update,

Man1130/task1/Man1130-python-comission/tryout/jupyter-helloworld/Pipfile

@@ -0,0 +1,21 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+jupyter = "*"
+notebook = "*"
+pandas = "*"
+quandl = "*"
+seaborn = "*"
+sklearn = "*"
+scikit-learn = "*"
+pydot = "*"
+bokeh = "*"
+jupyter-bokeh = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3"

Man1130/task1/Man1130-python-comission/tryout/jupyter-helloworld/Untitled1.ipynb

@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d6162175",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Graph' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m graph \u001b[38;5;241m=\u001b[39m \u001b[43mGraph\u001b[49m(with_edges_file\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medges.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, with_nodes_file\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnodes.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'Graph' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "graph = Graph(with_edges_file=\"edges.csv\", with_nodes_file=\"nodes.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e0b539",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Man1130/task1/Man1130-python-comission/tryout/jupyter-helloworld/dev.sh

@@ -0,0 +1,14 @@
+#!/usr/bin/env bash
+
+set -ex
+
+python -m pip install --upgrade pip
+
+python -m pip install pipenv
+
+pipenv sync
+
+pipenv run \
+  jupyter-notebook \
+    --allow-root \
+    --ip=0.0.0.0

Man1130/task1/Man1130-python-comission/tryout/jupyter-helloworld/helloworld.ipynb

@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b6bf609f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "helloworld\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('helloworld')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b24b4df",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cc7f2dc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Man1130/task1/Man1130-python-comission/tryout/jupyter-helloworld/init.sh

@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+set -ex
+
+pipenv install jupyter
+pipenv install jupyter notebook
+
+pipenv install pandas
+pipenv install quandl
+
+pipenv install seaborn
+pipenv install scikit-learn
+
+# jupyter-notebook
+

Man1130/task1/Man1130-python-comission/tryout/jupyter-helloworld/journal.md

@@ -0,0 +1,27 @@
+### to spin up dev environment
+
+```
+./start_docker.sh
+
+// inside docker
+
+./dev.sh
+
+open host browser: 
+http://127.0.0.1:8888/?token=98ab80de026fe83fd8e03c8e344b31e7575ec4a084c59f21
+
+```
+
+### to develop
+
+
+start from fresh python docker image
+
+```
+./start_docker.sh
+
+./init.sh
+```
+
+
+

Man1130/task1/Man1130-python-comission/tryout/jupyter-helloworld/nodes.csv

@@ -0,0 +1,792 @@
+id,name
+2975,Laurence Fishburne
+6384,Keanu Reeves
+530,Carrie-Anne Moss
+8349,Martin Sheen
+8351,Frederic Forrest
+8354,Albert Hall
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+1397846,Craig Barron
+2166741,Robert A. Baum Jr.
+3266,Joe Mantegna
+36059,Joanna Pacula
+21010,Alexandra Hay
+101032,Rod McCary
+98569,Vincent Van Patten
+2080584,Ben Jorgensen
+103835,Jeff Monahan
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+928944,Coleman Barks
+2022250,Robert Bly
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+1165240,Jason Alan Carvell
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+2102583,Andrew Cohen
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+21111,John F. Kennedy
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+4193577,Gabin Clerc
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+1683093,Terayle Hill
+79538,Khalil Kain
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+533195,Henry Kissinger
+143280,Norman Mailer
+2801284,Anthony Amsterdam
+1220601,John Chancellor
+1066763,Darrell Royal
+50707,Ed Marinaro
+1322033,Bob Brenly
+1322034,Scott Brosius
+1233313,Joe Buck
+3273875,Bob Taylor
+3622690,Betty Ong
+3622691,Nydia Gonzalez
+3492377,Madeleine Albright
+3492379,Ilie Alexandru
+3492380,Ioan Amarie
+1442410,Trevor Phillips
+1442411,Nigel Farage
+1225259,Kirsty Young
+174981,King Charles III of the United Kingdom
+1094687,Prince Edward Duke of Edinburgh
+1306064,Princess Diana of Wales
+558240,Queen Elizabeth II of the United Kingdom
+47,Björk
+3219627,Lydia Korniordou
+2921077,Jacques Rogge
+37759,Kathy Burke
+20056,Keith Allen
+2330903,Amir Amor
+3897137,Cool 'Disco' Dan
+9290,Henry Rollins
+15535,Robin Harris
+5726,Cedric the Entertainer
+68215,Reginald Hudlin
+181975,Mark Curry
+82587,Steve Harvey
+78029,Martin Lawrence
+9309,Richard Pryor
+57551,Mario Cantone
+18291,Larenz Tate
+1633946,Chris Gattling
+10814,Wesley Snipes
+1903010,Spence Moore II
+55638,Kevin Hart
+5916,Rosario Dawson
+9206,Neve Campbell
+36811,Nick Cannon
+1870421,Collie Buddz
+2412016,Lilly Bartlam
+186529,Tony Daniels
+88059,Tommy Davidson
+15411,T.K. Carter
+1428,Mick Jagger
+1429,Charlie Watts
+53396,Ron Wood
+8534,Kathy Bates
+1121,Benicio del Toro
+3392,Michael Douglas
+17051,James Franco
+59315,Olivia Wilde
+2838,Chloë Sevigny
+155488,Kelly Clarkson
+292445,Blake Shelton
+21986,Reba McEntire
+2387,Patrick Stewart
+2388,Jonathan Frakes
+1213786,Brent Spiner
+4756,Matthew Broderick
+15152,James Earl Jones
+16940,Jeremy Irons
+148120,Tiffany
+83170,Valerie Harper
+2774840,Luis Miranda
+1179651,Lin-Manuel Miranda
+216674,Hillary Clinton
+73641,Christiane Amanpour
+1202593,Neil Barnard
+33684,Bono
+1650331,Jeremy O. Harris
+350,Laura Linney
+2391,Michael Dorn
+172069,Chadwick Boseman
+1215522,Robin Roberts
+1896,Don Cheadle
+6240,Mariska Hargitay
+21411,Ice-T
+206637,Kelli Giddish
+120608,Sonia Manzano
+1596910,Nitya Vidyasagar
+934281,Alex Wolff
+1700685,Lewis Pullman
+1241339,Bo Mitchell
+14386,Beyoncé
+1214573,Serena Williams
+93527,Kelly Rowland
+167662,Ryan Seacrest
+2384929,Lindsay Arnold
+138986,Tony Bennett
+72208,Alicia Keys
+3284,Bruce Springsteen
+18746,Stevie Wonder
+37934,André 3000
+15310,Anthony Kiedis
+12207,Kylie Minogue
+1564846,Auli'i Cravalho
+1473281,James Monroe Iglehart
+2522829,Vanessa Laine Bryant
+23678,Michael Jordan
+35806,Shaquille O'Neal
+1212304,Jon Scieszka
+1192274,Jordan Fisher
+132354,Kate Micucci
+133451,Aya Cash
+111684,Lennon Parham
+60227,Greg Cipes
+1249820,Romi Dames
+8536,Victor Garber
+1178795,Ben Diskin
+1842039,Joe Tessitore
+71403,Rob Riggle
+129193,Paula Abdul
+75394,Terence Donovan
+1083951,Veronica Lang
+500,Tom Cruise
+150536,Barry Otto
+76182,Nicholas Eadie
+76788,Dev Patel
+108916,Rooney Mara
+1371,David Wenham
+113552,Briony Behets
+1242514,James Smillie
+1246450,Bartholomew John
+1747292,Robert Board
+106179,Anthony Franciosa
+82315,Jean Harlow
+11492,Clark Gable
+1916574,Caitlin Doughty
+5168,Gabriel Byrne
+516,Annette Bening
+4517,Joe Pesci
+71531,Deepak Chopra
+1383964,Thích Nhất Hạnh
+1867979,Sister Chân Không
+8293,Marion Cotillard
+32017,Didier Lavergne
+3753843,Bryna Rifkin
+5682,Anouk Aimée
+44556,Richard Anconina
+20234,Fanny Ardant
+40159,Keith Michell
+58414,Dina Merrill
+4,Carrie Fisher
+6541,Philip Bosco
+157359,Jane Connell
+1729824,Randy Doney
+1392152,Dan Levy
+26510,Eugene Levy
+11514,Catherine O'Hara
+290,Christopher Plummer
+21104,Glenne Headly
+80866,Nell Carter
+40063,Lea DeLaria
+537064,Perry Como
+109887,Caroll Spinney
+8515,Jane Darwell
+5829,Karen Dotrice
+1201,Garry Marshall
+1041410,Charmian Carr
+22384,John Amos
+105637,Morey Amsterdam
+6613,Minnie Driver
+57133,Paulo Costanzo
+18461,Nick Chinlund
+1216139,Michael Raynor
+57389,Justin Pierce
+87525,Alan Carr
+7056,Emma Thompson
+154782,Pat Benatar
+154783,Neil Giraldo
+174875,Carly Simon
+929825,Ava DuVernay
+2302408,Korey Wise
+20019,Lee Daniels
+1658802,Andra Day
+964843,Michelle Obama
+3889844,Haizel Adofo
+113461,John Legend
+206444,Constance Wu
+1700631,Liza Koshy
+2503684,Abdullah bin Abdulaziz Al Saud
+107379,LeBron James
+66620,Angela Merkel
+2134341,Theresa May
+3089883,Gabby Giffords
+1608713,Mark Kelly
+1068409,Frank Marshall Davis
+1068410,Ann Dunham
+1068411,Stanley Dunham
+149923,Lester Brown
+1063969,Yvo de Boer
+1063970,Paul R. Ehrlich
+10279,Adolf Hitler
+89300,Nelson Mandela
+3451053,Natalya Sindeeva
+3527796,Aleksandr Vinokurov
+1745467,Vera Krichevskaya
+1173480,François Hollande
+1348088,Laurent Delahousse
+1977908,Claire Chazal
+2169865,Birgitta Assheuer
+2920376,Îlham Ahmed
+2920377,Zaher al-Saket

Man1130/task1/Man1130-python-comission/tryout/jupyter-helloworld/start_docker.sh

@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+
+set -ex
+
+docker run -it \
+  -v $PWD:/app \
+  -w /app \
+  -v /var/run/docker.sock:/var/run/docker.sock \
+  -v ~/.ssh/id_rsa:/home/node/.ssh/id_rsa:ro \
+  -v ~/.ssh/known_host:/home/node/.ssh/known_hosts:ro \
+  -p 8888:8888 \
+  --rm \
+  python:3.10 \
+  bash
+  
+  # -u 1000:1000 \
+  # -e XDG_CACHE_HOME=/app/.cache \
+  

Man1130/task1/Man1130-python-comission/tryout/jupyter-helloworld/submission.py

@@ -0,0 +1,448 @@
+import http.client
+import json
+import csv
+
+
+#############################################################################################################################
+# cse6242 
+# All instructions, code comments, etc. contained within this notebook are part of the assignment instructions.
+# Portions of this file will auto-graded in Gradescope using different sets of parameters / data to ensure that values are not
+# hard-coded.
+#
+# Instructions:  Implement all methods in this file that have a return
+# value of 'NotImplemented'. See the documentation within each method for specific details, including
+# the expected return value
+#
+# Helper Functions:
+# You are permitted to write additional helper functions/methods or use additional instance variables within
+# the `Graph` class or `TMDbAPIUtils` class so long as the originally included methods work as required.
+#
+# Use:
+# The `Graph` class  is used to represent and store the data for the TMDb co-actor network graph.  This class must
+# also provide some basic analytics, i.e., number of nodes, edges, and nodes with the highest degree.
+#
+# The `TMDbAPIUtils` class is used to retrieve Actor/Movie data using themoviedb.org API.  We have provided a few necessary methods
+# to test your code w/ the API, e.g.: get_movie_cast(), get_movie_credits_for_person().  You may add additional
+# methods and instance variables as desired (see Helper Functions).
+#
+# The data that you retrieve from the TMDb API is used to build your graph using the Graph class.  After you build your graph using the
+# TMDb API data, use the Graph class write_edges_file & write_nodes_file methods to produce the separate nodes and edges
+# .csv files for use with the Argo-Lite graph visualization tool.
+#
+# While building the co-actor graph, you will be required to write code to expand the graph by iterating
+# through a portion of the graph nodes and finding similar artists using the TMDb API. We will not grade this code directly
+# but will grade the resulting graph data in your Argo-Lite graph snapshot.
+#
+#############################################################################################################################
+
+
+class Graph:
+
+    # Do not modify
+    def __init__(self, with_nodes_file=None, with_edges_file=None):
+        """
+        option 1:  init as an empty graph and add nodes
+        option 2: init by specifying a path to nodes & edges files
+        """
+        self.nodes = []
+        self.edges = []
+        if with_nodes_file and with_edges_file:
+            nodes_CSV = csv.reader(open(with_nodes_file))
+            nodes_CSV = list(nodes_CSV)[1:]
+            self.nodes = [(n[0], n[1]) for n in nodes_CSV]
+
+            edges_CSV = csv.reader(open(with_edges_file))
+            edges_CSV = list(edges_CSV)[1:]
+            self.edges = [(e[0], e[1]) for e in edges_CSV]
+
+
+    def add_node(self, id: str, name: str) -> None:
+        """
+        add a tuple (id, name) representing a node to self.nodes if it does not already exist
+        The graph should not contain any duplicate nodes
+        """
+        if (id, name) not in self.nodes:
+            self.nodes.append((id, name))
+
+        # return NotImplemented
+
+
+    def add_edge(self, source: str, target: str) -> None:
+        """
+        Add an edge between two nodes if it does not already exist.
+        An edge is represented by a tuple containing two strings: e.g.: ('source', 'target').
+        Where 'source' is the id of the source node and 'target' is the id of the target node
+        e.g., for two nodes with ids 'a' and 'b' respectively, add the tuple ('a', 'b') to self.edges
+        """
+        current_edge = (source, target)
+        current_edge_inverted = (target, source)
+        if (source != target) and (current_edge not in self.edges) and (current_edge_inverted not in self.edges):
+            self.edges.append(current_edge)
+
+        # return NotImplemented
+
+
+    def total_nodes(self) -> int:
+        """
+        Returns an integer value for the total number of nodes in the graph
+        """
+        return len(self.nodes)
+        # return NotImplemented
+
+    def total_edges(self) -> int:
+        """
+        Returns an integer value for the total number of edges in the graph
+        """
+        return len(self.edges)
+        # return NotImplemented
+
+    def non_leaf_nodes(self):
+        nodes_degrees = {}
+        for edge in self.edges:
+            for idx in edge:
+                if idx in nodes_degrees:
+                    nodes_degrees[idx] += 1
+                else:
+                    nodes_degrees[idx] = 1
+    
+        non_leaf_nodes = dict((k, v) for k, v in nodes_degrees.items() if v > 1)
+        return len(non_leaf_nodes)
+
+
+
+    def max_degree_nodes(self) -> dict:
+        """
+        Return the node(s) with the highest degree
+        Return multiple nodes in the event of a tie
+        Format is a dict where the key is the node_id and the value is an integer for the node degree
+        e.g. {'a': 8}
+        or {'a': 22, 'b': 22}
+        """
+
+        max_degree_nodes = {}
+
+        for edge in self.edges:
+            for idx in edge:
+                if idx in max_degree_nodes:
+                    max_degree_nodes[idx] += 1
+                else:
+                    max_degree_nodes[idx] = 1
+
+        v = list(max_degree_nodes.values())
+        k = list(max_degree_nodes.keys())
+        max_nodes = {k[v.index(max(v))]: max(v)}
+        print(max_nodes)
+        return max_nodes
+
+    def print_nodes(self):
+        """
+        No further implementation required
+        May be used for de-bugging if necessary
+        """
+        print(self.nodes)
+
+    def print_edges(self):
+        """
+        No further implementation required
+        May be used for de-bugging if necessary
+        """
+        print(self.edges)
+
+    # Do not modify
+    def write_edges_file(self, path="edges.csv")->None:
+        """
+        write all edges out as .csv
+        :param path: string
+        :return: None
+        """
+        edges_path = path
+        edges_file = open(edges_path, 'w', encoding='utf-8')
+
+        edges_file.write("source" + "," + "target" + "\n")
+
+        for e in self.edges:
+            edges_file.write(e[0] + "," + e[1] + "\n")
+
+        edges_file.close()
+        print("finished writing edges to csv")
+
+    # Do not modify
+    def write_nodes_file(self, path="nodes.csv")->None:
+        """
+        write all nodes out as .csv
+        :param path: string
+        :return: None
+        """
+        nodes_path = path
+        nodes_file = open(nodes_path, 'w', encoding='utf-8')
+
+        nodes_file.write("id,name" + "\n")
+        for n in self.nodes:
+            nodes_file.write(n[0] + "," + n[1] + "\n")
+        nodes_file.close()
+        print("finished writing nodes to csv")
+
+
+class TMDBAPIUtils:
+
+    # Do not modify
+    def __init__(self, api_key:str):
+        self.api_key=api_key
+
+    def get_movie_cast(self, movie_id:str, limit:int=None, exclude_ids:list=None) -> list:
+        """
+        Get the movie cast for a given movie id, with optional parameters to exclude an cast member
+        from being returned and/or to limit the number of returned cast members
+        documentation url: https://developers.themoviedb.org/3/movies/get-movie-credits
+
+        :param integer movie_id: a movie_id
+        :param integer limit: maximum number of returned cast members by their 'order' attribute
+            e.g., limit=5 will attempt to return the 5 cast members having 'order' attribute values between 0-4
+            If after excluding, there are fewer cast members than the specified limit, then return the remaining members (excluding the ones whose order values are outside the limit range). 
+            If cast members with 'order' attribute in the specified limit range have been excluded, do not include more cast members to reach the limit.
+            If after excluding, the limit is not specified, then return all remaining cast members."
+            e.g., if limit=5 and the actor whose id corresponds to cast member with order=1 is to be excluded,
+            return cast members with order values [0, 2, 3, 4], not [0, 2, 3, 4, 5]
+        :param list exclude_ids: a list of ints containing ids (not cast_ids) of cast members  that should be excluded from the returned result
+            e.g., if exclude_ids are [353, 455] then exclude these from any result.
+        :rtype: list
+            return a list of dicts, one dict per cast member with the following structure:
+                [{'id': '97909' # the id of the cast member
+                'character': 'John Doe' # the name of the character played
+                'credit_id': '52fe4249c3a36847f8012927' # id of the credit, ...}, ... ]
+                Note that this is an example of the structure of the list and some of the fields returned by the API.
+                The result of the API call will include many more fields for each cast member.
+
+        Important: the exclude_ids processing should occur prior to limiting output.
+        """
+
+        connection = http.client.HTTPSConnection('api.themoviedb.org')
+        connection.request("GET", "/3/movie/{0}/credits?api_key={1}&language=en-US".format(movie_id, self.api_key))
+        response = connection.getresponse()
+        if response.status == 200:
+            data = response.read()
+            decoded_data = json.loads(data.decode('UTF-8'))
+            cast = decoded_data['cast']
+            if exclude_ids is not None:
+                cast = [i for i in cast if i['id'] not in exclude_ids]
+                print(cast)
+            if limit is not None:
+                cast = [i for i in cast if i['order'] < limit]
+                print(cast)
+            for c in cast:
+                c['name'] = c['name'].replace(',', '')
+            return cast
+        else:
+            print(response.status.__str__())
+            return None
+
+    def get_movie_credits_for_person(self, person_id:str, vote_avg_threshold:float=None)->list:
+        """
+        Using the TMDb API, get the movie credits for a person serving in a cast role
+        documentation url: https://developers.themoviedb.org/3/people/get-person-movie-credits
+
+        :param string person_id: the id of a person
+        :param vote_avg_threshold: optional parameter to return the movie credit if it is >=
+            the specified threshold.
+            e.g., if the vote_avg_threshold is 5.0, then only return credits with a vote_avg >= 5.0
+        :rtype: list
+            return a list of dicts, one dict per movie credit with the following structure:
+                [{'id': '97909' # the id of the movie credit
+                'title': 'Long, Stock and Two Smoking Barrels' # the title (not original title) of the credit
+                'vote_avg': 5.0 # the float value of the vote average value for the credit}, ... ]
+        """
+
+        connection = http.client.HTTPSConnection('api.themoviedb.org')
+        connection.request("GET", "/3/person/{0}/movie_credits?api_key={1}&language=en-US".format(person_id, self.api_key))
+        response = connection.getresponse()
+        if response.status == 200:
+            data = response.read()
+            decoded_data = json.loads(data.decode('UTF-8'))
+            raw_credits = decoded_data['cast']
+            if vote_avg_threshold is not None:
+                filtered_credits = [{"id":idx["id"], "title":idx["title"], "vote_avg":idx["vote_average"]} for idx in raw_credits if idx["vote_average"] >= vote_avg_threshold]
+                print(filtered_credits)
+                return filtered_credits
+            else:
+                unfiltered_credits = [{"id":idx["id"], "title":idx["title"], "vote_avg":idx["vote_average"]} for idx in raw_credits]
+                print(unfiltered_credits)
+                return unfiltered_credits
+        else:
+            print("ERROR: " + response.status.__str__())
+            return None
+
+
+
+#############################################################################################################################
+#
+# BUILDING YOUR GRAPH
+#
+# Working with the API:  See use of http.request: https://docs.python.org/3/library/http.client.html#examples
+#
+# Using TMDb's API, build a co-actor network for the actor's/actress' highest rated movies
+# In this graph, each node represents an actor
+# An edge between any two nodes indicates that the two actors/actresses acted in a movie together
+# i.e., they share a movie credit.
+# e.g., An edge between Samuel L. Jackson and Robert Downey Jr. indicates that they have acted in one
+# or more movies together.
+#
+# For this assignment, we are interested in a co-actor network of highly rated movies; specifically,
+# we only want the top 3 co-actors in each movie credit of an actor having a vote average >= 8.0.
+# Build your co-actor graph on the actor 'Laurence Fishburne' w/ person_id 2975.
+#
+# You will need to add extra functions or code to accomplish this.  We will not directly call or explicitly grade your
+# algorithm. We will instead measure the correctness of your output by evaluating the data in your argo-lite graph
+# snapshot.
+#
+# GRAPH SIZE
+# With each iteration of your graph build, the number of nodes and edges grows approximately at an exponential rate.
+# Our testing indicates growth approximately equal to e^2x.
+# Since the TMDB API is a live database, the number of nodes / edges in the final graph will vary slightly depending on when
+# you execute your graph building code. We take this into account by rebuilding the solution graph every few days and
+# updating the auto-grader.  We establish a bound for lowest & highest encountered numbers of nodes and edges with a
+# margin of +/- 100 for nodes and +/- 150 for edges.  e.g., The allowable range of nodes is set to:
+#
+# Min allowable nodes = min encountered nodes - 100
+# Max allowable nodes = max allowable nodes + 100
+#
+# e.g., if the minimum encountered nodes = 507 and the max encountered nodes = 526, then the min/max range is 407-626
+# The same method is used to calculate the edges with the exception of using the aforementioned edge margin.
+# ----------------------------------------------------------------------------------------------------------------------
+# BEGIN BUILD CO-ACTOR NETWORK
+#
+# INITIALIZE GRAPH
+#   Initialize a Graph object with a single node representing Laurence Fishburne
+#
+# BEGIN BUILD BASE GRAPH:
+#   Find all of Laurence Fishburne's movie credits that have a vote average >= 8.0
+#   FOR each movie credit:
+#   |   get the movie cast members having an 'order' value between 0-2 (these are the co-actors)
+#   |
+#   |   FOR each movie cast member:
+#   |   |   using graph.add_node(), add the movie cast member as a node (keep track of all new nodes added to the graph)
+#   |   |   using graph.add_edge(), add an edge between the Laurence Fishburne (actress) node
+#   |   |   and each new node (co-actor/co-actress)
+#   |   END FOR
+#   END FOR
+# END BUILD BASE GRAPH
+#
+#
+# BEGIN LOOP - DO 2 TIMES:
+#   IF first iteration of loop:
+#   |   nodes = The nodes added in the BUILD BASE GRAPH (this excludes the original node of Laurence Fishburne!)
+#   ELSE
+#   |    nodes = The nodes added in the previous iteration:
+#   ENDIF
+#
+#   FOR each node in nodes:
+#   |  get the movie credits for the actor that have a vote average >= 8.0
+#   |
+#   |   FOR each movie credit:
+#   |   |   try to get the 3 movie cast members having an 'order' value between 0-2
+#   |   |
+#   |   |   FOR each movie cast member:
+#   |   |   |   IF the node doesn't already exist:
+#   |   |   |   |    add the node to the graph (track all new nodes added to the graph)
+#   |   |   |   ENDIF
+#   |   |   |
+#   |   |   |   IF the edge does not exist:
+#   |   |   |   |   add an edge between the node (actor) and the new node (co-actor/co-actress)
+#   |   |   |   ENDIF
+#   |   |   END FOR
+#   |   END FOR
+#   END FOR
+# END LOOP
+#
+# Your graph should not have any duplicate edges or nodes
+# Write out your finished graph as a nodes file and an edges file using:
+#   graph.write_edges_file()
+#   graph.write_nodes_file()
+#
+# END BUILD CO-ACTOR NETWORK
+# ----------------------------------------------------------------------------------------------------------------------
+
+# Exception handling and best practices
+# - You should use the param 'language=en-US' in all API calls to avoid encoding issues when writing data to file.
+# - If the actor name has a comma char ',' it should be removed to prevent extra columns from being inserted into the .csv file
+# - Some movie_credits may actually be collections and do not return cast data. Handle this situation by skipping these instances.
+# - While The TMDb API does not have a rate-limiting scheme in place, consider that making hundreds / thousands of calls
+#   can occasionally result in timeout errors. If you continue to experience 'ConnectionRefusedError : [Errno 61] Connection refused',
+#   - wait a while and then try again.  It may be necessary to insert periodic sleeps when you are building your graph.
+
+
+def return_name()->str:
+    """
+    Return a string containing your GT Username
+    e.g., gburdell3
+    Do not return your 9 digit GTId
+    """
+    return "kobrien43"
+
+
+def return_argo_lite_snapshot()->str:
+    """
+    Return the shared URL of your published graph in Argo-Lite
+    """
+    return "https://poloclub.github.io/argo-graph-lite/#1ac32aec-ba9f-4693-83e1-cd4a7e81fb93"
+
+
+# You should modify __main__ as you see fit to build/test your graph using  the TMDBAPIUtils & Graph classes.
+# Some boilerplate/sample code is provided for demonstration. We will not call __main__ during grading.
+
+
+if __name__ == "__main__":
+
+    # init graph object with a single node representing laurence fishburne
+    graph = Graph()
+    graph.add_node(id='2975', name='Laurence Fishburne')
+
+    # build base graph
+    # find all laurence fishburne's movie credits with a vote average >= 8.0
+    tmdb_api_utils = TMDBAPIUtils(api_key='25166b54e76c7ad3aaa306abdfecb190')
+    movie_credits = tmdb_api_utils.get_movie_credits_for_person(person_id='2975', vote_avg_threshold=8.0)
+    print(len(movie_credits))
+    print(movie_credits)
+
+    # for each movie credit get movie cast members having and order value between 0-2 (3 0-base)
+    for movie in movie_credits:
+        movie_cast = tmdb_api_utils.get_movie_cast(movie_id=movie['id'], limit=3)
+        for cast_member in movie_cast:
+            graph.add_node(str(cast_member['id']), cast_member['name'])
+            graph.add_edge(source='2975', target=str(cast_member['id']))
+
+    for i in range(2):
+        if i == 0:
+            current_nodes = graph.nodes[1:]
+        else:
+            current_nodes = [i for i in graph.nodes if i not in current_nodes]
+
+        for node in current_nodes:
+            cast = tmdb_api_utils.get_movie_credits_for_person(person_id=node[0], vote_avg_threshold=8.0)
+            for movie in cast:
+                actors = tmdb_api_utils.get_movie_cast(movie_id=movie['id'], limit=3)
+                for actor in actors:
+                    graph.add_node(str(actor['id']), actor['name'])
+                    graph.add_edge(str(node[0]), str(actor['id']))
+
+
+
+    # tmdb_api_utils.get_movie_cast(movie_id='100', limit=3, exclude_ids=[973, 974])
+    # tmdb_api_utils.get_movie_credits_for_person(person_id="9709", vote_avg_threshold=5.0)
+
+    # call functions or place code here to build graph (graph building code not graded)
+    # Suggestion: code should contain steps outlined above in BUILD CO-ACTOR NETWORK
+
+    print("Edges: " + str(graph.total_edges()))
+    print("Nodes: " + str(graph.total_nodes()))
+
+    graph.write_edges_file()
+    graph.write_nodes_file()
+
+    # If you have already built & written out your graph, you could read in your nodes & edges files
+    # to perform testing on your graph.
+    graph = Graph(with_edges_file="edges.csv", with_nodes_file="nodes.csv")
+
+    #get non-leaf nodes
+    non_leaf = graph.non_leaf_nodes()
+    print(non_leaf - 10)
+    print("DONE")