_string + suffix\n",
-    "\n",
-    "        \n",
-    "def erfajd_from_timestamp(timestamp, time_scale='UTC'):\n",
-    "    if isinstance(timestamp, numpy.datetime64):\n",
-    "        timestamp = timestamp.astype(datetime.datetime)\n",
-    "    elif isinstance(timestamp, str) and validate_iso8601(timestamp):\n",
-    "        timestamp = datetime.datetime.fromisoformat(timestamp)\n",
-    "    \n",
-    "    if isinstance(timestamp, datetime.datetime):        \n",
-    "        erfa_jd = erfa.dtf2d(scale=time_scale, \n",
-    "                             iy=timestamp.year, im=timestamp.month, id=timestamp.day, \n",
-    "                             ihr=timestamp.hour, imn=timestamp.minute, sec=timestamp.second+timestamp.microsecond/1000/1000)\n",
-    "    \n",
-    "    return erfa_jd\n",
-    "\n",
-    "def stare_from_timestamp(timestamp, forward_res=48, backwards_res=48, stare_type=1, time_scale='UTC'):\n",
-    "    stare_ts = stare_timestring(timestamp, forward_res, backwards_res, staret_type, time_scale)\n",
-    "    pystare.from_tai_iso_string(stare_ts)"
+    "# Create the TIVs\n",
+    "times = numpy.array(['2021-09-03', '2021-07-17 11:16'], dtype='datetime64')\n",
+    "times = time.Time(times, scale='utc', format='datetime64')\n",
+    "tivs = pystare.from_julian_date(times.jd1, times.jd2, \n",
+    "                                scale='utc', \n",
+    "                                forward_res=48, reverse_res=48)\n",
+    "tivs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "5ebc15b3-351c-4b90-9df9-fd90ad5cb0f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.datetime(2021, 9, 3, 0, 0),\n",
+       "       datetime.datetime(2021, 7, 17, 11, 16)], dtype=object)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Back via JD\n",
+    "jd = pystare.to_julian_date(tivs, scale='utc')\n",
+    "times = astropy.time.Time(val=jd[0], val2=jd[1], format='jd')\n",
+    "times.to_datetime()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "d9701dfd-77a6-4452-a364-7f2eabf7a1d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['2021-09-03T00:00:00', '2021-07-17T11:16:00']"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Back via MS\n",
+    "mss = pystare.to_ms_since_epoch_utc(tivs)\n",
+    "dts = [datetime.datetime.utcfromtimestamp(ms/1000) for ms in mss]\n",
+    "[dt.isoformat() for dt in dts]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a637f57",
+   "metadata": {},
+   "source": [
+    "## Valudations"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "id": "surgical-estonia",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pystare.validate_stare_timestring('2021-01-09T17:47:56.154 (45 12) (1)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ad07c1d1-2cba-44e0-9bd7-4cda080b5ebc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "ts = '2021-01-09T17:47:56.154564 (45 12) (1)'\n",
-    "validate_iso8601('2021-01-09T17:47:56.154564 (45 12) (1)')\n",
-    "validate_iso8601('2021-01-09T17:47:56.2435+05:00')\n",
-    "match_iso8601(ts)"
+    "pystare.validate_iso8601_string('2021-01-09T17:47:56.2435+05:00')"
    ]
   },
   {
@@ -121,17 +167,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "checked-drive",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1629999406000])"
+       "array([1633457355000])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -145,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "id": "finished-cincinnati",
    "metadata": {},
    "outputs": [
@@ -155,7 +201,7 @@
        "array([2275448110396223681])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -167,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "id": "protective-subdivision",
    "metadata": {},
    "outputs": [
@@ -177,7 +223,7 @@
        "array(['2021-01-03T00:00:00.000'], dtype='<U42')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -190,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "id": "precise-indication",
    "metadata": {},
    "outputs": [
@@ -201,7 +247,7 @@
        "       [0, 1]])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -224,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "id": "sporting-amazon",
    "metadata": {},
    "outputs": [
@@ -234,7 +280,7 @@
        "array([48, 38, 32, 26, 21, 12])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -252,28 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "id": "decreased-footage",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2275448179115690537, 2234915782217697833])"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tiv1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 16,
    "id": "secure-bullet",
    "metadata": {},
    "outputs": [
@@ -283,13 +308,13 @@
        "array([2270946778511573245, 2230414381613580541])"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pystare.reverse_resolution(tiv)\n",
+    "pystare.reverse_resolution(tiv1)\n",
     "pystare.set_reverse_resolution(tiv1, numpy.array([9, 20]))\n",
     "\n",
     "pystare.lower_bound_tai(tiv1)"
@@ -305,35 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "suffering-worker",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'2021-08-26T17:03:56.600'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def pad3(s):\n",
-    "    s = s.ljust(4, '0')\n",
-    "    s=s[0:4]\n",
-    "    return s\n",
-    "\n",
-    "s = '2021-08-26T17:03:56.6'\n",
-    "ms = match_iso8601(s).groups()[6]\n",
-    "s.replace(ms, pad3(ms))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 18,
    "id": "designed-cancer",
    "metadata": {},
    "outputs": [
@@ -343,7 +340,7 @@
        "array([2276038620409631817])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -355,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "id": "victorian-ireland",
    "metadata": {},
    "outputs": [
@@ -365,7 +362,7 @@
        "array([2276038620410065089])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -378,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
    "id": "intended-flesh",
    "metadata": {},
    "outputs": [
@@ -388,7 +385,7 @@
        "True"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -399,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 21,
    "id": "latter-therapy",
    "metadata": {},
    "outputs": [
@@ -409,7 +406,7 @@
        "['2021-08-26T17:03:56.626 (48 48) (1)']"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -428,308 +425,228 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "c96aafa8-47a9-439c-a139-f90501ef85d4",
+   "execution_count": 29,
+   "id": "optical-reynolds",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2459493.] [0.25661248]\n"
+     ]
     }
    ],
    "source": [
-    "now = datetime.datetime.now()\n",
-    "jd1 = erfajd_from_timestamp(now)\n",
-    "jd2 = erfajd_from_timestamp(now.isoformat())\n",
-    "jd3 = erfajd_from_timestamp(numpy.datetime64(now))\n",
-    "jd1 == jd2 == jd3"
+    "t = time.Time(now.isoformat(), format='isot')\n",
+    "jd1 = numpy.array([t.jd1])\n",
+    "jd2 = numpy.array([t.jd2])\n",
+    "print(jd1, jd2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "950e3b53-739b-49af-8164-08cc3a2dac2f",
+   "execution_count": 27,
+   "id": "15137e73-1bd6-48cc-9c18-4b8bb5949cec",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17.616"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "erfa.jdcalf(3, *jd1)[-1]/1000*24"
+    "tiv = pystare.from_julian_date(jd1, jd2, scale='tai')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "received-albuquerque",
+   "execution_count": 30,
+   "id": "51c1c890",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'2021-08-26T17:36:46.426092'"
+       "(array([2459215.5]), array([277.75661248]))"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "now.isoformat()"
+    "pystare.to_julian_date(tiv, scale='tai')"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "optical-reynolds",
+   "cell_type": "markdown",
+   "id": "598c3559",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2459453.0, 0.23387067236111114)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "from astropy import time\n",
-    "t = time.Time(now.isoformat(), format='isot')\n",
-    "t.jd1, t.jd2"
+    "### Temporal Triple"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "id": "51c1c890",
+   "execution_count": 31,
+   "id": "da3ca012",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([2459215.5]), array([237.71107206]))"
+       "(array([2276139850217242877]),\n",
+       " array([2276139850217271489]),\n",
+       " array([2276139850217291773]))"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pystare.to_julian_tai(tiv)"
+    "triple = pystare.to_temporal_triple_tai(tiv)\n",
+    "triple = pystare.to_temporal_triple_ms(tiv)\n",
+    "triple"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "id": "experimental-purple",
+   "execution_count": 32,
+   "id": "7621ebfc",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([2275448179115690537, 2234915782217697833])"
+       "array([2276139850217271489])"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tiv1"
+    "pystare.from_temporal_triple(numpy.array(triple).flatten())"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "7ad5aed8-2d34-4e0f-a19a-871a3745d8be",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2275448179115690533, 2234915782217697873])"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
   {
    "cell_type": "markdown",
-   "id": "598c3559",
+   "id": "981dc7ae",
    "metadata": {},
    "source": [
-    "### Temporal Triple"
+    "### Julian TAI"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "da3ca012",
+   "execution_count": 33,
+   "id": "6bef6bb4",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(array([2276038620410036477]),\n",
-       " array([2276038620410065089]),\n",
-       " array([2276038620410085373]))"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2459246.5 0.5\n"
+     ]
     }
    ],
    "source": [
-    "triple = pystare.to_temporal_triple_tai(tiv)\n",
-    "triple = pystare.to_temporal_triple_ms(tiv)\n",
-    "triple"
+    "time = datetime.datetime(2021,2,1,12,0)\n",
+    "time_tuple = time.timetuple()\n",
+    "\n",
+    "time = erfa.dtf2d(\"UTC\", *time_tuple[0:6])\n",
+    "print(time[0], time[1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "7621ebfc",
+   "execution_count": 34,
+   "id": "1277749e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([2276038620410065089])"
+       "array([2275521502176620737])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pystare.from_temporal_triple(numpy.array(triple).flatten())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "981dc7ae",
-   "metadata": {},
-   "source": [
-    "### Julian TAI"
+    "tiv_tai = pystare.from_julian_date(jd1=numpy.array([time[0]]), \n",
+    "                                   jd2=numpy.array([time[1]]),\n",
+    "                                   scale='tai')\n",
+    "tiv_tai"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "6bef6bb4",
+   "execution_count": 35,
+   "id": "fe6188d4-3917-4e64-9470-4d44fce1802d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(2021, 2, 1, 0.5)"
+       "array([2275521502797377729])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "time = datetime.datetime(2021,2,1,12,0)\n",
-    "time_tuple = time.timetuple()\n",
-    "\n",
-    "time = erfa.dtf2d(\"UTC\", *time_tuple[0:6])\n",
-    "erfa.jd2cal(*time)"
+    "tiv_utc = pystare.from_julian_date(jd1=numpy.array([time[0]]), \n",
+    "                                   jd2=numpy.array([time[1]]),\n",
+    "                                   scale='utc')\n",
+    "tiv_utc"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "1277749e",
+   "execution_count": 36,
+   "id": "99f54913",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([2275521502176608257])"
+       "(array([2459215.5]), array([31.5]))"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pystare.from_julian_tai(numpy.array([time[0]]), \n",
-    "                       numpy.array([time[1]]))"
+    "pystare.to_julian_date(tiv_tai, scale='tai')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "fe6188d4-3917-4e64-9470-4d44fce1802d",
+   "execution_count": 37,
+   "id": "e7cc8bef-ab3f-45a0-903e-0fe8220bc1df",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([2275521502797365249])"
+       "(array([2459215.5]), array([31.5]))"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pystare.from_julian_utc(numpy.array([time[0]]), \n",
-    "                       numpy.array([time[1]]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "99f54913",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "module 'pystare' has no attribute 'to_JulianTAI'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-32-03f5bc55736b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpystare\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_JulianTAI\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtiv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpystare\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_JulianUTC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtiv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: module 'pystare' has no attribute 'to_JulianTAI'"
-     ]
-    }
-   ],
-   "source": [
-    "t = pystare.to_JulianTAI(tiv)\n",
-    "t = pystare.to_JulianUTC(tiv)\n",
-    "t"
+    "pystare.to_julian_date(tiv_utc, scale='utc')"
    ]
   },
   {
@@ -742,15 +659,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "id": "c0ff6fad",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2275521502797398013])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "pystare.lowerBoundTAI\n",
-    "pystare.upperBoundTAI\n",
-    "pystare.lowerBoundMS\n",
-    "pystare.upperBoundMS"
+    "pystare.lower_bound_tai(tiv_tai)\n",
+    "pystare.upper_bound_tai(tiv_tai)\n",
+    "pystare.lower_bound_ms(tiv_utc)\n",
+    "pystare.upper_bound_ms(tiv_utc)"
    ]
   },
   {
@@ -763,18 +691,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "id": "1f6519d3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function pystare.temporal.coarsest_resolution_finer_or_equal_ms(ms)>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "set_reverse_resolution\n",
-    "set_forward_resolution\n",
-    "reverse_resolution\n",
-    "forward_resolution\n",
-    "coarsen\n",
-    "set_temporal_resolutions_from_sorted\n",
-    "coarsest_resolution_finer_or_equal_ms"
+    "pystare.set_reverse_resolution\n",
+    "pystare.set_forward_resolution\n",
+    "pystare.reverse_resolution\n",
+    "pystare.forward_resolution\n",
+    "pystare.coarsen\n",
+    "pystare.set_temporal_resolutions_from_sorted\n",
+    "pystare.coarsest_resolution_finer_or_equal_ms"
    ]
   },
   {
@@ -787,24 +726,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "2e907cfc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function pystare.temporal.temporal_contains_instant(indices1, indices2)>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "cmp_temporal\n",
+    "pystare.cmp_temporal\n",
     "\n",
-    "temporalValueIntersectionIfOverlap\n",
-    "temporalValueUnionIfOverlap\n",
-    "temporalOverlapTAI\n",
-    "temporalOverlap\n",
-    "temporalContainsInstant"
+    "pystare.temporal_value_intersection_if_overlap\n",
+    "pystare.temporal_value_union_if_overlap\n",
+    "pystare.temporal_overlap_tai\n",
+    "pystare.temporal_overlap\n",
+    "pystare.temporal_contains_instant"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05d40f9f-1d29-4beb-81ef-0228283af0e1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -823,4 +781,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/pystare/spatial.py b/pystare/spatial.py
index 1bf035e..5f7e7b1 100644
--- a/pystare/spatial.py
+++ b/pystare/spatial.py
@@ -858,23 +858,38 @@ def spatial_clear_to_resolution(sids):
     return (sids & ~mask) + resolution
 
 
-def shiftarg_lon(lon):
-    """ Corrects longitudes. If lon is outside +/-180, then correct back.
+def lon_wrap_180(lon):
+    """ Wrap angle in degrees to [-180 180]
 
-    """
-    if (lon > 180):
-        return ((lon + 180.0) % 360.0) - 180.0
-    else:
-        return lon
+    Wraps angles (in degrees) to the interval [–180, 180] such that
 
+    - 90 maps to 90
+    - –90 maps to –90
+    - 360 maps to 0
+    - 270 maps to -90
 
-def shiftarg_lat(lat):
-    """ Corrects latitudes; If lat is outside +/-90, then correct back.
-    """
-    if (lat > 90):
-        return ((lat + 90.0) % 180.0) - 90.0
-    else:
-        return lat
+    Notes
+    --------
+    - 180 wraps to -180 but -180 to -180
+    - lon_wrap_180() casts to floats
+    - This method is not intended to correct illformated longitudes outside the interval [-180, 360]
+
+    Examples
+    -----------
+    >>> lon_wrap_180(90.0)
+    90.0
+    >>> lon_wrap_180(-90.0)
+    -90.0
+    >>> lon_wrap_180(360.0)
+    0.0
+    >>> lon_wrap_180(270.0)
+    -90.0
+    >>> lon_wrap_180(180.0)
+    -180.0
+    >>> lon_wrap_180(-180.0)
+    -180.0
+    """
+    return ((lon + 180.0) % 360.0) - 180.0
 
 
 def spatial_resolution_from_km(km, return_int=True):
@@ -901,7 +916,7 @@ def triangulate(lats, lons):
         intmat.append([k, k + 1, k + 2])
         k = k + 3
     for i in range(len(lons)):
-        lons[i] = shiftarg_lon(lons[i])
+        lons[i] = lon_wrap_180(lons[i])
     return lons, lats, intmat
 
 
@@ -909,8 +924,8 @@ def triangulate_indices(indices):
     """
     Prepare data for matplotlib.tri.Triangulate.
 
-    Usage
-    ----------
+    Examples
+    --------
     >>> lons, lats, intmat = triangulate_indices(indices)   # doctest: +SKIP
     >>> triang = tri.Triangulation(lons,lats,intmat)        # doctest: +SKIP
     >>> plt.triplot(triang,'r-',transform=transform,lw=1,markersize=3) # doctest: +SKIP
diff --git a/pystare/temporal.py b/pystare/temporal.py
index 2718815..6a984e3 100644
--- a/pystare/temporal.py
+++ b/pystare/temporal.py
@@ -130,7 +130,7 @@ def coarsest_resolution_finer_or_equal_ms(ms):
     |11        | -           | -     | -   |  1   | Before/After Epoch         |
     +----------+-------------+-------+-----+------+----------------------------+
 
-    Arguments
+    Parameters
     ----------
     ms: 1D array of ints
         resolution in milliseconds


Test patch:
diff --git a/examples/test_intersect_single_res.ipynb b/examples/test_intersect_single_res.ipynb
deleted file mode 100644
index 39e1813..0000000
--- a/examples/test_intersect_single_res.ipynb
+++ /dev/null
@@ -1,209 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.tri as tri\n",
-    "import cartopy.crs as ccrs\n",
-    "import numpy\n",
-    "import pystare"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "proj = ccrs.PlateCarree()\n",
-    "transf = ccrs.PlateCarree()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "def plot1(lon, lat, lons, lats, triang, c0='r', c1='b', transf=None, lw=1, ax=None):\n",
-    "    if lon is not None and ax is not None:\n",
-    "        x = numpy.zeros([lon.size + 1], dtype=numpy.double)\n",
-    "        x[:-1] = lon[:]\n",
-    "        x[-1] = lon[0]\n",
-    "        y = numpy.zeros([lat.size + 1], dtype=numpy.double)\n",
-    "        y[:-1] = lat[:]\n",
-    "        y[-1] = lat[0]\n",
-    "        ax.plot(x, y, True, transform=transf, c=c0)\n",
-    "    ax.triplot(triang, c1 + '-', transform=transf, lw=lw, markersize=3)\n",
-    "    ax.scatter(lons, lats, s=10, c=c1, transform=ccrs.PlateCarree())\n",
-    "    return"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    " resolution = 6\n",
-    "    resolution0 = resolution\n",
-    "    lat0 = numpy.array([10, 5, 60, 70], dtype=numpy.double)\n",
-    "    lon0 = numpy.array([-30, -20, 60, 10], dtype=numpy.double)\n",
-    "    hull0 = pystare.cover_from_hull(lat0, lon0, resolution0)\n",
-    "\n",
-    "    lons0, lats0, intmat0 = pystare.triangulate_indices(hull0)\n",
-    "    triang0 = tri.Triangulation(lons0, lats0, intmat0)\n",
-    "\n",
-    "    resolution1 = 6\n",
-    "    lat1 = numpy.array([10, 20, 30, 20], dtype=numpy.double)\n",
-    "    lon1 = numpy.array([-60, 60, 60, -60], dtype=numpy.double)\n",
-    "    hull1 = pystare.cover_from_hull(lat1, lon1, resolution1)\n",
-    "\n",
-    "    lons1, lats1, intmat1 = pystare.triangulate_indices(hull1)\n",
-    "    triang1 = tri.Triangulation(lons1, lats1, intmat1)\n",
-    "\n",
-    "    fig, axs = plt.subplots(4, subplot_kw={'projection': proj, 'transform': transf})\n",
-    "    # plt.figure()\n",
-    "    # plt.subplot(projection=proj,transform=transf)\n",
-    "    ax = axs[0]\n",
-    "    # ax.set_global()\n",
-    "    ax.coastlines()\n",
-    "\n",
-    "    plot1(None, None, lons0, lats0, triang0, c0='r', c1='b', transf=transf, ax=ax)\n",
-    "    plot1(None, None, lons1, lats1, triang1, c0='c', c1='r', transf=transf, ax=ax)\n",
-    "    # plt.show()\n",
-    "\n",
-    "    intersectedFalse = pystare.intersection(hull0, hull1, multi_resolution=False)\n",
-    "    # print('intersectedFalse: ',intersectedFalse)\n",
-    "\n",
-    "    intersectedTrue = pystare.intersection(hull0, hull1, multi_resolution=True)\n",
-    "\n",
-    "    # plt.figure()\n",
-    "    # plt.subplot(projection=proj,transform=transf)\n",
-    "    ax = axs[1]\n",
-    "    # ax.set_global()\n",
-    "    ax.coastlines()\n",
-    "\n",
-    "    lonsF, latsF, intmatF = pystare.triangulate_indices(intersectedFalse)\n",
-    "    triangF = tri.Triangulation(lonsF, latsF, intmatF)\n",
-    "    plot1(None, None, lonsF, latsF, triangF, c0='r', c1='b', transf=transf, ax=ax)\n",
-    "    # plt.show()\n",
-    "\n",
-    "    # plt.figure()\n",
-    "    # plt.subplot(projection=proj,transform=transf)\n",
-    "    ax = axs[2]\n",
-    "    # ax.set_global()\n",
-    "    ax.coastlines()\n",
-    "\n",
-    "    lonsT, latsT, intmatT = pystare.triangulate_indices(intersectedTrue)\n",
-    "    triangT = tri.Triangulation(lonsT, latsT, intmatT)\n",
-    "    plot1(None, None, lonsT, latsT, triangT, c0='r', c1='b', transf=transf, ax=ax)\n",
-    "    # plt.show()\n",
-    "\n",
-    "    ## ok ## print('   len(False),len(True),delta: ',len(intersectedFalse),len(intersectedTrue),len(intersectedTrue)-len(intersectedFalse))\n",
-    "    ## ok ## print('un len(False),len(True),delta: ',len(numpy.unique(intersectedFalse)),len(numpy.unique(intersectedTrue)),len(numpy.unique(intersectedTrue))-len(numpy.unique(intersectedFalse)))\n",
-    "    ## ok ## print('compressed==True, first total, then w/o double counting: ',(7*16)+(17*4),(7*16)+(17*4)-(7+17))\n",
-    "    # print('count 0:    ',sum([1,34,34,4*6,24,22,9]))\n",
-    "\n",
-    "    if True:\n",
-    "        r0 = pystare.core.srange()\n",
-    "        r0.add_intervals(hull0)\n",
-    "\n",
-    "        r1 = pystare.core.srange()\n",
-    "        r1.add_intervals(hull1)\n",
-    "\n",
-    "        r01 = pystare.core.srange()\n",
-    "        r01.add_intersect(r0, r1, False)\n",
-    "        n01 = r01.get_size_as_values()\n",
-    "\n",
-    "        intersected = numpy.zeros([n01], dtype=numpy.int64)\n",
-    "        r01.copy_values(intersected)\n",
-    "        # See examples/test_intersect_single_res.py\n",
-    "\n",
-    "        r01.purge()\n",
-    "        n01 = r01.get_size_as_values()\n",
-    "\n",
-    "        r01.reset()\n",
-    "        r01.add_intersect(r0, r1, True)\n",
-    "        # n01 = r01.get_size_as_values()\n",
-    "        # n01 = r01.get_size_as_values_multi_resolution(False)\n",
-    "        n01 = r01.get_size_as_values()\n",
-    "\n",
-    "        print('r01 n01: ', n01)\n",
-    "\n",
-    "        intersected = numpy.zeros([n01], dtype=numpy.int64)\n",
-    "        r01.copy_values(intersected)\n",
-    "\n",
-    "        ###??? Would intervals be different?\n",
-    "\n",
-    "        lonsRT, latsRT, intmatRT = pystare.triangulate_indices(intersected)\n",
-    "        triangRT = tri.Triangulation(lonsRT, latsRT, intmatRT)\n",
-    "\n",
-    "        # fig, axs = plt.subplots(3,subplot_kw={'projection':proj,'transform':transf})\n",
-    "\n",
-    "        # plt.figure()\n",
-    "        # plt.subplot(projection=proj,transform=transf)\n",
-    "        ax = axs[3]\n",
-    "        # ax.set_global()\n",
-    "        ax.coastlines()\n",
-    "        # plot1(None,None,lonsRT,latsRT,triangRT,c0='r',c1='b',transf=transf,ax=ax)\n",
-    "\n",
-    "        lonsRT_, latsRT_, intmatRT_ = pystare.triangulate_indices(intersected[51:55])\n",
-    "        triangRT_ = tri.Triangulation(lonsRT_, latsRT_, intmatRT_)\n",
-    "\n",
-    "        # k = 51\n",
-    "        # for j in intersected[51:55]:\n",
-    "        #     print(k,' siv: 0x%016x'%intersected[k])\n",
-    "        #     k += 1\n",
-    "\n",
-    "        plot1(None, None, lonsRT_, latsRT_, triangRT_, c0='g', c1='g', transf=transf, ax=ax)\n",
-    "\n",
-    "        print('len(intersected): ', len(intersected))\n",
-    "\n",
-    "    plt.show()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/tests/intervals.py b/tests/data/intervals.py
similarity index 100%
rename from tests/intervals.py
rename to tests/data/intervals.py
diff --git a/tests/sids.py b/tests/data/sids.py
similarity index 100%
rename from tests/sids.py
rename to tests/data/sids.py
diff --git a/tests/test_compress.py b/tests/test_compress.py
new file mode 100644
index 0000000..b2df537
--- /dev/null
+++ b/tests/test_compress.py
@@ -0,0 +1,56 @@
+import pystare
+import numpy
+
+
+sids = numpy.array([2521902129312956427, 2521902404190863371, 2521899930289700875,
+       2521900067728654347, 2521900205167607819, 2521991876949573643,
+       2521992701583294475, 2521992839022247947, 2521992976461201419,
+       2521997099629805579, 2521889484929237003, 2521991739510620171,
+       2521902679068770315, 2521908726382723083, 2521894432731561995,
+       2521901579557142539, 2521989952804225035, 2521990365121085451,
+       2521991327193759755, 2521997237068759051, 2521997511946665995,
+       2521898418461212683, 2521898555900166155, 2521898693339119627,
+       2521889759807143947, 2521903091385630731, 2521901716996096011,
+       2521897456388538379, 2521902266751909899, 2521897868705398795,
+       2521898968217026571, 2521990227682131979, 2521889622368190475,
+       2521901167240282123, 2521887973100748811, 2521902816507723787,
+       2521892920903073803, 2521894020414701579, 2521890721879818251,
+       2521993526217015307, 2521901304679235595, 2521901854435049483,
+       2521902953946677259, 2521898006144352267, 2521890859318771723,
+       2521886461272260619, 2521888110539702283, 2521898830778073099,
+       2521903228824584203, 2521897044071677963, 2521903366263537675,
+       2521899105655980043, 2521903503702491147, 2521886048955400203,
+       2521991602071666699, 2521899380533886987, 2521890584440864779,
+       2521992014388527115, 2521886873589121035, 2521887011028074507,
+       2521889347490283531, 2521890447001911307, 2521886736150167563,
+       2521893745536794635, 2521893882975748107, 2521894157853655051,
+       2521897181510631435, 2521897731266445323, 2521899792850747403,
+       2521887698222841867, 2521900892362375179, 2521901442118189067,
+       2521906389920514059, 2521908588943769611, 2521901991874002955,
+       2521902541629816843, 2521909001260630027, 2521989540487364619,
+       2521897318949584907, 2521898143583305739, 2521898281022259211,
+       2521897593827491851, 2521993113900154891, 2521993251339108363,
+       2521993388778061835, 2521993663655968779, 2521886598711214091,
+       2521886186394353675])
+
+expanded_stare_test = numpy.array([0x22ff8a000000000b, 0x22ff8a200000000b,
+        0x22ff8a600000000b, 0x22ff8a800000000b,0x22ff8aa00000000b,
+        0x22ff8ac00000000b,0x22ff8ae00000000b,0x22ff8b800000000b,
+        0x22ff8bc00000000b,0x22ff8be00000000b,0x22ff8d000000000a,
+        0x22ff8e000000000a,0x22ff90400000000b,0x22ff91000000000a,
+        0x22ff91a00000000b,0x22ff940000000009,0x22ff96200000000b,
+        0x22ff96800000000a,0x22ff97800000000b,0x22ff97c00000000b,
+        0x22ff97e00000000b,0x22ff980000000009,0x22ff9c800000000b,
+        0x22ff9e800000000b,0x22ff9ea00000000b,0x22ff9ee00000000b,
+        0x22ffe8200000000b,0x22ffe8800000000b,0x22ffe8c00000000b,
+        0x22ffe8e00000000b,0x22ffe9c00000000b,0x22ffea000000000a,
+        0x22ffeb000000000a,0x22ffeb800000000a,0x22ffef000000000b,
+        0x22ffef200000000b,0x22ffef600000000b
+        ])
+
+
+def test_bug27():
+    compressed = pystare.to_compressed_range(sids)
+    expanded = pystare.expand_intervals(compressed, -1, False)
+    numpy.testing.assert_array_equal(expanded, expanded_stare_test)
+
diff --git a/tests/test_intersect_single_res.py b/tests/test_intersect_single_res.py
index 8710013..54209ad 100644
--- a/tests/test_intersect_single_res.py
+++ b/tests/test_intersect_single_res.py
@@ -1,7 +1,6 @@
 # Visualize test_intersect_single_res seen in test_intersections.py
 
 import pystare
-import matplotlib.tri
 import numpy
 
 
@@ -12,68 +11,35 @@ def test_intersect_single_res():
     lon0 = numpy.array([-30, -20, 60, 10], dtype=numpy.double)
     hull0 = pystare.cover_from_hull(lat0, lon0, resolution0)
 
-    lons0, lats0, intmat0 = pystare.triangulate_indices(hull0)
-    triang0 = matplotlib.tri.Triangulation(lons0, lats0, intmat0)
-
     resolution1 = 6
     lat1 = numpy.array([10, 20, 30, 20], dtype=numpy.double)
     lon1 = numpy.array([-60, 60, 60, -60], dtype=numpy.double)
     hull1 = pystare.cover_from_hull(lat1, lon1, resolution1)
 
-    lons1, lats1, intmat1 = pystare.triangulate_indices(hull1)
-    triang1 = matplotlib.tri.Triangulation(lons1, lats1, intmat1)
-
-    intersectedFalse = pystare.intersection(hull0, hull1, multi_resolution=False)
-    intersectedTrue = pystare.intersection(hull0, hull1, multi_resolution=True)
-
-    lonsF, latsF, intmatF = pystare.triangulate_indices(intersectedFalse)
-    triangF = matplotlib.tri.Triangulation(lonsF, latsF, intmatF)
-
-    lonsT, latsT, intmatT = pystare.triangulate_indices(intersectedTrue)
-    triangT = matplotlib.tri.Triangulation(lonsT, latsT, intmatT)
-
-    if True:
-        r0 = pystare.core.srange()
-        r0.add_intervals(hull0)
-
-        r1 = pystare.core.srange()
-        r1.add_intervals(hull1)
-
-        r01 = pystare.core.srange()
-        r01.add_intersect(r0, r1, False)
-        n01 = r01.get_size_as_values()
-
-        assert n01 == 77
-
-        intersected = numpy.zeros([n01], dtype=numpy.int64)
-        r01.copy_values(intersected)
-        # See examples/test_intersect_single_res.py
-
-        assert len(intersected) == 77
-
-        r01.purge()
-        n01 = r01.get_size_as_values()
-
-        assert 0 == n01
-
-        r01.reset()
-        r01.add_intersect(r0, r1, True)
-
-        n01 = r01.get_size_as_values()
-        assert n01 == 79
-
-        intersected = numpy.zeros([n01], dtype=numpy.int64)
-        r01.copy_values(intersected)
+    r0 = pystare.core.srange()
+    r0.add_intervals(hull0)
 
-        lonsRT, latsRT, intmatRT = pystare.triangulate_indices(intersected)
-        triangRT = matplotlib.tri.Triangulation(lonsRT, latsRT, intmatRT)
+    r1 = pystare.core.srange()
+    r1.add_intervals(hull1)
 
+    r01 = pystare.core.srange()
+    r01.add_intersect(r0, r1, False)
+    n01 = r01.get_size_as_values()
 
-        lonsRT_, latsRT_, intmatRT_ = pystare.triangulate_indices(intersected[51:55])
-        triangRT_ = matplotlib.tri.Triangulation(lonsRT_, latsRT_, intmatRT_)
+    assert n01 == 77
 
+    intersected = numpy.zeros([n01], dtype=numpy.int64)
+    r01.copy_values(intersected)
 
+    assert len(intersected) == 77
 
+    r01.purge()
+    n01 = r01.get_size_as_values()
 
+    assert 0 == n01
 
+    r01.reset()
+    r01.add_intersect(r0, r1, True)
 
+    n01 = r01.get_size_as_values()
+    assert n01 == 79
diff --git a/tests/test_intersections.py b/tests/test_intersections.py
index 9f0acd5..5fe7b30 100644
--- a/tests/test_intersections.py
+++ b/tests/test_intersections.py
@@ -6,14 +6,6 @@
 lon = numpy.array([45, 60, 10], dtype=numpy.double)
 
 
-def shiftarg_lon(lon):
-    "If lon is outside +/-180, then correct back."
-    if lon > 180:
-        return ((lon + 180.0) % 360.0)-180.0
-    else:
-        return lon
-
-
 def triangulate(i0, i1, i2):
     "Prepare data structures for tri.Triangulate."
     # print('triangulating...')
@@ -35,7 +27,7 @@ def triangulate(i0, i1, i2):
         intmat.append([k, k+1, k+2])
         k=k+3
     for i in range(len(lons)):
-        lons[i] = shiftarg_lon(lons[i])
+        lons[i] = pystare.lon_wrap_180(lons[i])
         # print('triangulating done.')      
     return lons, lats, intmat
 
@@ -50,7 +42,7 @@ def triangulate1(lats,lons):
         intmat.append([k, k+1, k+2])
         k = k+3
     for i in range(len(lons)):
-        lons[i] = shiftarg_lon(lons[i])
+        lons[i] = pystare.lon_wrap_180(lons[i])
     # print('triangulating1 done.')      
     return lons, lats, intmat
 
diff --git a/tests/test_spatial.py b/tests/test_spatial.py
index 70ba1fb..41edfe8 100644
--- a/tests/test_spatial.py
+++ b/tests/test_spatial.py
@@ -1,19 +1,21 @@
-import numpy 
-import pystare 
+import numpy
+import pystare
 import unittest
 
 
 class MainTest(unittest.TestCase):
-    
+
     def test_fromlatlon(self):
         lat = numpy.array([30, 45, 60], dtype=numpy.double)
         lon = numpy.array([45, 60, 10], dtype=numpy.double)
         indices = pystare.from_latlon(lat, lon, 12)
         expected = numpy.array([4151504989081014892, 4161865161846704588, 3643626718498217164])
         numpy.testing.assert_array_equal(indices, expected)
-        
+
     def test_intervals(self):
-        a = numpy.array([0x0000000000000008, 0x000030000000000a, 0x000067ffffffffff, 0x000070000000000a, 0x0000907fffffffff], dtype=numpy.int64)
+        a = numpy.array(
+            [0x0000000000000008, 0x000030000000000a, 0x000067ffffffffff, 0x000070000000000a, 0x0000907fffffffff],
+            dtype=numpy.int64)
         starts, ends = pystare.from_intervals(a)
         self.assertEqual(starts[0], 8)
         self.assertEqual(ends[0], 8796093022207)
@@ -21,58 +23,58 @@ def test_intervals(self):
     def test_tolatlon(self):
         indices = numpy.array([4151504989081014892, 4161865161846704588, 3643626718498217164])
         lat, lon = pystare.to_latlon(indices)
-        numpy.testing.assert_allclose(lat, numpy.array([30,45,60]), verbose=True)
-        numpy.testing.assert_allclose(lon, numpy.array([45,60,10]), verbose=True)
-        
+        numpy.testing.assert_allclose(lat, numpy.array([30, 45, 60]), verbose=True)
+        numpy.testing.assert_allclose(lon, numpy.array([45, 60, 10]), verbose=True)
+
     def test_tolatlonlevel(self):
         indices = numpy.array([4151504989081014892, 4161865161846704588, 3643626718498217164])
         lat, lon, level = pystare.to_latlonlevel(indices)
-        numpy.testing.assert_allclose(lat, numpy.array([30,45,60]), verbose=True)
-        numpy.testing.assert_allclose(lat, numpy.array([30,45,60]), verbose=True)
-        numpy.testing.assert_allclose(level, numpy.array([12,12,12]), verbose=True)
-        
+        numpy.testing.assert_allclose(lat, numpy.array([30, 45, 60]), verbose=True)
+        numpy.testing.assert_allclose(lat, numpy.array([30, 45, 60]), verbose=True)
+        numpy.testing.assert_allclose(level, numpy.array([12, 12, 12]), verbose=True)
+
     def test_latlonroundtrip(self):
         lat1 = numpy.array([30, 45, 60], dtype=numpy.double)
         lon1 = numpy.array([45, 60, 10], dtype=numpy.double)
         level1 = 12
-        indices = pystare.from_latlon(lat1, lon1, level1)        
+        indices = pystare.from_latlon(lat1, lon1, level1)
         lat2, lon2, level2 = pystare.to_latlonlevel(indices)
         numpy.testing.assert_allclose(lat1, lat2)
         numpy.testing.assert_allclose(lon1, lon2)
         self.assertEqual(level1, level2[0])
-                
+
     def test_tolevel(self):
         indices = numpy.array([4151504989081014892, 4161865161846704588, 3643626718498217164])
         level = pystare.to_level(indices)
-        numpy.testing.assert_array_equal(level, numpy.array([12,12,12]), verbose=True)
+        numpy.testing.assert_array_equal(level, numpy.array([12, 12, 12]), verbose=True)
 
     def test_toarea(self):
         indices = numpy.array([4151504989081014892, 4161865161846704588, 3643626718498217164])
         area = pystare.to_area(indices)
-        expected = numpy.array([8.66507750e-08, 8.74786654e-08, 7.97819113e-08])        
+        expected = numpy.array([8.66507750e-08, 8.74786654e-08, 7.97819113e-08])
         numpy.testing.assert_allclose(area, expected)
-    
+
     def test__tocompressedrange(self):
-        
+
         # indices = numpy.array([4151504989081014892, 4161865161846704588, 3643626718498217164])
         # expected = numpy.array([3643626718498217164, 4151504989081014892, 4161865161846704588])
 
-        indices  = numpy.array([0x399d1bcabd6f926c, 0x39c1ea506ef249cc,0x3290c3321a355ccc])
-        expected = numpy.array([0x3290c3300000000c, 0x399d1bc80000000c,0x39c1ea500000000c])
+        indices = numpy.array([0x399d1bcabd6f926c, 0x39c1ea506ef249cc, 0x3290c3321a355ccc])
+        expected = numpy.array([0x3290c3300000000c, 0x399d1bc80000000c, 0x39c1ea500000000c])
 
         compressed = numpy.array([0, 0, 0], dtype=numpy.int64)
         pystare.core._to_compressed_range(indices, compressed)
-            
+
         numpy.testing.assert_array_equal(compressed, expected)
-    
+
     def test_tocompressedrange(self):
 
         # indices = numpy.array([4151504989081014892, 4161865161846704588, 3643626718498217164])
         # expected = numpy.array([3643626718498217164, 4151504989081014892, 4161865161846704588])
 
-        indices  = numpy.array([0x399d1bcabd6f926c,0x39c1ea506ef249cc,0x3290c3321a355ccc])
-        expected = numpy.array([0x3290c3300000000c,0x399d1bc80000000c,0x39c1ea500000000c])
-        
+        indices = numpy.array([0x399d1bcabd6f926c, 0x39c1ea506ef249cc, 0x3290c3321a355ccc])
+        expected = numpy.array([0x3290c3300000000c, 0x399d1bc80000000c, 0x39c1ea500000000c])
+
         compressed = pystare.to_compressed_range(indices)
 
         # print('')
@@ -85,7 +87,7 @@ def test_tocompressedrange(self):
         # print('\ncompressed')
         # for i in [0,1,2]:
         #     print(hex(compressed[i]))
-        
+
         numpy.testing.assert_array_equal(compressed, expected)
 
     def test__tohullrange(self):
@@ -111,18 +113,18 @@ def test__cmpspatial(self):
     def test_cmpspatial(self):
         indices1 = numpy.array([4151504989081014892, 4161865161846704588, 3643626718498217164])
         indices2 = numpy.zeros([2], dtype=numpy.int64)
-        indices2[0] = indices1[1]-2
+        indices2[0] = indices1[1] - 2
         indices2[1] = indices1[1]
         compared = pystare.cmp_spatial(indices1, indices2)
-        expected = numpy.array([0,  0, -1,  1,  0,  0])
+        expected = numpy.array([0, 0, -1, 1, 0, 0])
         numpy.testing.assert_array_equal(compared, expected)
-        
+
     def test__expand(self):
-        import tests.intervals as intervals
-        src                = numpy.array(intervals.src, dtype=numpy.int64)
-        expected_expanded  = numpy.array(intervals.expanded_src, dtype=numpy.int64)
-        expanded           = numpy.zeros([len(expected_expanded)], dtype=numpy.int64)
-        expanded_len       = numpy.zeros([1], dtype=numpy.int64)
+        import tests.data.intervals as intervals
+        src = numpy.array(intervals.src, dtype=numpy.int64)
+        expected_expanded = numpy.array(intervals.expanded_src, dtype=numpy.int64)
+        expanded = numpy.zeros([len(expected_expanded)], dtype=numpy.int64)
+        expanded_len = numpy.zeros([1], dtype=numpy.int64)
         intervals_len = len(src)
         resolution = -1
         result = pystare.core._expand_intervals(src, resolution, multi_resolution=False)
@@ -132,11 +134,11 @@ def test__expand(self):
         error_found = False
         for i in range(len(expanded)):
             if expanded[i] != expected_expanded[i]:
-                error_found = True    
+                error_found = True
         self.assertFalse(error_found)
-        
+
     def test_expand(self):
-        import tests.intervals as intervals
+        import tests.data.intervals as intervals
         src = numpy.array(intervals.src, dtype=numpy.int64)
         expected_expanded = numpy.array(intervals.expanded_src, dtype=numpy.int64)
         resolution = -1
@@ -146,21 +148,20 @@ def test_expand(self):
             if expanded[i] != expected_expanded[i]:
                 error_found = True
         self.assertFalse(error_found)
-  
+
     def test_cover(self):
-        expected = [(0,     4430603050402447369 ),
-                    (295,   4430595237856935950 ),
-                    (590,   4430626891765907470 ),
-                    (885,   4430691958372958222 ),
-                    (1180,  4430869121478950926 )]
-        cover = pystare.to_circular_cover(1.5,0.5,0.25,14)
+        expected = [(0, 4430603050402447369),
+                    (295, 4430595237856935950),
+                    (590, 4430626891765907470),
+                    (885, 4430691958372958222),
+                    (1180, 4430869121478950926)]
+        cover = pystare.to_circular_cover(1.5, 0.5, 0.25, 14)
         for i in list(expected):
             self.assertEqual(i[1], cover[i[0]])
 
     def test_spatial_resolution_from_km(self):
-        self.assertEqual(10,int(pystare.spatial_resolution_from_km(10)))
+        self.assertEqual(10, int(pystare.spatial_resolution_from_km(10)))
 
     def test_spatial_resolution(self):
-        self.assertEqual(11,int(pystare.spatial_resolution(11)))
-        self.assertEqual(15,int(pystare.spatial_resolution(15)))
-        
+        self.assertEqual(11, int(pystare.spatial_resolution(11)))
+        self.assertEqual(15, int(pystare.spatial_resolution(15)))
diff --git a/tests/test_srange.py b/tests/test_srange.py
index 3346f39..0ed3a39 100644
--- a/tests/test_srange.py
+++ b/tests/test_srange.py
@@ -1,11 +1,10 @@
-import numpy 
+import numpy
 import pystare 
 import unittest
 
 
 class MainTest(unittest.TestCase):
 
-
     def test_bug38(self):
         #https://github.com/SpatioTemporal/pystare/issues/38
         sids = numpy.array([0x0, 0x3f08000000000005], dtype=numpy.int64)
@@ -13,18 +12,18 @@ def test_bug38(self):
     
     def test_make_srange1000(self):
         try:
-            from sids import sids
+            from tests.data.sids import sids
         except:
-            from tests.sids import sids
+            from tests.data.sids import sids
             pass
         sids = sids[0:1000]
         srange = pystare.core.srange(sids)
         
     def test_make_srange10000(self):
         try:
-            from sids import sids
+            from tests.data.sids import sids
         except:
-            from tests.sids import sids
+            from tests.data.sids import sids
             pass
         sids = sids[0:10000]
         srange = pystare.core.srange(sids)
@@ -80,7 +79,5 @@ def test_multires_intersect2(self):
             self.assertEqual(ivs.size, 1)
         with self.subTest():    
             self.assertEqual(hex(ivs[0]), '0x300a300000000009')
-        
-if __name__ == "__main__":
-    unittest.main()
+