diff --git a/.cache-1241528689 b/.cache-1241528689
index 77c84bc..b72c30b 100644
--- a/.cache-1241528689
+++ b/.cache-1241528689
@@ -1 +1 @@
-{"access_token": "BQCXOeVx3tqRXMnJYZvyArG-IeXrr2w9JYbTNffOmHY0n1Sw2xsAse8KUJH193GwRDJnpAlylbrNwJzqfBQMvFOqHF1dgOBrovrPe2Udp3wWHCvs6k55gplLE4BlTjDH7IQdfpM-DSeHqfCnhndyqfIH80uTNWseiFgRsfjTMjUV9vysotl3np0uefImINLMLdhDrebuiOrjO_OvRxWl9lQ", "token_type": "Bearer", "expires_in": 3600, "scope": "playlist-modify-private playlist-modify-public user-follow-modify user-follow-read", "expires_at": 1618268185, "refresh_token": "AQAsxkWjXR0Iw8q65vbKmXUR0cOGEM8liRshm9vhsJbDenCcjijwBgyKF91oCqQ8NjdD8fwk3uO-NKGUVWYtWRF0E2f5ydGSyFlJRi29TR1Zyw71OKdaIs89XzUBfCOOO0M"}
\ No newline at end of file
+{"access_token": "BQDEW_X1QUACQIUptws4nQkpzMw_9xGpqPDoWtE2JLfMMjuXC_aS8cG_v9igpKNN5Wl37IQOk0Fe0LjK4g-GPATYPacGQKlO19jbOaS4Ey9heYvHaBJnNx92kwsnhf0WjqitLNrStbI9ITLYBPpumdf0hanX2O3i6A1HczgzaNZ4Qx6mc80YsOCukJo41tmyH0u1_FxhtLyTCt42Bm3eQRA", "token_type": "Bearer", "expires_in": 3600, "scope": "playlist-modify-private playlist-modify-public", "expires_at": 1619468878, "refresh_token": "AQAsxkWjXR0Iw8q65vbKmXUR0cOGEM8liRshm9vhsJbDenCcjijwBgyKF91oCqQ8NjdD8fwk3uO-NKGUVWYtWRF0E2f5ydGSyFlJRi29TR1Zyw71OKdaIs89XzUBfCOOO0M"}
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index e46cd3b..8670efd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,9 +4,149 @@ storm/Storm/Storm.mdproj
 storm/config/config_secret.json
 
 *.env
+*.cache
 
 .idea
 .vscode
 .ipynb_checkpoints
 
 token.json
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
\ No newline at end of file
diff --git a/Pipfile.lock b/Pipfile.lock
index daa7e9e..0228f25 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -16,14 +16,6 @@
         ]
     },
     "default": {
-        "appnope": {
-            "hashes": [
-                "sha256:93aa393e9d6c54c5cd570ccadd8edad61ea0c4b9ea7a01409020c9aa019eb442",
-                "sha256:dd83cd4b5b460958838f6eb3000c660b1f9caf2a5b1de4264e941512f603258a"
-            ],
-            "markers": "sys_platform == 'darwin' and platform_system == 'Darwin'",
-            "version": "==0.1.2"
-        },
         "argon2-cffi": {
             "hashes": [
                 "sha256:05a8ac07c7026542377e38389638a8a1e9b78f1cd8439cd7493b39f08dd75fbf",
@@ -135,13 +127,21 @@
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
             "version": "==4.0.0"
         },
+        "colorama": {
+            "hashes": [
+                "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b",
+                "sha256:9f47eda37229f68eee03b24b9748937c7dc3868f906e8ba69fbcbdd3bc5dc3e2"
+            ],
+            "markers": "sys_platform == 'win32'",
+            "version": "==0.4.4"
+        },
         "decorator": {
             "hashes": [
-                "sha256:d9f2d2863183a3c0df05f4b786f2e6b8752c093b3547a558f287bf3022fd2bf4",
-                "sha256:f2e71efb39412bfd23d878e896a51b07744f2e2250b2e87d158e76828c5ae202"
+                "sha256:6f201a6c4dac3d187352661f508b9364ec8091217442c9478f1f83c003a0f060",
+                "sha256:945d84890bb20cc4a2f4a31fc4311c0c473af65ea318617f13a7257c9a58bc98"
             ],
             "markers": "python_version >= '3.5'",
-            "version": "==5.0.6"
+            "version": "==5.0.7"
         },
         "defusedxml": {
             "hashes": [
@@ -421,25 +421,25 @@
         },
         "pandas": {
             "hashes": [
-                "sha256:09761bf5f8c741d47d4b8b9073288de1be39bbfccc281d70b889ade12b2aad29",
-                "sha256:0f27fd1adfa256388dc34895ca5437eaf254832223812afd817a6f73127f969c",
-                "sha256:43e00770552595c2250d8d712ec8b6e08ca73089ac823122344f023efa4abea3",
-                "sha256:46fc671c542a8392a4f4c13edc8527e3a10f6cb62912d856f82248feb747f06e",
-                "sha256:475b7772b6e18a93a43ea83517932deff33954a10d4fbae18d0c1aba4182310f",
-                "sha256:4d821b9b911fc1b7d428978d04ace33f0af32bb7549525c8a7b08444bce46b74",
-                "sha256:5e3c8c60541396110586bcbe6eccdc335a38e7de8c217060edaf4722260b158f",
-                "sha256:621c044a1b5e535cf7dcb3ab39fca6f867095c3ef223a524f18f60c7fee028ea",
-                "sha256:72ffcea00ae8ffcdbdefff800284311e155fbb5ed6758f1a6110fc1f8f8f0c1c",
-                "sha256:8a051e957c5206f722e83f295f95a2cf053e890f9a1fba0065780a8c2d045f5d",
-                "sha256:97b1954533b2a74c7e20d1342c4f01311d3203b48f2ebf651891e6a6eaf01104",
-                "sha256:9f5829e64507ad10e2561b60baf285c470f3c4454b007c860e77849b88865ae7",
-                "sha256:a93e34f10f67d81de706ce00bf8bb3798403cabce4ccb2de10c61b5ae8786ab5",
-                "sha256:d59842a5aa89ca03c2099312163ffdd06f56486050e641a45d926a072f04d994",
-                "sha256:dbb255975eb94143f2e6ec7dadda671d25147939047839cd6b8a4aff0379bb9b",
-                "sha256:df6f10b85aef7a5bb25259ad651ad1cc1d6bb09000595cab47e718cbac250b1d"
+                "sha256:167693a80abc8eb28051fbd184c1b7afd13ce2c727a5af47b048f1ea3afefff4",
+                "sha256:2111c25e69fa9365ba80bbf4f959400054b2771ac5d041ed19415a8b488dc70a",
+                "sha256:298f0553fd3ba8e002c4070a723a59cdb28eda579f3e243bc2ee397773f5398b",
+                "sha256:2b063d41803b6a19703b845609c0b700913593de067b552a8b24dd8eeb8c9895",
+                "sha256:2cb7e8f4f152f27dc93f30b5c7a98f6c748601ea65da359af734dd0cf3fa733f",
+                "sha256:52d2472acbb8a56819a87aafdb8b5b6d2b3386e15c95bde56b281882529a7ded",
+                "sha256:612add929bf3ba9d27b436cc8853f5acc337242d6b584203f207e364bb46cb12",
+                "sha256:649ecab692fade3cbfcf967ff936496b0cfba0af00a55dfaacd82bdda5cb2279",
+                "sha256:68d7baa80c74aaacbed597265ca2308f017859123231542ff8a5266d489e1858",
+                "sha256:8d4c74177c26aadcfb4fd1de6c1c43c2bf822b3e0fc7a9b409eeaf84b3e92aaa",
+                "sha256:971e2a414fce20cc5331fe791153513d076814d30a60cd7348466943e6e909e4",
+                "sha256:9db70ffa8b280bb4de83f9739d514cd0735825e79eef3a61d312420b9f16b758",
+                "sha256:b730add5267f873b3383c18cac4df2527ac4f0f0eed1c6cf37fcb437e25cf558",
+                "sha256:bd659c11a4578af740782288cac141a322057a2e36920016e0fc7b25c5a4b686",
+                "sha256:c601c6fdebc729df4438ec1f62275d6136a0dd14d332fc0e8ce3f7d2aadb4dd6",
+                "sha256:d0877407359811f7b853b548a614aacd7dea83b0c0c84620a9a643f180060950"
             ],
             "index": "pypi",
-            "version": "==1.2.3"
+            "version": "==1.2.4"
         },
         "pandocfilters": {
             "hashes": [
@@ -455,14 +455,6 @@
             "markers": "python_version >= '3.6'",
             "version": "==0.8.2"
         },
-        "pexpect": {
-            "hashes": [
-                "sha256:0b48a55dcb3c05f3329815901ea4fc1537514d6ba867a152b581d69ae3710937",
-                "sha256:fc65a43959d153d0114afe13997d439c22823a27cefceb5ff35c2178c6784c0c"
-            ],
-            "markers": "sys_platform != 'win32'",
-            "version": "==4.8.0"
-        },
         "pickleshare": {
             "hashes": [
                 "sha256:87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca",
@@ -486,14 +478,6 @@
             "markers": "python_full_version >= '3.6.1'",
             "version": "==3.0.18"
         },
-        "ptyprocess": {
-            "hashes": [
-                "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35",
-                "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"
-            ],
-            "markers": "os_name != 'nt'",
-            "version": "==0.7.0"
-        },
         "pycparser": {
             "hashes": [
                 "sha256:2d475327684562c3a96cc71adf7dc8c4f0565175cf86b6d7a404ff4c771f15f0",
@@ -626,6 +610,38 @@
             ],
             "version": "==2021.1"
         },
+        "pywin32": {
+            "hashes": [
+                "sha256:1c204a81daed2089e55d11eefa4826c05e604d27fe2be40b6bf8db7b6a39da63",
+                "sha256:27a30b887afbf05a9cbb05e3ffd43104a9b71ce292f64a635389dbad0ed1cd85",
+                "sha256:350c5644775736351b77ba68da09a39c760d75d2467ecec37bd3c36a94fbed64",
+                "sha256:60a8fa361091b2eea27f15718f8eb7f9297e8d51b54dbc4f55f3d238093d5190",
+                "sha256:638b68eea5cfc8def537e43e9554747f8dee786b090e47ead94bfdafdb0f2f50",
+                "sha256:8151e4d7a19262d6694162d6da85d99a16f8b908949797fd99c83a0bfaf5807d",
+                "sha256:a3b4c48c852d4107e8a8ec980b76c94ce596ea66d60f7a697582ea9dce7e0db7",
+                "sha256:b1609ce9bd5c411b81f941b246d683d6508992093203d4eb7f278f4ed1085c3f",
+                "sha256:d7e8c7efc221f10d6400c19c32a031add1c4a58733298c09216f57b4fde110dc",
+                "sha256:fbb3b1b0fbd0b4fc2a3d1d81fe0783e30062c1abed1d17c32b7879d55858cfae"
+            ],
+            "markers": "sys_platform == 'win32'",
+            "version": "==300"
+        },
+        "pywinpty": {
+            "hashes": [
+                "sha256:1e525a4de05e72016a7af27836d512db67d06a015aeaf2fa0180f8e6a039b3c2",
+                "sha256:2740eeeb59297593a0d3f762269b01d0285c1b829d6827445fcd348fb47f7e70",
+                "sha256:2d7e9c881638a72ffdca3f5417dd1563b60f603e1b43e5895674c2a1b01f95a0",
+                "sha256:33df97f79843b2b8b8bc5c7aaf54adec08cc1bae94ee99dfb1a93c7a67704d95",
+                "sha256:5fb2c6c6819491b216f78acc2c521b9df21e0f53b9a399d58a5c151a3c4e2a2d",
+                "sha256:8fc5019ff3efb4f13708bd3b5ad327589c1a554cb516d792527361525a7cb78c",
+                "sha256:b358cb552c0f6baf790de375fab96524a0498c9df83489b8c23f7f08795e966b",
+                "sha256:dbd838de92de1d4ebf0dce9d4d5e4fc38d0b7b1de837947a18b57a882f219139",
+                "sha256:dd22c8efacf600730abe4a46c1388355ce0d4ab75dc79b15d23a7bd87bf05b48",
+                "sha256:e854211df55d107f0edfda8a80b39dfc87015bef52a8fe6594eb379240d81df2"
+            ],
+            "markers": "os_name == 'nt'",
+            "version": "==0.5.7"
+        },
         "pyzmq": {
             "hashes": [
                 "sha256:13465c1ff969cab328bc92f7015ce3843f6e35f8871ad79d236e4fbc85dbe4cb",
@@ -704,11 +720,11 @@
         },
         "spotipy": {
             "hashes": [
-                "sha256:1164f4bb327a2b98492a020d120f095dafcdb86e7f99ad2fdfb5bdd95eb4493a",
-                "sha256:29c60c8b99da1c4b9f0d722169bc31e624b8c07d7186b8eadd9c02e8d2d42cbf"
+                "sha256:8acbc18dd44e1c22b3da500ca9225c5d2f7476f2e68d5d56a317b0b8c87ec8a5",
+                "sha256:f7293b808696807e9acec6bdcff63f7dcc3cc1b148c0c4b4299ef43c966f7177"
             ],
             "index": "pypi",
-            "version": "==2.17.1"
+            "version": "==2.18.0"
         },
         "terminado": {
             "hashes": [
diff --git a/Storm.ipynb b/Storm.ipynb
deleted file mode 100644
index 7737a7f..0000000
--- a/Storm.ipynb
+++ /dev/null
@@ -1,410 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-06T17:45:33.961856Z",
-     "start_time": "2020-05-06T17:45:33.958404Z"
-    },
-    "code_folding": []
-   },
-   "outputs": [],
-   "source": [
-    "# Imports\n",
-    "from src.utils import Storm\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "#import matplotlib.pyplot as plt\n",
-    "import datetime as dt\n",
-    "import time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Storm Run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-06T17:56:06.578188Z",
-     "start_time": "2020-05-06T17:49:10.645686Z"
-    },
-    "code_folding": []
-   },
-   "outputs": [],
-   "source": [
-    "# Shared Variables and Functions\n",
-    "user = '1241528689'\n",
-    "\n",
-    "# Playlist Inputs\n",
-    "output_playlist = {'daily':'7fnvajjUoWBQDo8iFNMH3s',\n",
-    "                   'archive':'1Q8WS7Xj51WCHZctXGDsrp'}\n",
-    "\n",
-    "# Inputs\n",
-    "inputs = {'Much Needed':'7N3pwZE1N38wcdiuLxiPvq',\n",
-    "                  'Room on the Boat':'1SZS16UcW0XOzgh6UWXA9S',\n",
-    "                  'Refuge':'3K9no6AflSDYiiMzignAm7',\n",
-    "                  'Safety':'0R1gw1JbcOFD0r8IzrbtYP',\n",
-    "                  'Shelter from the Storm':'2yueH0i9C2daBRawYIc9P8',\n",
-    "                  'Soundtracked':'37i9dQZF1DWW7gj0FcGEx6',\n",
-    "                  'Soundtrack for Study':'0hZNf3tcMT4x03FyjKYJ3M',\n",
-    "                  'Film Music - Movie Scores':'5GhatXsZVNYxrhqEAfZPLR',\n",
-    "                  'Video Game Soundtracks':'3Iwd2RiXCzmm1AMUpRAaHO',\n",
-    "                  'Video Game Music Unofficial':'3aI7ztMmDhMHhYe1KOPFLG'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Generating Token and Authenticating. . .\n",
-      "Authentication Complete.\n",
-      "\n",
-      "Reading in existing Data.\n",
-      "Storm Arists Found! Reading in now.\n",
-      "Done! 346 Unique Artists found.\n",
-      "\n",
-      "\n",
-      "Previously Discovered Albums Found! Reading in now.\n",
-      "Done! 29198 Albums found.\n",
-      "\n",
-      "Augmenting new Artists from playlist input dictionary.\n",
-      "Obtaining a list of Tracks from Playlist . . .TIAPTP Archive\n",
-      "100%|██████████| 346/346 [00:00<00:00, 696367.17it/s]\n",
-      "  0%|          | 0/346 [00:00<?, ?it/s]Finding Artists . . .\n",
-      "Done! All Input Playlists Scanned.\n",
-      "Removing Blacklist Artists.\n",
-      "Saving Artist Ids.\n",
-      "Obtaining all albums from the list of artists. (Albums)\n",
-      "100%|██████████| 346/346 [01:04<00:00,  5.36it/s]\n",
-      "  0%|          | 0/346 [00:00<?, ?it/s]Albums being tracked: 4827\n",
-      "Obtaining all albums from the list of artists. (Singles)\n",
-      "100%|██████████| 346/346 [01:23<00:00,  4.15it/s]\n",
-      "  2%|▏         | 3/146 [00:00<00:07, 19.88it/s]Albums being tracked: 29198\n",
-      "Filtering Album list for new content.\n",
-      "Using Filtered albums to obtain a track list.\n",
-      "100%|██████████| 146/146 [00:10<00:00, 14.21it/s]\n",
-      "  1%|▏         | 3/204 [00:00<00:08, 23.46it/s]Checking Tracks for bad features.\n",
-      "Starting track amount: 204\n",
-      "100%|██████████| 204/204 [00:19<00:00, 10.35it/s]\n",
-      "Ending Track Amount: 200\n",
-      "Archiving Current Storm Listening.\n",
-      "No Tracks to Archive.\n",
-      "Preparing Tracks for Writing\n",
-      "Writing Tracks\n",
-      "100%|██████████| 3/3 [00:00<00:00,  8.52it/s]Writing metadata from run.\n",
-      "Saving Albums from run.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "storm_name = 'TIAPTP'\n",
-    "user = '1241528689'\n",
-    "inputs = {'TIAPTP Archive':'3Rr0eHGWeCO2uOuVld6RpD'}\n",
-    "output_playlist = {'daily':'5vUGxhke6R5xfdKufQkbds', 'archive':'22rucwoZOgIhC6eWNv5EUZ'}\n",
-    "\n",
-    "storm = Storm(user, inputs, output_playlist['daily'], output_playlist['archive'], storm_name, \n",
-    "              start_date='2021-04-02', filter_unseen=True, instrumental=False)\n",
-    "storm.Run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "code_folding": [],
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Generating Token and Authenticating. . .\n",
-      "Authentication Complete.\n",
-      "\n",
-      "Reading in existing Data.\n",
-      "Storm Arists Found! Reading in now.\n",
-      "Done! 1571 Unique Artists found.\n",
-      "\n",
-      "\n",
-      "Previously Discovered Albums Found! Reading in now.\n",
-      "Done! 65135 Albums found.\n",
-      "\n",
-      "Augmenting new Artists from playlist input dictionary.\n",
-      "Obtaining a list of Tracks from Playlist . . .Much Needed\n",
-      "Finding Artists . . .\n",
-      "Obtaining a list of Tracks from Playlist . . .Room on the Boat\n",
-      "Finding Artists . . .\n",
-      "Obtaining a list of Tracks from Playlist . . .Refuge\n",
-      "Finding Artists . . .\n",
-      "Obtaining a list of Tracks from Playlist . . .Safety\n",
-      "Finding Artists . . .\n",
-      "Obtaining a list of Tracks from Playlist . . .Shelter from the Storm\n",
-      "Finding Artists . . .\n",
-      "Obtaining a list of Tracks from Playlist . . .Soundtracked\n",
-      "Finding Artists . . .\n",
-      "Obtaining a list of Tracks from Playlist . . .Soundtrack for Study\n",
-      "Finding Artists . . .\n",
-      "Obtaining a list of Tracks from Playlist . . .Film Music - Movie Scores\n",
-      "Finding Artists . . .\n",
-      "Obtaining a list of Tracks from Playlist . . .Video Game Soundtracks\n",
-      "Finding Artists . . .\n",
-      "Obtaining a list of Tracks from Playlist . . .Video Game Music Unofficial\n",
-      "Finding Artists . . .\n",
-      "Done! All Input Playlists Scanned.\n",
-      "Removing Blacklist Artists.\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "  0%|          | 0/1591 [00:00<?, ?it/s]",
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-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Saving Artist Ids.\nObtaining all albums from the list of artists. (Albums)\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "  0%|          | 0/1591 [00:00<?, ?it/s]",
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-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Albums being tracked: 44445\nObtaining all albums from the list of artists. (Singles)\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "  0%|          | 0/1591 [00:00<?, ?it/s]",
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-       "model_id": "f5f9e73d134b4013838521b15a3fa329"
-      }
-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Albums being tracked: 78148\nFiltering Album list for new content.\nUsing Filtered albums to obtain a track list.\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "  0%|          | 0/562 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "16720bf8b83042a78c2accc4b5ed815c"
-      }
-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Checking Tracks for bad features.\nStarting track amount: 1945\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "  0%|          | 0/1945 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "51c014eb03f74d5e85d4060b6a562a5c"
-      }
-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Ending Track Amount: 1194\n",
-      "Archiving Current Storm Listening.\n",
-      "Preparing Tracks for Writing\n",
-      "Writing Tracks\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "  0%|          | 0/9 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "45b4567800f64139ab244ee89266fd03"
-      }
-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Preparing Tracks for Writing\nWriting Tracks\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "  0%|          | 0/23 [00:00<?, ?it/s]",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "623c51b0c6e44ea5b4b6f44536d64d1a"
-      }
-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Writing metadata from run.\nSaving Albums from run.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Authorized spotipy object\n",
-    "storm = Storm(user, inputs, output_playlist['daily'], output_playlist['archive'], 'instrumental', \n",
-    "              start_date='2021-03-15', filter_unseen=True)\n",
-    "\n",
-    "storm.Run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Post-run Metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "code_folding": []
-   },
-   "outputs": [],
-   "source": [
-    "# Plots and Processing\n",
-    "df = storm.mdf\n",
-    "\n",
-    "df['track_added_sum'] = df.tracks_added.cumsum()\n",
-    "df['track_elig_sum'] = df.tracks_eligible.cumsum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(2, 2, figsize = (15, 10));\n",
-    "df.artists_tracked.plot(ax=ax[0][0]).set_title(\"Artists Tracked\");\n",
-    "df.blacklisted_artists.plot(ax=ax[1][0]).set_title(\"Blacklisted Artists\");\n",
-    "df.albums_augmented.plot(ax=ax[0][1]).set_title(\"Albums Augmented\");\n",
-    "df.albums_tracked.plot(ax=ax[1][1]).set_title(\"Albums Tracked\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "<Figure size 1080x720 with 2 Axes>",
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(2, 1, figsize=(15, 10));\n",
-    "df[['tracks_added', 'tracks_eligible']].plot(ax=ax[0]).set_title('Tracks Added by Day');\n",
-    "df[['track_added_sum', 'track_elig_sum']].plot(ax=ax[1]).set_title('Tracks Added Cumulatively');"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "name": "python394jvsc74a57bd0c0c0f186f792db3a37ba7c51f0ce49c4b45c8511f10270060f342a8364fd0546",
-   "display_name": "Python 3.9.4 64-bit ('Storm': pipenv)"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.4-final"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/config_loader/film_vg_instrumental.json b/config_loader/film_vg_instrumental.json
new file mode 100644
index 0000000..2d5af98
--- /dev/null
+++ b/config_loader/film_vg_instrumental.json
@@ -0,0 +1,23 @@
+{
+    "storm_name":"film_vg_instrumental",
+    "good_targets":"3K9no6AflSDYiiMzignAm7",
+    "great_targets":"0R1gw1JbcOFD0r8IzrbtYP",
+    "rolling_good":{"is_active":true, "palylist":"1SZS16UcW0XOzgh6UWXA9S"},
+    "full_storm_delivery":{"is_active":true, "playlist":"7fnvajjUoWBQDo8iFNMH3s", "rank_ordered":true},
+    "sample_storm_delivery":{"is_active":true, "playlist":"1Q8WS7Xj51WCHZctXGDsrp", "sample_size":50},
+    "additional_input_playlists":{
+        "is_active":true,
+        "playlists":{
+            "Much Needed":"7N3pwZE1N38wcdiuLxiPvq",
+            "Room on the Boat":"1SZS16UcW0XOzgh6UWXA9S",
+            "Refuge":"3K9no6AflSDYiiMzignAm7",
+            "Safety":"0R1gw1JbcOFD0r8IzrbtYP",
+            "Shelter from the Storm":"2yueH0i9C2daBRawYIc9P8",
+            "Soundtracked":"37i9dQZF1DWW7gj0FcGEx6",
+            "Soundtrack for Study":"0hZNf3tcMT4x03FyjKYJ3M",
+            "Film Music - Movie Scores":"5GhatXsZVNYxrhqEAfZPLR",
+            "Video Game Soundtracks":"3Iwd2RiXCzmm1AMUpRAaHO",
+            "Video Game Music Unofficial":"3aI7ztMmDhMHhYe1KOPFLG"
+        }
+    }
+}
\ No newline at end of file
diff --git a/scratch.py b/scratch.py
new file mode 100644
index 0000000..7879b7b
--- /dev/null
+++ b/scratch.py
@@ -0,0 +1,27 @@
+import spotipy
+from spotipy import util
+from spotipy import oauth2
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+import os
+import datetime as dt
+import time
+import json
+
+# Internal
+from src.db import *
+
+
+sdb = StormDB()
+sdb.get_playlists(name=True)
+
+sadb = StormAnalyticsDB()
+params = {'playlist_id':'0R1gw1JbcOFD0r8IzrbtYP', 'index':True}
+name = 'playlist_track_changes'
+test = sadb.gen_view(name, params)
+
+
+params = {'playlist_ids':[], 'index':True}
+name = 'many_playlist_track_changes'
+test = sadb.gen_view(name, params)
\ No newline at end of file
diff --git a/src/db.py b/src/db.py
index 4018f03..2f5d09c 100644
--- a/src/db.py
+++ b/src/db.py
@@ -1,6 +1,527 @@
-from pymongo import MongoClient
 import os
+from sys import getsizeof
 import json
+from pymongo import MongoClient
+import pandas as pd
+import numpy as np
+from timeit import default_timer as timer
+
 from dotenv import load_dotenv
 load_dotenv()
 
+class StormDB:
+    """
+    Manages the MongoDB connections, reading and writing.
+    """
+    def __init__(self):
+
+        # Build mongo client and db
+        self.mc = MongoClient(os.getenv('mongo_host'),
+                             username=os.getenv('mongo_user'),
+                             password=os.getenv('mongo_pass'),
+                             authSource=os.getenv('mongo_db'),
+                             authMechanism='SCRAM-SHA-256')
+        self.db = self.mc[os.getenv('mongo_db')]
+
+        # initialize collections
+        self.artists = self.db['artists']
+        self.albums = self.db['albums']
+        self.storms = self.db['storm_metadata']
+        self.tracks = self.db['tracks']
+        self.playlists = self.db['playlists']
+        self.runs = self.db['runs']
+        self.blacklists = self.db['blacklists']
+
+    def get_config(self, storm_name):
+        """
+        returns a storm configuration given its name, assuming it exists.
+        """
+        q = {'name':storm_name}
+        cols = {'config':1}
+        r = list(self.storms.find(q, cols))
+
+        if len(r) == 0:
+            raise KeyError(f"{storm_name} not found, no configuration to load.")
+        else:
+            return r[0]['config']
+
+    def get_all_configs(self):
+        """
+        Returns all configurations in DB.
+        """
+        q = {}
+        cols = {"name":1, "_id":0}
+        r = list(self.storms.find(q, cols))
+
+        return [x['name'] for x in r]
+
+    def get_last_run(self, storm_name):
+        """
+        returns the run_record from last storm run under a given name
+        """
+        q = {"storm_name":storm_name}
+        cols = {"_id":0}
+        r = list(self.runs.find(q, cols))
+
+        if len(r) == 0:
+            return None
+        elif len(r) > 0:
+            max_run_idx = np.argmax(np.array([dt.datetime.strptime(x['run_date'], '%Y-%m-%d') for x in r]))
+            return r[max_run_idx]
+
+    def write_run_record(self, run_record):
+
+        q = {}
+        self.runs.insert_one(run_record)
+
+    # Playlist
+    def get_playlists(self, name=False):
+        """
+        Returns all playlist ids in stormdb as a list, or as their names if you'd rather
+        """
+        q = {}
+        cols = {"_id":1, "info":1}
+        r = list(self.playlists.find(q, cols))
+
+        if name:
+            return [x["info"]["name"] for x in r]
+        else:
+            return [x["_id"] for x in r]
+
+    def get_playlist_current_info(self, playlist_id):
+        """
+        Returns a playlists full record excluding changelog
+        """
+        q = {"_id":playlist_id}
+        cols = {"changelog":0}
+        r = list(self.playlists.find(q, cols))
+
+        if len(r) == 0:
+            raise Exception(f"{playlist_id} not found.")
+        else:
+            return r[0]
+
+    def get_playlist_changelog(self, playlist_id):
+        """
+        Returns a playlists changelog, a dictionary where each entry is a date.
+        """
+        q = {"_id":playlist_id}
+        cols = {"changelog":1}
+        r = list(self.playlists.find(q, cols))
+
+        if len(r) == 0:
+            raise Exception(f"{playlist_id} not found.")
+        else:
+            if 'changelog' in r[0].keys():
+                return r[0]['changelog']
+            else:
+                raise Exception(f"No changelog found for {playlist_id}, has it been collected more than once?")
+
+    def get_playlist_collection_date(self, playlist_id):
+        """
+        Gets a playlists last collection date.
+        """
+        q = {"_id":playlist_id}
+        cols = {"last_collected":1}
+        r = list(self.playlists.find(q, cols))
+
+        # If not found print old date
+        if len(r) == 0:
+            return '2000-01-01' # Long ago
+        elif len(r) == 1:
+            return r[0]['last_collected']
+        else:
+            raise Exception("Playlist Ambiguous, should be unique to table.")
+
+    def update_playlist(self, pr):
+
+        q = {'_id':pr['_id']}
+
+        # Add new entry or update existing one
+        record = pr
+        changelog_update =  {
+                            'snapshot':pr['info']['snapshot_id'],
+                            'tracks':pr['tracks']
+                            }
+
+        # Update static fields
+        exclude_keys = ['changelog']
+        update_dict = {k: pr[k] for k in set(list(pr.keys())) - set(exclude_keys)}
+        self.playlists.update_one(q, {"$set":record}, upsert=True)
+
+        # Push to append fields (date as new key)
+        for key in exclude_keys:
+            self.playlists.update_one(q, {"$set":{f"{key}.{pr['last_collected']}":changelog_update}}, upsert=True)
+
+    def get_loaded_playlist_tracks(self, playlist_id):
+        """
+        Returns a playlists most recently collected tracks
+        """
+        q = {"_id":playlist_id}
+        cols = {'tracks':1, "_id":0}
+        r = list(self.playlists.find(q, cols))
+        
+        if len(r) == 0:
+            raise ValueError(f"Playlist {playlist_id} not found.")
+        else:
+            return r[0]['tracks']
+
+    def get_loaded_playlist_artists(self, playlist_id):
+        """
+        Returns a playlists most recently collected artists
+        """
+        q = {"_id":playlist_id}
+        cols = {'artists':1, "_id":0}
+        r = list(self.playlists.find(q, cols))
+        
+        if len(r) == 0:
+            raise ValueError(f"Playlist {playlist_id} not found.")
+        else:
+            return r[0]['artists']
+
+    # Artists
+    def get_known_artist_ids(self):
+        """
+        Returns all ids from the artists db.
+        """
+
+        q = {}
+        cols = {"_id":1}
+        r = list(self.artists.find(q, cols))
+
+        return [x['_id'] for x in r]
+
+    def update_artists(self, artist_info):
+        """
+        Updates the artist db with new info
+        """
+
+        for artist in tqdm(artist_info):
+            q = {"_id":artist['id']}
+
+            # Writing updates (formatting changes)
+            artist['last_updated'] = dt.datetime.now().strftime('%Y-%m-%d')
+            artist['total_followers'] = artist['followers']['total']
+            del artist['followers']
+            del artist['id']
+
+            self.artists.update_one(q, {"$set":artist}, upsert=True)
+
+    def get_artists_for_album_collection(self, max_date):
+        """
+        returns all artists with album collection dates before max_date.
+        """
+        q = {}
+        cols = {"_id":1, "album_last_collected":1}
+        r = list(self.artists.find(q, cols))
+
+        # Only append artists who need collection in result
+        result = []
+        for artist in r:
+            if 'album_last_collected' in artist.keys():
+                if artist['album_last_collected'] < max_date:
+                    result.append(artist['_id'])
+            else:
+                result.append(artist['_id'])
+        return result
+
+    def update_artist_album_collected_date(self, artist_ids):
+        """
+        Updates a list of artists album_collected date to today.
+        """
+        date = dt.datetime.now().strftime('%Y-%m-%d')
+
+        for artist_id in tqdm(artist_ids):
+            q = {"_id":artist_id}
+            self.artists.update_one(q, {"$set":{"album_last_collected":date}}, upsert=True)
+
+    def get_blacklist(self, name):
+        """
+        Returns a full blacklist record by name (id)
+        """
+        q = {"_id":name}
+        cols = {"_id":1, "blacklist":1, "type":1, "input_playlist":1}
+        return list(self.blacklists.find(q, cols))
+
+    def get_artists_by_genres(self, genres):
+        """
+        Gets a list artists in DB that have one or more of the genres
+        """
+        q = {"genres":{"$all":genres}}
+        cols = {"_id":1}
+        r = list(self.artists.find(q, cols))
+
+        return [x["_id"] for x in r]
+
+    def update_blacklist(self, blacklist_name, artists):
+        """
+        updates a blacklists artists given its name
+        """
+        q = {"_id":blacklist_name}
+        [self.blacklists.update_one(q, {"$addToSet":{"blacklist":x}}) for x in artists]
+
+    # Albums
+    def update_albums(self, album_info):
+        """
+        update album info if needed.
+        """
+
+        for album in tqdm(album_info):
+            q = {"_id":album['id']}
+
+            # Writing updates (formatting changes)
+            album['last_updated'] = dt.datetime.now().strftime('%Y-%m-%d')
+            del album['id']
+
+            self.albums.update_one(q, {"$set":album}, upsert=True)
+
+    def get_albums_by_release_date(self, start_date, end_date):
+        """
+        Get all albums in date window
+        """
+        q = {"release_date":{"$gt": start_date, "$lte": end_date}}
+        cols = {"_id":1}
+        r = list(sdb.albums.find(q, cols))
+
+        return [x['_id'] for x in r]
+
+    def get_albums_for_track_collection(self):
+        """
+        Get all albums that need tracks added.
+        """
+        q = {}
+        cols = {"_id":1, "tracks":1}
+        r = list(self.albums.find(q, cols))
+
+        # Only append artists who need collection in result
+        result = []
+        for album in r:
+            if 'tracks' not in album.keys():
+                result.append(album['_id'])
+        return result
+    
+    def get_albums_from_artists_by_date(self, artists, start_date, end_date):
+        """
+        Get all albums in date window
+        """
+
+        # Get starting list of albums with artists
+        q = {"_id":{"$in":artists}}
+        cols = {"albums":1}
+        r = list(self.artists.find(q, cols))
+
+        valid_albums = []
+        [valid_albums.extend(x['albums']) for x in r if 'albums' in x]
+
+        # Return the albums in this list that also meet date criteria
+        q = {"_id":{"$in":valid_albums}, "release_date":{"$gte": start_date, "$lte": end_date}}
+        cols = {"_id":1}
+        r = list(self.albums.find(q, cols))
+
+        return [x['_id'] for x in r]
+
+    # Tracks
+    def update_tracks(self, track_info):
+        """
+        update track and its album info if needed.
+        """
+
+        for track in tqdm(track_info):
+
+            # Add track to album record
+            q = {'_id':track['album_id']}
+            self.albums.update_one(q, {"$push":{"tracks":track['id']}}, upsert=True)
+
+            # Add track data to tracks
+            q = {"_id":track['id']}
+            track['last_updated'] = dt.datetime.now().strftime('%Y-%m-%d')
+            del track['id']
+            self.tracks.update_one(q, {"$set":track}, upsert=True)
+
+    def update_track_features(self, tracks):
+        """
+        Updates a track's record with audio features
+        """
+        for track in tqdm(tracks):
+            q = {"_id":track['id']}
+
+            # Writing updates (formatting changes)
+            track['audio_features'] = True
+            track['last_updated'] = dt.datetime.now().strftime('%Y-%m-%d')
+            del track['id']
+
+            self.tracks.update_one(q, {"$set":track}, upsert=True)
+
+    def get_tracks_for_feature_collection(self):
+        """
+        Get all tracks that need audio features added.
+        """
+        q = {}
+        cols = {"_id":1, "audio_features":1}
+        r = list(self.tracks.find(q, cols))
+
+        # Only append artists who need collection in result
+        result = []
+        for track in r:
+            if 'audio_features' not in track.keys():
+                result.append(track['_id'])
+            else:
+                if not track['audio_features']:
+                    result.append(track['_id'])
+        return result
+
+    def update_bad_track_features(self, bad_tracks):
+        """
+        If tracks that can't get features are identified, mark them here
+        """
+        for track in tqdm(bad_tracks):
+            q = {"_id":track['id']}
+
+            # Writing updates (formatting changes)
+            track['audio_features'] = False
+            track['last_updated'] = dt.datetime.now().strftime('%Y-%m-%d')
+            del track['id']
+
+            self.tracks.update_one(q, {"$set":track}, upsert=True)
+
+    def get_tracks_from_albums(self, albums):
+        """
+        returns a track list based on an album list
+        """
+        q = {"album_id":{"$in":albums}}
+        cols = {"_id":1}
+        r = list(self.tracks.find(q, cols))
+
+        return [x["_id"] for x in r]
+
+    def filter_tracks_by_audio_feature(self, tracks, audio_filter):
+        """
+        Takes in a specific audio_filter format to get tracks with a filter
+        """
+        q = {"_id":{"$in":tracks}, **audio_filter}
+        cols = {"_id":1}
+        r = list(self.tracks.find(q, cols))
+
+        return [x["_id"] for x in r]
+
+    def get_track_artists(self, track):
+
+        q = {"_id":track}
+        cols = {"_id":1, "artists":1}
+
+        try:
+            return list(self.tracks.find(q, cols))[0]['artists']
+        except:
+            return []
+            raise ValueError(f"Track {track} not found or doesn't have any artists.")
+
+    # DB Cleanup and Prep
+    def update_artist_albums(self):
+        """
+        Adds a track list to each artist or appends if not there
+        """
+
+        q = {}
+        cols = {"_id":1, "added_to_artists":1, 'artists':1}
+        r = list(self.albums.find(q, cols))
+
+        for album in tqdm(r):
+
+            if 'added_to_artists' not in album.keys():
+                for artist in album['artists']:
+                    self.artists.update_one({"_id":artist}, {"$addToSet":{"albums":album["_id"]}}, upsert=True)
+                self.albums.update_one({"_id":album["_id"]}, {"$set":{"added_to_artists":True}})
+            else:
+                if not album['added_to_artists']:
+                    for artist in album['artists']:
+                        self.artists.update_one({"_id":artist}, {"$addToSet":{"albums":album["_id"]}}, upsert=True)
+                    self.albums.update_one({"_id":album["_id"]}, {"$set":{"added_to_artists":True}})
+
+class StormAnalyticsDB:
+    """
+    A StormDB wrapper dedicated to machine learning and general database analytics.
+    Most data will get converted into plot friendly functions, like pandas dataframes.
+    """
+
+    def __init__(self, verbose=True):
+
+        self.sdb = StormDB()
+        #self.sql_db
+
+        self.map = {'playlist_track_changes':self.gen_v_playlist_track_changes,
+                    'many_playlist_track_changes':self.gen_v_many_playlist_track_changes}
+        self.print = print if verbose else lambda x: None
+
+    # Get views from StormDB
+    def gen_view(self, name, view_params={}):
+        """
+        Caller function for views (prints and other nice additions)
+        """
+        if name in self.map.keys():
+            self.print(f"Generating View: {name}")
+            self.print(f"Supplied Parameters: {view_params}")
+
+            start = timer()
+            r = self.map[name](**view_params)
+            end = timer()
+
+            self.print("View Complete!")
+            self.print(f"Elapsed Time to Build: {round(end-start, 4)} ms. | File Size: {getsizeof(r)} bytes")
+
+            return r
+
+        else:
+            raise Exception(f"View {name} not in map.")
+
+    def gen_v_many_playlist_track_changes(self, playlist_ids=[], index=False):
+        """
+        Cross-Compares many playlist track changes
+        """
+
+        if len(playlist_ids) == 0:
+            self.print("No playlists specified, defaulting to all in DB.")
+            playlist_ids = self.sdb.get_playlists()
+        elif len(playlist_ids) == 1:
+            self.print("Only one playlist specified, returning single view.")
+            return self.gen_v_playlist_track_changes(playlist_ids[0])
+
+        # Generate the multiple view dataframe
+        df = pd.DataFrame()
+        self.print("Building and combining Playlist views")
+        for playlist_id in tqdm(playlist_ids):
+
+            playlist_df = self.gen_v_playlist_track_changes(playlist_id, index=False)
+            playlist_df['playlist'] = playlist_id
+
+            # Join it back in
+            df = pd.concat([df, playlist_df])
+
+        return df.set_index(['date_collected', 'playlist']) if index else df
+
+    # Single object views - low-level
+    def gen_v_playlist_track_changes(self, playlist_id, index=False):
+        """
+        Generates a view of a playlists timely health
+        """
+
+        #playlist_info = self.sdb.get_playlist_current_info()
+        playlist_changelog = self.sdb.get_playlist_changelog(playlist_id)
+
+        # Create Dataframe
+        df = pd.DataFrame(index=list(playlist_changelog.keys()))
+
+        # Compute Metrics
+        for change in playlist_changelog:
+
+            # Tracks
+            df.loc[change, 'Number of tracks'] = len(playlist_changelog[change]['tracks'])
+
+            # Artists
+            artists = []
+            [artists.extend(self.sdb.get_track_artists(x)) for x in playlist_changelog[change]['tracks']]
+            df.loc[change, 'Number of Artists'] = len(np.unique(artists))
+
+        # Metadata
+        df.index.rename('date_collected', inplace=True)
+
+        return df if index else df.reset_index()
\ No newline at end of file
diff --git a/src/helper.py b/src/helper.py
new file mode 100644
index 0000000..6f278c8
--- /dev/null
+++ b/src/helper.py
@@ -0,0 +1,8 @@
+import time
+import sys
+
+def slow_print(string='', t=.01):
+    for letter in string:
+        sys.stdout.write(letter)
+        time.sleep(t)
+    sys.stdout.write('\n')
diff --git a/src/runner.py b/src/runner.py
new file mode 100644
index 0000000..bc58586
--- /dev/null
+++ b/src/runner.py
@@ -0,0 +1,494 @@
+import spotipy
+from spotipy import util
+from spotipy import oauth2
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+import os
+import datetime as dt
+import time
+import json
+
+# DB
+from .db import *
+from .storm_client import *
+from pymongo import MongoClient
+
+class StormRunner:
+    """
+    Orchestrates a storm run
+    """
+    def __init__(self, storm_name, start_date=None):
+
+        print(f"Initializing Runner for {storm_name}")
+        self.sdb = StormDB()
+        self.config = self.sdb.get_config(storm_name)
+        self.sc = StormClient(self.config['user_id'])
+        self.suc = StormUserClient(self.config['user_id'])
+        self.name = storm_name
+        self.start_date = start_date
+
+        # metadata
+        self.run_date = dt.datetime.now().strftime('%Y-%m-%d')
+        self.run_record = {'config':self.config, 
+                           'storm_name':self.name,
+                           'run_date':self.run_date,
+                           'start_date':self.start_date,
+                           'playlists':[],
+                           'input_tracks':[], # Determines what gets collected
+                           'input_artists':[], # Determines what gets collected, also 'egligible' artists
+                           'eligible_tracks':[], # Tracks that could be delivered before track filters
+                           'storm_tracks':[], # Tracks actually written out
+                           'storm_artists':[], # Used for track filtering
+                           'storm_albums':[], # Release Date Filter
+                           'storm_sample_tracks':[], # subset of storm tracks delivered to sample
+                           'removed_artists':[] # Artists filtered out
+                           }
+        self.last_run = self.sdb.get_last_run(self.name)
+        self.gen_dates()
+
+        print(f"{self.name} Started Successfully!\n")
+        #self.Run()
+
+    def Run(self):
+        """
+        Storm Orchestration based on a configuration.
+        """
+
+        print(f"{self.name} - Step 0 / 8 - Initializing using last run.")
+        self.load_last_run()
+
+        print(f"{self.name} - Step 1 / 8 - Collecting Playlist Tracks and Artists. . .")
+        self.collect_playlist_info()
+        
+        print(f"{self.name} - Step 2 / 8 - Collecting Artist info. . .")
+        self.collect_artist_info()
+
+        print(f"{self.name} - Step 3 / 8 - Collecting Albums and their Tracks. . .")
+        self.collect_album_info()
+
+        print(f"{self.name} - Step 4 / 8 - Collecting Track Features . . .")
+        self.collect_track_features()
+
+        print(f"{self.name} - Step 5 / 8 - Filtering Track List . . .")
+        self.filter_storm_tracks()
+
+        print(f"{self.name} - Step 6 / 8 - Handing off to Weatherboy . . . ")
+        self.call_weatherboy()
+
+        print(f"{self.name} - Step 7 / 8 - Writing to Spotify . . .")
+        self.write_storm_tracks()
+
+        print(f"{self.name} - Step 8 / 8 - Saving Storm Run . . .")
+        self.save_run_record()
+
+        print(f"{self.name} - Complete!\n")
+    
+    # Object Based orchestration
+    def load_last_run(self):
+        """
+        Loads in relevant information from last run.
+        """
+
+        if self.last_run is None:
+            print("Storm is new, nothing to load")
+
+        else:
+            print("Appending last runs tracks and artists.")
+            self.run_record['input_tracks'].extend(self.last_run['input_tracks'])
+            self.run_record['input_artists'].extend(self.last_run['storm_artists']) # Post-filter
+
+    def collect_playlist_info(self):
+        """
+        Initial Playlist setup orchestration
+        """
+
+        print("Loading Great Targets . . .")
+        self.load_playlist(self.config['great_targets'])
+
+        print("Loading Good Targets . . .")
+        self.load_playlist(self.config['good_targets'])
+
+        # Check for additional playlists
+        if 'additional_input_playlists' in self.config.keys():
+            if self.config['additional_input_playlists']['is_active']:
+                for ap, ap_id in self.config['additional_input_playlists']['playlists'].items():
+                    print(f"Loading Additional Playlist: {ap}")
+                    self.load_playlist(ap_id)
+        
+        # Check what songs remain in sample and full delivery
+        self.load_output_playlist(self.config['full_storm_delivery']['playlist'])
+
+        ## ---- Future Version ----
+        self.load_output_playlist(self.config['rolling_good']['playlist'])
+        # Check if we need to move rolling
+       
+        print("Playlists Prepared. \n")
+
+    def collect_artist_info(self):
+        """
+        Loads in the data from the run_records artists
+        """
+
+        # get data for artists we don't know
+        known_artists = self.sdb.get_known_artist_ids()
+        new_artists = [x for x in self.run_record['input_artists'] if x not in known_artists]
+
+        if len(new_artists) > 0:
+            print(f"{len(new_artists)} New Artists Found! Getting their info now.")
+            new_artist_info = self.sc.get_artist_info(new_artists)
+
+            print("Writing their info to DB . . .")
+            self.sdb.update_artists(new_artist_info)
+        
+        else:
+            print("No new Artists found.")
+
+        print("Artist Info Collection Done.\n")
+
+    def collect_album_info(self):
+        """
+        Get and update all albums associated with the artists
+        """
+        
+        print("Getting the albums for Input Artists that haven't been acquired.")
+        self.collect_artist_albums()
+        
+        print("Getting tracks for albums that need it")
+        self.collect_album_tracks()
+    
+        print("Album Collection Done. \n")
+
+    def collect_track_features(self):
+        """
+        Gets all track features needed
+        Also in a while try except loop to get through all tracks in the case of bad batches.
+        """
+        
+        to_collect = self.sdb.get_tracks_for_feature_collection()
+        if len(to_collect) == 0:
+            print("No Track Features to collect.")
+            return True
+
+        batch_size = 1000
+        batches = np.array_split(to_collect, int(np.ceil(len(to_collect)/batch_size)))
+
+        # Attempt to go get the batches
+        bad_batch_retries = 0
+        consecutive_bad_batches_limit = 10
+        retry_limit = 5
+        while (bad_batch_retries < retry_limit) & (len(batches) > 0):
+
+            bad_batches = []
+            consecutive_bad_batches = 0
+            print(f"Batch Size: {batch_size} | Number of Batches {len(batches)}")
+            for batch in tqdm(batches):
+
+                if consecutive_bad_batches > consecutive_bad_batches_limit:
+                    raise Exception(f"{consecutive_bad_batches_limit} consecutive bad batches. . . Terminating Process.")
+                try:
+                    batch_tracks = self.sc.get_track_features(batch)
+                    self.sdb.update_track_features(batch_tracks)
+
+                    # Successful, does not need collection
+                    consecutive_bad_batches = 0
+
+                except:
+                    print("Bad Batch, will try again after.")
+                    bad_batches.append(batch)
+                    consecutive_bad_batches += 1
+
+            bad_batch_retries += 1
+            batches = bad_batches
+
+            bad_batch_retries += 1
+        
+        print("All Track batches collected!")
+        print("Track Collection Done! \n")
+        return True
+
+    def filter_storm_tracks(self):
+        """
+        Get a List of tracks to deliver.
+        """
+
+        print("Filtering artists.")
+        self.apply_artist_filters()
+
+        print("Obtaining all albums from storm artists.")
+        self.run_record['storm_albums'] = self.sdb.get_albums_from_artists_by_date(self.run_record['storm_artists'], 
+                                                                                   self.run_record['start_date'],
+                                                                                   self.run_date)
+        print("Getting tracks from albums.")
+        self.run_record['eligible_tracks'] = self.sdb.get_tracks_from_albums(self.run_record['storm_albums'])
+
+        print("Filtering Tracks.")
+        self.apply_track_filters()
+
+        print("Storm Tracks Generated! \n")
+
+    def call_weatherboy(self):
+        """
+        Run Modeling process
+        """
+        return None
+
+    def write_storm_tracks(self):
+        """
+        Output the tracks in storm_tracks
+        """
+        self.suc.write_playlist_tracks(self.config['full_storm_delivery']['playlist'], self.run_record['storm_tracks'])
+
+    def save_run_record(self):
+        """
+        Update Metadata and save run_record
+        """
+        self.sdb.write_run_record(self.run_record)
+
+
+    # Low Level orchestration
+    def gen_dates(self):
+        """
+        If there was a last run, do all tracks in between. Otherwise do a week since run
+        """
+
+        if self.last_run is not None:
+            if 'run_date' in self.last_run.keys():
+                self.start_date = self.last_run['run_date']
+                self.run_record['start_date'] = self.start_date
+        
+        if self.start_date is None:
+            self.start_date = (dt.datetime.now() - dt.timedelta(days=7)).strftime("%Y-%m-%d")
+            self.run_record['start_date'] = self.start_date
+
+    def load_playlist(self, playlist_id):
+        """
+        Pulls down playlist info and writes it back to db
+        """
+
+        # Determine if playlists need examining
+        if self.run_date > self.sdb.get_playlist_collection_date(playlist_id):
+
+            # Acquire data
+            playlist_record = {'_id':playlist_id, 
+                            'last_collected':self.run_date}
+
+            playlist_record['info'] = self.sc.get_playlist_info(playlist_id)
+            playlist_record['tracks'] = self.sc.get_playlist_tracks(playlist_id)
+            playlist_record['artists'] = self.sc.get_artists_from_tracks(playlist_record['tracks'])
+
+            print("Writing changes to DB")
+            self.sdb.update_playlist(playlist_record)
+
+        else:
+            print("Skipping API Load, already collected today.")
+
+        # Get the playlists tracks from DB
+        input_tracks = self.sdb.get_loaded_playlist_tracks(playlist_id)
+        input_artists = self.sdb.get_loaded_playlist_artists(playlist_id)
+
+        # Update run record
+        self.run_record['playlists'].append(playlist_id)
+        self.run_record['input_tracks'].extend([x for x in input_tracks if x not in self.run_record['input_tracks']])
+        self.run_record['input_artists'].extend([x for x in input_artists if x not in self.run_record['input_artists']])
+
+    def load_output_playlist(self, playlist_id):
+        """
+        Pulls down playlist info and writes it back to db
+        """
+
+        # Determine if playlists need examining
+        if self.run_date > self.sdb.get_playlist_collection_date(playlist_id):
+
+            # Acquire data
+            playlist_record = {'_id':playlist_id, 
+                            'last_collected':self.run_date}
+
+            playlist_record['info'] = self.sc.get_playlist_info(playlist_id)
+            playlist_record['tracks'] = self.sc.get_playlist_tracks(playlist_id)
+            if len(playlist_record['tracks']) > 0:
+                playlist_record['artists'] = self.sc.get_artists_from_tracks(playlist_record['tracks'])
+
+                print("Writing changes to DB")
+                self.sdb.update_playlist(playlist_record)
+            else:
+                print("No tracks, must be new storm or something odd is happening.")
+
+        else:
+            print("Skipping API Load, already collected today.")
+
+    def load_artist_albums(self, artists):
+        """
+        Get many artists information in batches and write back to database incrementally.
+        """
+        batch_size = 20
+        batches = np.array_split(artists, int(np.ceil(len(artists)/batch_size)))
+
+        print(f"Batch Size: {batch_size} | Number of Batches {len(batches)}")
+        for batch in tqdm(batches):
+
+            batch_albums = self.sc.get_artist_albums(batch)
+            self.sdb.update_albums(batch_albums)
+            self.sdb.update_artist_album_collected_date(batch)
+
+    def collect_artist_albums(self):
+        """
+        Get artist albums for input artists that need it.
+        """
+        # Get a list of all artists in storm that need album collection
+        needs_collection = self.sdb.get_artists_for_album_collection(self.run_date)
+        to_collect = [x for x in self.run_record['input_artists'] if x in needs_collection]
+
+        # Get their albums
+        if len(to_collect) == 0:
+            print("Evey Input Artist's Albums already acquired today.")
+        else:
+            print(f"New albums to collect for {len(to_collect)} artists.")
+            print("Collecting data in batches from API and Updating DB.")
+            self.load_artist_albums(to_collect)
+
+        print("Updating artist album association in DB.")
+        self.sdb.update_artist_albums()
+
+    def collect_album_tracks(self):
+        """
+        Gets tracks for every album that needs them, not just storm.
+        In the case of new storms this helps populate historical.
+        In the case of existing ones it will only be the storm albums that need collection.
+        Given the intensity, try except implemented to retry bad batches
+        """
+        needs_collection = self.sdb.get_albums_for_track_collection()
+        batch_size = 20
+        if len(needs_collection) == 0:
+            print("No Albums needed to collect.")
+            return True
+
+        batches = np.array_split(needs_collection, int(np.ceil(len(needs_collection)/batch_size)))
+
+        # Attempt to go get the batches
+        bad_batch_retries = 0
+        consecutive_bad_batches_limit = 10
+        retry_limit = 5
+        while (bad_batch_retries < retry_limit) & (len(batches) > 0):
+
+            bad_batches = []
+            consecutive_bad_batches = 0
+            print(f"Batch Size: {batch_size} | Number of Batches {len(batches)}")
+            for batch in tqdm(batches):
+
+                if consecutive_bad_batches > consecutive_bad_batches_limit:
+                    raise Exception(f"{consecutive_bad_batches_limit} consecutive bad batches. . . Terminating Process.")
+                try:
+                    batch_tracks = self.sc.get_album_tracks(batch)
+                    self.sdb.update_tracks(batch_tracks)
+
+                    # Successful, does not need collection
+                    consecutive_bad_batches = 0
+
+                except:
+                    print("Bad Batch, will try again after.")
+                    bad_batches.append(batch)
+                    consecutive_bad_batches += 1
+
+            bad_batch_retries += 1
+            batches = bad_batches
+        
+        print("All album batches collected!")
+        return True
+
+    def apply_artist_filters(self):
+        """
+        read in filters from configurations
+        """
+        filters = self.config['filters']['artist']
+        supported = ['genre', 'blacklist']
+        bad_artists = []
+
+        # Filters
+        print(f"{len(filters)} valid filters to apply")
+        for filter_name, filter_value in filters.items():
+            
+            print(f"Attemping filter {filter_name} - {filter_value}")
+            if filter_name == 'genre':
+                # Add all known artists in sdb of a genre to remove in tracks later
+                genre_artists = self.sdb.get_artists_by_genres(filter_value)
+                bad_artists.extend(genre_artists)
+
+            elif filter_name == 'blacklist':
+                blacklist = self.sdb.get_blacklist(filter_value)
+                if len(blacklist) == 0:
+                    print(f"{filter_value} not found, no filtering will be done.'")
+                else:
+                    print(f"{filter_value} found!'")
+                    if 'input_playlist' in blacklist[0].keys():
+                        print("Updating Blacklist . . .")
+                        self.update_blacklist_from_playlist(blacklist[0]['_id'], blacklist[0]['input_playlist'])
+
+                        # Reload
+                        blacklist = self.sdb.get_blacklist(filter_value)
+                    bad_artists.extend(blacklist[0]['blacklist'])
+            else:
+                print(f"{filter_name} not supported or misspelled. ")
+
+        self.run_record['storm_artists'] = [x for x in self.run_record['input_artists'] if x not in bad_artists]
+        self.run_record['removed_artists'] = bad_artists
+        print(f"Starting Artist Amount: {len(self.run_record['input_artists'])}")
+        print(f"Ending Artist Amount: {len(self.run_record['storm_artists'])}")
+
+    def update_blacklist_from_playlist(self, blacklist_name, playlist_id):
+        """
+        Updates a blacklist from a playlist (reads the artists)
+        """
+        bl_tracks = self.sc.get_playlist_tracks(playlist_id)
+        bl_artists = self.sc.get_artists_from_tracks(bl_tracks)
+        self.sdb.update_blacklist(blacklist_name, bl_artists)
+
+    def apply_track_filters(self):
+        """
+        read in filters from configurations
+        """
+        filters = self.config['filters']['track']
+        supported = ['audio_features', 'artist_filter']
+        bad_tracks = []
+
+        # Filters
+        print(f"{len(filters)} valid filters to apply")
+        for filter_name, filter_value in filters.items():
+            
+            print(f"Attemping filter {filter_name} - {filter_value}")
+            if filter_name == 'audio_features':
+                for feature, feature_value in filter_value.items():
+                    op = f"${feature_value.split('&&')[0]}"
+                    val = float(feature_value.split('&&')[1])
+                    print(f"Removing tracks with {feature} - {op}:{val}")
+                    valid = self.sdb.filter_tracks_by_audio_feature(self.run_record['eligible_tracks'], {feature:{op:val}})
+                    bad_tracks.extend([x for x in self.run_record['eligible_tracks'] if x not in valid])
+                    print(f"Cumulative Bad Tracks found {len(np.unique(bad_tracks))}")
+
+                
+            elif filter_name == "artist_filter":
+                if filter_value == 'hard':
+                    # Limits output to tracks that contain only storm artists
+                    for track in tqdm(self.run_record['eligible_tracks']):
+
+                        track_artists = set(self.sdb.get_track_artists(track))
+                        if not track_artists.issubset(set(self.run_record['storm_artists'])):
+                            bad_tracks.append(track)
+
+                elif filter_value == 'soft':
+                    # Removes tracks that contain known filtered out artists
+                    # Other 'bad' artists could sneak in if not tracked by storm
+                    for track in tqdm(self.run_record['eligible_tracks']):
+                        track_artists = set(self.sdb.get_track_artists(track))
+                        if not set(self.run_record['removed_artists']).isdisjoint(track_artists):
+                            bad_tracks.append(track)
+
+            else:
+                print(f"{filter_name} not supported or misspelled. ")
+
+        bad_tracks = np.unique(bad_tracks).tolist()
+        print("Removing bad tracks . . .")
+        self.run_record['storm_tracks'] = [x for x in self.run_record['eligible_tracks'] if x not in bad_tracks]
+        self.run_record['removed_tracks'] = bad_tracks
+        print(f"Starting Track Amount: {len(self.run_record['eligible_tracks'])}")
+        print(f"Ending Track Amount: {len(self.run_record['storm_tracks'])}")
diff --git a/src/storm.py b/src/storm.py
new file mode 100644
index 0000000..5b69adc
--- /dev/null
+++ b/src/storm.py
@@ -0,0 +1,44 @@
+import spotipy
+from spotipy import util
+from spotipy import oauth2
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+import os
+import datetime as dt
+import time
+import json
+
+# DB
+from pymongo import MongoClient
+
+# ENV
+from dotenv import load_dotenv
+load_dotenv()
+
+# INTERNAL
+from .db import *
+from .storm_client import *
+from .runner import *
+
+class Storm:
+    """
+    Main callable that initiates and saves storm data
+    """
+    def __init__(self, storm_names, start_date=None):
+
+        self.print_initial_screen()
+        self.storm_names = storm_names
+
+    def print_initial_screen(self):
+
+        print("A Storm is Brewing. . .\n")
+        time.sleep(.5)
+        
+    def Run(self):
+
+        print("Spinning up Storm Runners. . . ")
+        for storm_name in self.storm_names:
+            StormRunner(storm_name).Run()
+
+Storm(['film_vg_instrumental', 'contemporary_lyrical']).Run()
\ No newline at end of file
diff --git a/src/storm_client.py b/src/storm_client.py
index 8142e79..3a56e51 100644
--- a/src/storm_client.py
+++ b/src/storm_client.py
@@ -11,125 +11,30 @@
 
 # DB
 from pymongo import MongoClient
-from dotenv import load_dotenv
-load_dotenv()
 
-
-class StormClient:
+class StormUserClient:
 
     def __init__(self, user_id):
+        """
+        Client with authorization for modifying user information.
+        """
 
-        self.scope = 'user-follow-read playlist-modify-private playlist-modify-public user-follow-modify' # scope for permissions
-        self.user_id = user_id
+        self.user_id = user_id # User to authorize, only needed for modify operations
+        self.scope = 'playlist-modify-private playlist-modify-public' # scope for permissions
         self.client_id = os.getenv('storm_client_id') # API app id
         self.client_secret = os.getenv('storm_client_secret') # API app secret
 
-        # DB connection
-        self.mc = MongoClient(os.getenv('mongodb_uri'))
-        self.db = self.mc['storm']
-
-        # Spotify API connection
-        self.sp = None
-        self.token_end = None
-        self.get_token()
-
-    # Authentication
-    def get_token(self):
-
-        if os.path.exists('token.json'):
-            with json.load(open('token.json', "r")) as f:
-                if dt.datetime.fromtimestamp(f['expires']) < dt.datetime.now():
-                    self.token = f['token']
-                    self.token_end = f['expires']
-
-        else:
-            self.get_new_token()
-
-        self.sp = spotipy.Spotify(auth=self.token)
-
-    def get_new_token(self):
-
-        self.token = util.prompt_for_user_token(self.user_id,
-                                                    scope=self.scope,
-                                                    client_id=self.client_id,
-                                                    client_secret=self.client_secret,
-                                                    redirect_uri='http://localhost/')
-
-        self.token_end = dt.datetime.timestamp(dt.datetime.now() + dt.timedelta(minutes=59))
-        json.dump({'token':self.token, 'expires':str(self.token_end)}, open('token.json', 'w'))
-
-
-storm = StormClient('1241528689')
-
-
-# A class to manage all of the storm functions and authentication
-class Storm:
-    """
-    Single object for running and saving data frm the storm run. Call Storm.Run() to generate a playlist from
-    saved artists.
-    """
-    def __init__(self, user_id, inputs, output, archive, name, start_date=None, filter_unseen=True, instrumental=True):
-        """
-        params:
-            user_id - spotify user account number
-            inputs - Dictionary of playlists 'name':'playlist_id' that will feed new releases
-            output - Playlist id to save new releases to
-            archive - Playlist id to archive current songs in the storm to
-            name - A name for this storm setup (for saving metadata and allowing for multiple storm configurations)
-            start_date - defaults to a 2-day window frm current date, but could be wider if desired (format: 'yyyy-mm-dd')
-        """
-        # Variables
-        self.scope = 'user-follow-read playlist-modify-private playlist-modify-public user-follow-modify' # scope for permissions
-        self.user_id = user_id
-        self.client_id = os.getenv('client_id') # API app id
-        self.client_secret = os.getenv('client_secret') # API app secret
         self.token = None
-        self.token_start = None
-        self.sp = None
-        self.inputs = inputs
-        self.output = output
-        self.archive = archive
-        self.name = name
-        self.start_date = start_date
-        self.window_date = None
-        self.filter_unseen = filter_unseen
-        self.instrumental = instrumental
-        
-        # Initialization
+
+        # Authenticate
         self.authenticate()
-        self.gen_dates()
-        
-        # I/O Params for file saving
-        self.artist_id_csv = './data/storm_artists_'+self.name+'.csv'
-        self.album_id_csv = './data/storm_albums_'+self.name+'.csv'
-        self.md_name = './data/storm_run_metadata_'+self.name+'.csv'
-        
-        # Dataframe initialization
-        self.blacklist = []
-        self.artist_ids = []
-        self.album_ids = []
-        self.albums = pd.DataFrame(columns = ['album_group', 'album_type', 'artists', 'available_markets',
-                               'external_urls', 'href', 'id', 'images', 'name', 'release_date',
-                               'release_date_precision', 'total_tracks', 'type', 'uri'])
-        self.new_ablums = pd.DataFrame()
-        self.new_tracks = pd.DataFrame(columns = ['artists', 'available_markets', 'disc_number', 'duration_ms',
-                               'explicit', 'external_urls', 'href', 'id', 'is_local', 'name',
-                               'preview_url', 'track_number', 'type', 'uri'])
-        self.storm_track_ids = []
-        
-        
-        # Metadata for post-run reports
-        self.mdf = pd.read_csv(self.md_name).set_index('run_date')
-        self.rd = dt.datetime.now().strftime("%Y/%m/%d")
-        self.mdf.loc[self.rd, 'start_date'] = self.start_date
-        
-            
+        print("Storm User Client successfully connected to Spotify.")
+
     # Authentication Functions
     def authenticate(self):
         """
         Connect to Spotify API, intialize spotipy object and generate access token.
         """
-        print("Generating Token and Authenticating. . .")
         self.token = util.prompt_for_user_token(self.user_id,
                                                 scope=self.scope,
                                                 client_id=self.client_id,
@@ -137,426 +42,217 @@ def authenticate(self):
                                                 redirect_uri='http://localhost/')
         self.sp = spotipy.Spotify(auth=self.token)
         self.token_start = dt.datetime.now()
-        print("Authentication Complete.")
-        print()
-    
-    def check_token(self):
-        """
-        Determine if token is still valid. This is called in many methods to avoid timeout
-        """
-        
-        if abs((self.token_start - dt.datetime.now()).total_seconds()) < 3580:
-            return True
-        else:
-            print("Awaiting Expiration and Refreshing.")
-            time.sleep(25)
-            self.authenticate()
-
-    def gen_dates(self):
-        """
-        Generates a window-date to filter album release dates based on start-date
-        """
-        
-        # Start Dates
-        if self.start_date == None:
-            self.start_date = (dt.datetime.now() - dt.timedelta(days=1)).strftime("%Y-%m-%d")
-            
-        # Playlist Cycling dates
-        self.window_date = (dt.datetime.now() - dt.timedelta(days=14)).strftime("%Y-%m-%d")
-     
-    
-    # Ochestration Function
-    def Run(self):
+
+    def write_playlist_tracks(self, playlist_id, tracks):
         """
-        The function that a user must run to generate their playlist of new releases.
-        Call this function after building a storm object
-        
-        Example Usage:
-        storm = Storm(params)
-        storm.Run() # Use parameters to generate releases
+        Writes a list of track ids into a user's playlist
         """
-        # Read-in existing data from past runs
-        self.read_in()
-        
-        # Augment artist list before track collection
-        self.augment_artist_list()
-        self.clean_artists()
-        self.save_artists()
-        
-        # Get Album lists
-        self.get_artist_albums()
-        self.filter_albums()
-
-        # Tracks
-        self.get_album_tracks()
-        self.clean_tracks()
-
-        # if track list to large apply date filter
-        if len(self.storm_track_ids)>9999:
-            self.filter_unseen = True
-            self.filter_albums()
-            self.get_album_tracks()
-            self.clean_tracks()
-        
-        # Playlist Writing
-        self.archive_current()
-        self.add_tracks_to_playlist(self.output, self.storm_track_ids)
-        
-        # Metadata save
-        self.save_md()
-        self.save_albums()
-        
-    
-    # I/O
-    # methods in this section are straightforward and mostly used for metadata
-    # tracking and simplifying the number of API calls using information fr0m
-    # past runs
-    def read_in(self):
+
+        # Call info
+        id_lim = 50
+        batches = np.array_split(tracks, int(np.ceil(len(tracks)/id_lim)))
+
+        # First batch overwrite
+        self.authenticate()
+        self.sp.user_playlist_replace_tracks(self.user_id, playlist_id, batches[0])
+
+        for batch in tqdm(batches[1:]):
+            self.sp.user_playlist_add_tracks(self.user_id, playlist_id, batch)
+
+        return True
+
+class StormClient:
+
+    def __init__(self, user_id):
         """
-        Storm init function to gather 
+        Simple client, no user needed
         """
-        print("Reading in existing Data.")
-        
-        if path.exists(self.artist_id_csv):
-            print("Storm Arists Found! Reading in now.")
-            self.artist_ids = pd.read_csv(self.artist_id_csv)['artists'].values.tolist()
-            self.mdf.loc[self.rd, 'artists_tracked'] = len(self.artist_ids)
-            print(f"Done! {len(self.artist_ids)} Unique Artists found.")
-            
-        else:
-            self.mdf.loc[self.rd, 'artists_tracked'] = 0
-        print()
-            
-        if path.exists('storm_blacklist_'+self.name+'.csv'):
-            print("Blacklisted Arists Found! Reading in now.")
-            self.blacklist = pd.read_csv('storm_blacklist_'+self.name+'.csv')['artists'].tolist()
-            self.mdf.loc[self.rd, 'blacklisted_artists'] = len(self.blacklist)
-            print(f"Done! {len(self.blacklist)} Blacklisted Artists found.")
-        print()
-            
-        if path.exists(self.album_id_csv):
-            print("Previously Discovered Albums Found! Reading in now.")
-            self.album_ids = pd.read_csv(self.album_id_csv)['albums'].values.tolist()
-            self.mdf.loc[self.rd, 'albums_tracked'] = len(self.album_ids)
-            print(f"Done! {len(self.album_ids)} Albums found.") 
-            
-        else:
-            self.mdf.loc[self.rd, 'albums_tracked'] = 0
-        print()
-    
-    def save_artists(self):
-        
-        print("Saving Artist Ids.")
-        pd.DataFrame(self.artist_ids, columns=['artists']).to_csv(self.artist_id_csv, index=False)
-    
-    def save_albums(self):
-        print("Saving Albums from run.")
-        self.album_ids = self.albums.id.tolist()
-        pd.DataFrame(self.album_ids, columns=['albums']).to_csv(self.album_id_csv, index=False)
-    
-    def save_md(self):
-        
-        print("Writing metadata from run.")
-        self.mdf.to_csv(self.md_name)
-    
-    # Storm Aggregate Functions
-    # These methods do the bulk of the API interfacing
-    # Most functions take in the previous step and work with the API
-    # to obtain all the data needed to progress the Run method forward
-    def augment_artist_list(self):
+
+        self.user_id = user_id # User scope, no authorization needed, though
+        self.client_id = os.getenv('storm_client_id') # API app id
+        self.client_secret = os.getenv('storm_client_secret') # API app secret
+
+        # Spotify API connection
+        self.sp_cc = oauth2.SpotifyClientCredentials(self.client_id, self.client_secret)
+        self.token = None
+
+        # Authenticate
+        self.refresh_connection()
+
+        # Good
+        print("Storm Client successfully connected to Spotify.")
+
+
+    # Authentication
+    def refresh_connection(self):
         """
-        Use playlist inputs to get a list of artists to track releases from
-        output:
-            Arists from playlists added to artist_ids
+        Get a cached token (again) or try to get a new one. 
+        Call this before any api call to make sure it won't get credential error.
         """
-        # Comb through playlists and get the artist ids
-        print("Augmenting new Artists from playlist input dictionary.")
-        for pl in self.inputs.keys():
-            print("Obtaining a list of Tracks from Playlist . . ." + pl)
-            playlist_df = self.get_playlist_tracks(self.inputs[pl])
-
-            print("Finding Artists . . .")
-            self.extend_artists(playlist_df['track'])
-        
-        print("Done! All Input Playlists Scanned.")
+        self.token = self.sp_cc.get_access_token(as_dict=False)
+        self.sp = spotipy.Spotify(auth=self.token)
+
+    def get_playlist_info(self, playlist_id):
+        """ Returns subset of playlist metadata """
+
+        # params
+        fields = 'description,id,name,owner,snapshot_id'
+
+        # Get the info
+        self.refresh_connection()
+        return self.sp.playlist(playlist_id, fields=fields)
 
     def get_playlist_tracks(self, playlist_id):
         """
-        Obtain all tracks from a playlist id
-        input:
-            playlist_id - input playlist that tracks will be collected for
-        output:
-            All tracks from playlist saved
+        Return subset of information about a playlists tracks (unique)
         """
-        lim = 50
-        more_tracks = True
-        offset=0
 
-        self.check_token()
-        playlist_results = self.sp.user_playlist_tracks(self.user_id, playlist_id, limit=lim, offset=offset)
+        # Call info
+        lim = 100
+        offset = 0
+        fields = 'items(track(id))' # only getting the ids, get info about them later
         
-        if len(playlist_results['items']) < lim:
-                more_tracks = False
-
-        while more_tracks:
+        # Get number of tracks trying to get (faster to know then go in blind)
+        self.refresh_connection()
+        total = int(self.sp.user_playlist_tracks(self.user_id, playlist_id, fields='total')['total'])
+        print(f"Total Tracks: {total}")
+        
+        # loop through and append track ids
+        result = ['' for x in range(total)] # List of track ids pre-initialized
+        for i in tqdm(range(int(np.ceil(total/lim)))):
+            self.refresh_connection()
+            response = self.sp.user_playlist_tracks(self.user_id, playlist_id, fields=fields, limit=lim, offset=(i*lim))
 
-            self.check_token()
-            offset += lim
-            batch = self.sp.user_playlist_tracks(self.user_id, playlist_id, limit=lim, offset=offset)
-            playlist_results['items'].extend(batch['items'])
+            result[i*lim:(i*lim)+len(response['items'])] = [x['track']['id'] for x in response['items']]
 
-            if len(batch['items']) < lim:
-                more_tracks = False
+        return np.unique(result).tolist()
 
-        response_df = pd.DataFrame(playlist_results['items'])
-        return response_df
-    
-    def extend_artists(self, track_df):
-        """
-        Take a list of artists, get information and decide whether to include
-        input:
-            Dataframe of Tracks
-        output:
-            Cleaned set of artist ids to augment
-        """
-        for track in track_df:
-            try:
-                artists = dict(track)['artists']
-            except:
-                continue
-
-            for artist in artists:
-                if artist['id'] not in self.artist_ids:
-                    self.check_token()
-                    artist_info = self.sp.artist(artist['id'])
-                    if 'classical' not in artist_info['genres']:
-                        self.artist_ids.append(artist['id'])
-    
-    def clean_artists(self):
-        """
-        Remove any artists saved in the Storm's blacklist metadata file
-        """
-        print("Removing Blacklist Artists.")
-        self.filter_blacklist()
-    
-    def clean_tracks(self):
+    def get_artists_from_tracks(self, tracks):
         """
-        Perform clean-up on list of newly released tracks
+        Returns list of artist_ids given track_ids
         """
-        self.storm_track_ids = np.unique(self.storm_track_ids)
-        self.new_tracks = self.new_tracks.drop_duplicates('id').reset_index(drop=True)
-        newids = []
-        
-        print("Checking Tracks for bad features.")
-        print("Starting track amount: "+str(len(self.new_tracks)))
-        for index in tqdm(self.new_tracks.index):
-            
-            artists = self.new_tracks.loc[index, 'artists']
-            check=True
-            
-            # Check artists
-            for artist in artists:
-                if artist['id'] in self.blacklist:
-                     check = False
-            
-            # If still a valid track, check a few features
-            if check:
-                
-                # Get track features
-                af = self.sp.audio_features(self.new_tracks.loc[index, 'id'])[0]
-                
-                try:
-                    if af['instrumentalness'] < .7:
-                        check = False
-                    elif af['speechiness'] > .32:
-                        check = False
-                    elif af['duration_ms'] < 60001:
-                        check = False
-                except:
-                    continue
-            
-            # Remove if certain features don't clear
-            if not self.instrumental:
-                check = True
-
-            if check:
-                 newids.append(self.new_tracks.loc[index, 'id'])
-        print("Ending Track Amount: " + str(len(newids)))
-        self.storm_track_ids = newids
-        self.mdf.loc[self.rd, 'tracks_added'] = len(self.storm_track_ids)
-        self.mdf.loc[self.rd, 'tracks_removed'] = self.mdf.loc[self.rd, 'tracks_eligible'] - self.mdf.loc[self.rd, 'tracks_added']
-    
-    def filter_classical(self):
+
+        # Call Info
+        id_lim = 50
+        batches = np.array_split(tracks, int(np.ceil(len(tracks)/id_lim)))
+
+        # Get Artists
+        artists = []
+        for batch in tqdm(batches):
+            self.refresh_connection()
+            response = self.sp.tracks(batch, market='US')['tracks']
+            [artists.extend(x['artists']) for x in response]
+
+        # Filter to just ids
+        return np.unique([x['id'] for x in artists]).tolist()
+
+    def get_artist_info(self, artists):
         """
-        Classical music filters on artist
+        Gets a subset of artist info from a list of ids
         """
-        output_list = []
-        for artist in tqdm(self.artist_ids):
-            self.check_token()
-            artist_info = self.sp.artist(artist)
 
-            if 'classical' not in artist_info['genres']:
-                output_list.append(artist)
+        # Call info
+        id_lim = 50
+        keys = ['followers', 'genres', 'id', 'name', 'popularity']
+        batches = np.array_split(artists, int(np.ceil(len(artists)/id_lim)))
 
-        self.artist_ids = output_list
-        
-    def filter_blacklist(self):
-        """
-        Blacklist metadata file filter
+        # Get All artist info
+        result = []
+        for batch in tqdm(batches):
+            self.refresh_connection()
+            response = self.sp.artists(batch)['artists']
+            result.extend(response)
+
+        # Filter to just relevant fields
+        for i in range(len(result)):
+            result[i] = {k: result[i][k] for k in keys}
+
+        return result
+
+    def get_artist_albums(self, artists):
         """
-        output_list = []
-        for artist in tqdm(self.artist_ids):
-            if artist not in self.blacklist:
-                output_list.append(artist)
-
-        self.artist_ids = output_list
-        self.mdf.loc[self.rd, 'artists_augmented'] = len(self.artist_ids)-self.mdf.loc[self.rd, 'artists_tracked']
-    
-    def get_artist_albums(self):
+        Returns subset of album fields
         """
-        Get a list of all albums an artist has released
-        """        
-        
-        print("Obtaining all albums from the list of artists. (Albums)")
+
+        # Call info
         lim = 50
-        for artist_id in tqdm(self.artist_ids):
-            
-            self.check_token()
-            response = self.sp.artist_albums(artist_id, limit=lim, album_type='album', country='US')
-            offset = 0
-            more_albums = True
-
-            while more_albums:
-                
-                self.check_token()
-                batch = self.sp.artist_albums(artist_id, limit=lim, offset=offset, album_type='album', country='US')
-                response['items'].extend(batch['items'])
-                offset += lim
-
-                if len(batch['items']) < lim:
-                        more_albums = False
-
-            response_df = pd.DataFrame(response['items'])
-            self.albums = pd.concat([self.albums, response_df], axis=0)
-           
-        print(f"Albums being tracked: {len(self.albums)}")
-        print("Obtaining all albums from the list of artists. (Singles)")
-        for artist_id in tqdm(self.artist_ids):
-            
-            self.check_token()
-            response = self.sp.artist_albums(artist_id, limit=lim, album_type='single', country='US')
-            offset = 0
-            more_albums = True
-
-            while more_albums:
-                
-                self.check_token()
-                batch = self.sp.artist_albums(artist_id, limit=lim, offset=offset, album_type='single', country='US')
-                response['items'].extend(batch['items'])
-                offset += lim
-
-                if len(batch['items']) < lim:
-                        more_albums = False
-
-            response_df = pd.DataFrame(response['items'])
-            response_df = response_df
-            self.albums = pd.concat([self.albums, response_df], axis=0)
-            
-        print(f"Albums being tracked: {len(self.albums)}")
-   
-    def filter_albums(self):
-        """
-        If filter_unseen is True, only releases in the window are tracked. Otherwise
-        any new piece will be added.
-        """
-        # Or Condition, either its new or hasn't been viewed
-        print("Filtering Album list for new content.")
-        if self.filter_unseen:
-            self.new_albums = self.albums[self.albums.release_date >= self.start_date]
-        else:
-            self.new_albums = self.albums[(~self.albums.id.isin(self.album_ids)) | (self.albums.release_date >= self.start_date)]
-
-        self.mdf.loc[self.rd, 'albums_augmented'] = len(self.new_albums)
-          
-    def get_album_tracks(self):
+        offset = 0
+        album_types = 'single,album'
+        country='US'
+        keys = ['album_type', 'album_group', 'id', 'name', 'release_date', "artists", 'total_tracks']
+
+        # Get All artist info
+        result = []
+        for artist in tqdm(artists):
+
+            # Initialize array for speed
+            self.refresh_connection()
+            total = int(self.sp.artist_albums(artist, country=country, album_type=album_types, limit=1)['total'])
+
+            artist_result = ['' for x in range(total)] # List of album ids pre-initialized
+            for i in range(int(np.ceil(total/lim))):
+                self.refresh_connection()
+                response = self.sp.artist_albums(artist, country=country, album_type=album_types, limit=lim, offset=(i*lim))
+                artist_result[i*lim:(i*lim)+len(response['items'])] = [{k: x[k] for k in keys} for x in response['items']]
+
+            result.extend(artist_result)
+
+        # Remove all other info about artists except ids
+        for i in range(len(result)):
+            result[i]['artists'] = [x['id'] for x in result[i]['artists']]
+
+        return result
+
+    def get_album_tracks(self, albums):
         """
-        Get all tracks off an album.
+        Returns an albums info and tracks.
         """
+        # Call info
         lim = 50
-        print("Using Filtered albums to obtain a track list.")
-        for album_id in tqdm(self.new_albums.id):
-            self.check_token()
-            response = self.sp.album_tracks(album_id, limit=lim)
-            offset = 0
-            more_tracks = True
-            if len(response['items']) < lim:
-                    more_tracks = False
-
-            while more_tracks:
-                
-                self.check_token()
-                batch = self.sp.album_tracks(album_id, limit=lim, offset=offset)
-                response['items'].extend(batch['items'])
-                offset += lim
-
-                if len(batch['items']) < lim:
-                    more_tracks = False
-
-            response_df = pd.DataFrame(response['items'])
-            self.new_tracks = pd.concat([self.new_tracks, response_df], axis=0)
-        self.mdf.loc[self.rd, 'tracks_eligible'] = len(self.new_tracks)
-    
-    def archive_current(self):
-        """
-        Stash files still in output playlist to new playlist
-        """
-        # Read-in current tracks
-        print("Archiving Current Storm Listening.")
-        current_listening = self.get_playlist_tracks(self.output)
-        current_archive = self.get_playlist_tracks(self.archive)
-        
-        try:
-            track_ids_cur = [dict(track)['id'] for track in current_listening.track]
-            track_ids_arc = [dict(track)['id'] for track in current_archive.track]
-            track_ids_writing = []
-
-            for track in track_ids_cur:
-                if track not in track_ids_arc:
-                    track_ids_writing.append(track)
-
-            # Write them to the archive playlist
-            if len(track_ids_writing) == 0:
-                print("No Unique tracks to Archive.")
-            else:
-                self.add_tracks_to_playlist(self.archive, track_ids_writing, replace=False)
-        except:
-            print("No Tracks to Archive.")
-    
-    def add_tracks_to_playlist(self, playlist_id, track_ids, replace=True):
+        country = 'US'
+        keys = ['artists', 'duration_ms', 'id', 'name', 'explicit', 'track_number']
+
+        # Get All album tracks info
+        result = []
+        for album in tqdm(albums):
+
+            # Initialize array for speed
+            self.refresh_connection()
+            total = int(self.sp.album_tracks(album, market=country, limit=1)['total'])
+
+            album_result = ['' for x in range(total)] # List of album ids pre-initialized
+            for i in range(int(np.ceil(total/lim))):
+                self.refresh_connection()
+                response = self.sp.album_tracks(album, market=country, limit=lim, offset=(i*lim))               
+                album_result[i*lim:(i*lim)+len(response['items'])] = [{k: x[k] for k in keys} for x in response['items']]
+
+            # Add the album_id back in
+            [x.update({'album_id':album}) for x in album_result]
+            result.extend(album_result)
+
+        # Remove all other info about artists except ids
+        for i in range(len(result)):
+            result[i]['artists'] = [x['id'] for x in result[i]['artists']]
+
+        return result
+
+    def get_track_features(self, tracks):
         """
-        Write new releases to output playlist.
+        Returns a tracks info and audio features
         """
-        print("Preparing Tracks for Writing")
-        lim = 50
-        if len(self.storm_track_ids) > lim:
-            split_tracks = np.array_split(track_ids, np.ceil(len(track_ids)/lim))
-
-            print("Writing Tracks")
-            if replace:
-                self.check_token()
-                self.sp.user_playlist_replace_tracks(self.user_id, playlist_id, split_tracks[0])
-                for track_list in tqdm(split_tracks[1:]):
-                    self.check_token()
-                    self.sp.user_playlist_add_tracks(self.user_id, playlist_id, track_list)
-            else:
-                for track_list in tqdm(split_tracks):
-                    self.check_token()
-                    self.sp.user_playlist_add_tracks(self.user_id, playlist_id, track_list)
-        else:
-            print("Writing Tracks")
-            if replace:
-                self.check_token()
-                self.sp.user_playlist_replace_tracks(self.user_id, playlist_id, self.storm_track_ids)
-            else:
-                self.check_token()
-                self.sp.user_playlist_add_tracks(self.user_id, playlist_id, self.storm_track_ids)
\ No newline at end of file
+        # Call info
+        id_lim = 50
+        keys = ["id", "danceability", "energy", "key", "loudness", "mode", "speechiness", "acousticness",
+        "instrumentalness", "liveness", "valence", "tempo", "time_signature"]
+        batches = np.array_split(tracks, int(np.ceil(len(tracks)/id_lim)))
+
+        # Get track features in batches
+        result = []
+        for batch in tqdm(batches):
+            self.refresh_connection()
+            response = self.sp.audio_features(batch)
+            result.extend([{k: x[k] for k in keys} for x in response if x is not None])
+
+        # Filter to just ids
+        return result
+
diff --git a/src/weatherboy.py b/src/weatherboy.py
new file mode 100644
index 0000000..4452d2a
--- /dev/null
+++ b/src/weatherboy.py
@@ -0,0 +1,14 @@
+# Modeling
+
+
+
+class WeatherBoy:
+
+    def __init__(self, tracks):
+
+        self.tracks = tracks
+
+    def rank_order():
+
+        return False
+