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日期:2020-08-31 10:12

Data Analytics - Python & other tools

Fall 2020

HW 1: End-to-end analysis of TMDb data, Argo-Lite, SQLite, D3 Warmup, OpenRefine, Flask

Use HW1 Skeleton zip file and unzip to folders.

Homework Overview

In Question 1 (Q1), you will collect data using an API for The Movie Database (TMDb). You will construct a

graph representation of this data that will show which actors have acted together in various movies, and use

Argo Lite to visualize this graph and highlight patterns that you find. This exercise demonstrates

how visualizing and interacting with data can help with discovery.

In Q2, you will construct a TMDb database in SQLite, with tables capturing information such as how well

each movie did, which actors acted in each movie, and what the movie was about. You will also

partition and combine information in these tables in order to more easily answer questions such as "which

actors acted in the highest number of movies?".

In Q3, you will visualize temporal trends in movie releases, using a JavaScript-based library called D3. This

part will show how creating interactive rather than static plots can make data more visually appealing,

engaging and easier to parse.

In Q4, you will use OpenRefine to clean data from Mercari, and construct GREL queries to filter the entries

in this dataset.

In Q5, you will build a simple web application that displays a table of TMDb data on a single-page website.

To do this, you will use Flask, a Python framework for building web applications that allows you to connect

Python data processing on the back end with serving a site that displays these results.

Extremely Important: folder structure & content of submission zip file

The zip file’s folder structure must exactly be (when unzipped):

HW1-username/

Q1/

Q2/

Q3/

submission.py

Q2_SQL.py

index.(html / js / css)

q3.csv

lib/

d3/

d3.min.js

d3-fetch/

d3-fetch.min.js

d3-dsv/

d3-dsv.min.js

Q4/

Q5/

properties_clean.csv

changes.json

Q4Observations.txt

wrangling.py

2 Version 1

Q1 [40 points] Collect data from TMDb and visualize co-actor network

Q1.1 [30 points] Collect data from TMDb and build a graph

For this Q1.1, you will be using and submitting a python file - submission.py in folder Q1

Complete all tasks according to the instructions found in submission.py to complete the Graph class, the

TMDbAPIUtils class, and the two global functions. The Graph class will serve as a re-usable way to

represent and write out your collected graph data. The TMDbAPIUtils class will be used to work with the

TMDB API for data retrieval.

NOTE: You must only use a version of Python ≥ 3.7.0 and < 3.8 for this question. You must not use any

other versions (e.g., Python 3.8).

NOTE: You must only use the modules and libraries provided at the top of submission.py and modules

from the Python Standard Library. Pandas and Numpy CANNOT be used

NOTE: We will call each function once in submission.py during grading. You may lose some points if your

program runs for unreasonably long time, such as more than 10 minutes during “non-busy” times. The

average runtime of the code during grading is expected to take approximately 4 seconds.

a) [10 pts] Implementation of the Graph class according to the instructions in submission.py

b) [10 pts] Implementation of the TMDbAPIUtils class according to the instructions in

submission.py. You will use version 3 of the TMDb API to download data about actors and their

co-actors. To use the TMDb API:

o Use the TMDb API key to access the TMDb data: 74a32a264202ce8e09c7

o Refer to the TMDB API Documentation, which contains a helpful ‘try-it-out’ feature for

interacting with the API calls.

c) [10 pts] Producing correct nodes.csv and edges.csv. You must upload your nodes.csv and

edges.csv file as directed in Q1.2.

NOTE: Q1.2 builds on the results of Q1.1

3 Version 1

Q1.2 [10 points] Visualizing a graph of co-actors using Argo-Lite

Using Argo Lite, visualize a network of actors and their co-actors. You can access Argo Lite here

You will produce an Argo Lite graph snapshot your edges.csv and nodes.csv from Q1.1.c.

a. To get started, review Argo Lite’s readme on GitHub. Argo Lite has been open-sourced.

b. Importing your Graph

● Launch Argo Lite

● From the menu bar, click ‘Graph’ → ‘Import CSV’. In the dialogue that appears:

o Select ‘I have both nodes and edges file’

● Under Nodes, use ‘Choose File’ to select nodes.csv from your computer

o Leave 'Has Headers' selected

o Verify ‘Column for Node ID’ is ‘id’

● Under Edges, use ‘Choose File’ to select edges.csv from your computer

o Verify ‘Column for Source ID’ is ‘source’

o Select ‘Column for Target ID’ to ‘target’

o Verify ‘Selected Delimiter’ is ','

● At the bottom of the dialogue, verify that ‘After import, show’ is ‘All Nodes’

● The graph will load in the window. Note that the layout is paused by default; you can select

to 'Resume’ or ‘Pause’ layout as needed.

● Dragging a node will 'pin' it, freezing its position. Selecting a pinned node, right clicking it,

then choosing 'unpin selected' will unpin that node, so its position will once again be

computed by the graph layout algorithm. Experiment with pinning and unpinning nodes.

c. [7 points] Setting graph display options

● On “Graph Options” panel, under 'Nodes' → 'Modifying All Nodes', expand 'Color' menu

o Select Color by 'degree', with scale: ‘Linear Scale’

o Select a color gradient of your choice that will assign lighter colors to nodes with higher

node degrees, and darker colors to nodes with lower degrees

● Collapse the 'Color' options, expand the 'Size' options.

o Select 'Scale by' to 'degree', with scale: Linear Scale'

o Select meaningful Size Range values of your choice or use the default range.

● Collapse the 'Size' options

● On the Menu, click ‘Tools’ → ‘Data Sheet’

● Within the ‘Data Sheet’ dialogue:

o Click ‘Hide All’

o Set ‘10 more nodes with highest degree’

o Click ‘Show’ and then close the ‘Data Sheet’ dialogue

● Click and drag a rectangle selection around the visible nodes

● With the nodes selected, configure their node visibility by setting the following:

o Go to 'Graph Options' → 'Labels'

o Click ‘Show Labels of Selected Nodes’

o At the bottom of the menu, select 'Label By' to ‘name'

o Adjust the ‘Label Length’ so that the full text of the actor name is displayed

● On the Menu, click ‘Tools’ -> ‘Filters’ -> ‘Show All Nodes’ The result of this workflow yields a

graph with the sizing and coloring depending upon the node degree and the nodes with the

highest degree are emphasized by showing their labels.

d. [3 points] Designing a meaningful graph layout

4 Version 1

Using the following guidelines, create a visually meaningful and appealing layout:

● Reduce as much edge crossing as possible

● Reduce node overlap as much as possible

● Keep the graph compact and symmetric as possible

● Use the nodes’ spatial positions to convey information (e.g., “clusters” or groups)

● Experiment with showing additional node labels. If showing all node labels creates too much

visual complexity, show at least 10 “important” nodes. You may decide what “importance”

mean to you. For example, you may consider nodes (actors) having higher connectivity as

potentially more “important” (based on how the graph is built).

The objective of this task is to familiarize yourself with basic, important graph visualization features.

Therefore, this is an open-ended task, and most designs receive full marks. So please experiment

with Argo Lite’s features, changing node size and shape, etc. In practice, it is not possible to create

“perfect” visualizations for most graph datasets. The above guidelines are ones that generally help.

However, like most design tasks, creating a visualization is about making selective design

compromises. Some guidelines could create competing demands and following all guidelines may

not guarantee a “perfect” design.

If you want to save your Argo Lite graph visualization snapshot locally to your device, so you can

continue working on it later, we recommend the following workflow.

● Select 'Graph' → 'Save Snapshot'

o In the 'Save Snapshot` dialog, click 'Copy to Clipboard'

o Open an external text editor program such as TextEdit or Notepad. Paste the clipboard

contents of the graph snapshot, and save it to a file with a .json extension. You should

be able to accomplish this with a default text editor on your computer by overriding the

default file extension and manually entering ‘.json’.

o You may save your progress by saving the snapshot and loading them into Argo Lite to

continue your work.

● To load a snapshot, choose 'Graph' → 'Open Snapshot'

● Select the graph snapshot you created.

e. Publish and Share your graph snapshot

● Select 'Graph ' → 'Publish and Share Snapshot' → 'Share’

● Next, click 'Copy to Clipboard' to copy the generated URL

● Return the URL in the return_argo_lite_snapshot() function in submission.py

If you modify your graph after you publish and share a URL, you will need to re-publish and obtain a

new URL of your latest graph. Only the graph snapshot shared via the URL will be graded.

Deliverables: Place the files listed below in the Q1 folder.

? submission.py: the completed Python file

Q2 [35 points] SQLite

SQLite is a lightweight, serverless, embedded database that can easily handle multiple gigabytes of data. It

is one of the world’s most popular embedded database systems. It is convenient to share data stored in an

SQLite database — just one cross-platform file which does not need to be parsed explicitly (unlike CSV

5 Version 1

files, which have to be parsed).

You will modify the given Q2_SQL.py file in folder Q2 by adding SQL statements to it.

NOTE: You must only use a version of Python ≥ 3.7.0 and < 3.8 for this question. You must not use any

other versions (e.g., Python 3.8)

NOTE: Do not modify the import statements, everything you need to complete this question has been

imported for you. You may not use other libraries for this assignment.

A Sample class has been provided for you to see some sample SQL statements, you can turn off this output

by changing the global variable SHOW to False. NOTE: This must be set to false before uploading to

Gradescope and turning it in to Canvas.

username - use this name, e.g. mhull32

NOTE: For the questions in this section, you must only use INNER JOIN when performing a join

between two tables. Other types of joins may result in incorrect results.

a. [9 points] Create tables and import data.

i. [2 points] Create two tables (via two separate methods) named movies and movie_cast with

columns having the indicated data types:

1. movies

1. id (integer)

2. title (text)

3. score (real)

2. movie_cast

1. movie_id (integer)

2. cast_id (integer)

3. cast_name (text)

4. birthday (text)

5. popularity (real)

ii. [2 points] Import the provided movies.csv file into the movies table and movie_cast.csv into

the movie_cast table

1. You will write Python code that imports the .csv files into the individual tables. This will

include looping though the file and using the ‘INSERT INTO’ SQL command. Only use

relative paths while importing files since absolute/local paths are specific locations that

exist only on your computer and will cause the auto-grader to fail.

iii. [5 points] Vertical Database Partitioning. Database partitioning is an important technique that

divides large tables into smaller tables, which may help speed up queries. For this question you

will create a new table cast_bio from the movie_cast table (i.e., columns in cast_bio will

be a subset of those in movie_cast) Do not edit the movie_cast table. Be sure that when you

insert into the new cast_bio that the values are unique. Please read this page for an example

of vertical database partitioning.

cast_bio

1. cast_id (integer)

2. cast_name (text)

3. birthday (date)

6 Version 1

4. popularity (real)

b. [1 point] Create indexes. Create the following indexes for the tables specified below. This step increases

the speed of subsequent operations; though the improvement in speed may be negligible for this small

database, it is significant for larger databases.

i. movie_index for the id column in movies table

ii. cast_index for the cast_id column in movie_cast table

iii. cast_bio_index for the cast_id column in cast_bio table

c. [3 points] Calculate a proportion. Find the proportion of movies having a score > 50 and that has ‘war’ in

the name. Treat each row as a different movie. The proportion should only be based on the total number

of rows in the movie table. Format all decimals to two places using printf(). Do NOT use the

ROUND() function as it does not work the same on every OS.

Output format and sample value:

7.70

d. [4 points] Find the most prolific actors. List 5 cast members with the highest number of movie

appearances that have a popularity > 10. Sort the results by the number of appearances in descending

order, then by cast_name in alphabetical order.

Output format and sample values (cast_name,appearance_count):

Harrison Ford,2

e. [4 points] Find the highest scoring movies with the smallest cast. List the 5 highest-scoring movies that

have the fewest cast members. Sort the results by score in descending order, then by number of cast

members in ascending order, then by movie name in alphabetical order. Format all decimals to two

places using printf().

Output format and sample values (movie_title,movie_score,cast_count):

Star Wars: Holiday Special,75.01,12

War Games,58.49,33

f. [4 points] Get high scoring actors. Find the top ten cast members who have the highest average movie

scores. Format all decimals to two places using printf().

? Sort the output by average score in descending order, then by cast_name in alphabetical order.

? Do not include movies with score <25 in the average score calculation.

? Exclude cast members who have appeared in two or fewer movies.

Output format and sample values (cast_id,cast_name,average_score):

8822,Julia Roberts,53.00

g. [6 points] Creating views. Create a view (virtual table) called good_collaboration that lists pairs of

actors who have had a good collaboration as defined here. Each row in the view describes one pair of

actors who appeared in at least 3 movies together AND the average score of these movies is >= 40.

The view should have the format:

good_collaboration(

cast_member_id1,

7 Version 1

cast_member_id2,

movie_count,

average_movie_score)

For symmetrical or mirror pairs, only keep the row in which cast_member_id1 has a lower

numeric value. For example, for ID pairs (1, 2) and (2, 1), keep the row with IDs (1, 2). There

should not be any “self pair” where the value of cast_member_id1 is the same as that of

cast_member_id2.

NOTE: Full points will only be awarded for queries that use joins for part g.

Remember that creating a view will not produce any output, so you should test your view with a

few simple select statements during development. One such test has already been added to the

code as part of the auto-grading.

NOTE: Do not submit any code that creates a ‘TEMP’ or ‘TEMPORARY’ view that you may

have used for testing.

Optional Reading: Why create views?

i. [4 points] Find the best collaborators. Get the 5 cast members with the highest average scores

from the good_collaboration view, and call this score the collaboration_score. This

score is the average of the average_movie_score corresponding to each cast member,

including actors in cast_member_id1 as well as cast_member_id2. Format all decimals to

two places using printf().

? Sort your output by this score in descending order, then by cast_name alphabetically.

Output format (cast_id,cast_name,collaboration_score):

2,Mark Hamil,99.32

1920,Winoa Ryder,88.32

h. [4 points] SQLite supports simple but powerful Full Text Search (FTS) for fast text-based querying (FTS

documentation). Import movie overview data from the movie_overview.csv into a new FTS table called

movie_overview with the schema:

movie_overview

? id (integer)

? overview (text)

NOTE: Create the table using fts3 or fts4 only. Also note that keywords like NEAR, AND, OR and NOT

are case sensitive in FTS queries.

i. [1 point] Count the number of movies whose overview field contains the word ‘fight’. Matches

are not case sensitive. Match full words, not word parts/sub-strings.

e.g., Allowed: ‘FIGHT’, ‘Fight’, ‘fight’, ‘fight.’. Disallowed: ‘gunfight’, ‘fighting’, etc.

Output format:

12

ii. [2 points] Count the number of movies that contain the terms ‘space’ and ‘program’ in the

8 Version 1

overview field with no more than 5 intervening terms in between. Matches are not case

sensitive. As you did in h(i)(1), match full words, not word parts/sub-strings. e.g., Allowed: ‘In

Space there was a program’, ‘In this space program’. Disallowed: ‘In space you are not

subjected to the laws of gravity. A program.’, etc.

Output format:

6

Deliverables: Place all the files listed below in the Q2 folder

1. Q2_SQL.py: Modified file containing all the SQL statements you have used to answer parts a - h in

the proper sequence.

Q3 [15 points] D3 (v5) Warmup

Read chapters 4-8 of Scott Murray’s Interactive Data Visualization for the Web, 2nd edition . . This

simple reading provides important foundation you will need for Homework 2. This question uses D3

version v5, while the book covers D3 v4. What you learn from the book is transferable to v5.

NOTE the following important points:

1. We highly recommend that you use the latest Firefox browser to complete this question. We will grade

your work using Firefox 79.0 (or newer).

2. For this homework, the D3 library is provided to you in the lib folder. You must NOT use any D3 libraries

(d3*.js) other than the ones provided.

3. You may need to setup an HTTP server to run your D3 visualizations. The easiest way is to use

http.server for Python 3.x. Run your local HTTP server in the hw1-skeleton/Q3 folder.

4. We have provided sections of code along with comments in the skeleton to help you complete the

implementation. While you do not need to remove them, you may need to write additional code to make things

work.

5. All d3*.js files in the lib folder are referenced using relative paths in your html file. For example, since the

file “Q3/index.html” uses d3, its header contains:

<script type="text/javascript" src="lib/d3/d3.min.js"></script>

It is incorrect to use an absolute path such as:

<script type="text/javascript" src="http://d3js.org/d3.v5.min.js"></script>

The 3 files that are referenced are:

- lib/d3/d3.min.js

- lib/d3-dsv/d3-dsv.min.js

- lib/d3-fetch/d3-fetch.min.js

9 Version 1

6. For a question that reads in a dataset, you are required to submit the dataset too (as part of your

deliverable). In your html / js code, use a relative path to read in the dataset file. For example, since Q3

requires reading data from the q3.csv file, the path should be ‘q3.csv’ and NOT an absolute path such as

“C:/Users/polo/HW1-skeleton/Q3/q3.csv”. Absolute/local paths are specific locations that exist only on your

computer, which means your code will NOT run on our machines when we grade (and you will lose points).

7. You can and are encouraged (though not required) to decouple the style, functionality and markup in the

code for each question. That is, you can use separate files for CSS, JavaScript and HTML — this is a good

programming practice in general.

Deliverables: Place all the files/folders listed below in the Q3 folder

● A folder named lib containing folders d3, d3-fetch, d3-dsv

● q3.csv: the file that we have provided you, in the hw1 skeleton under Q3 folder, which contains the

data that will be loaded into the D3 plot.

● index.(html / css / js) : when run in a browser, it should display a barplot with the following

specifications:

a. [1.5 points] Load the data from q3.csv using D3 fetch methods. We recommend d3.dsv().

b. [2 points] The barplot must display one bar per row in the q3.csv dataset. Each bar

corresponds to the running total of movies for a given year. The height of each bar

represents the running total. The bars are ordered by ascending time with the earliest

observation at the far left. i.e., 1880, 1890, ..., 2000

c. [1 point] The bars must have the same fixed width, and there must be some space between

two bars, so that the bars do not overlap.

d. [3 points] The plot must have visible X and Y axes that scale according to the generated

bars. That is, the axes are driven by the data that they are representing. Likewise, the ticks

on these axes must adjust automatically based on the values within the datasets, i.e., they

must not be hard-coded.

e. [2 point] Set x-axis label to ‘Year’ and y-axis label to ‘Running Total’.

f. [1 point] Use a linear scale for the Y axis to represent the running total (recommended

function: d3.scaleLinear()).

g. [3 points] Use a time scale for the X axis to represent year (recommended function:

d3.scaleTime()). It may be necessary to use time parsing / formatting when you load and

display the year data. The axis would be overcrowded if you display every year value so set

the X-axis ticks to display one tick for every 10 years.

h. [1 point] Set the HTML title tag and display a title for the plot.

■ Position the title “Running Total of TMDb Movies by Year” above the barplot.

■ Set the HTML title tag (i.e., <title> Running Total of TMDb Movies by Year

</title>).

10 Version 1

i. [0.5 points] Add your username (usually includes a mix of letters and numbers,e.g. mhull32) to the

area beneath the bottom-right of the plot (see example image).

The barplot should appear similar in style to the sample data plot provided below.

Q4 [5 points] OpenRefine

OpenRefine is a Java application and requires Java JRE to run. Download and install Java if you do not

have it (you can verify by typing ‘java -version’ in your computer’s terminal or command prompt).

a. Watch the videos on OpenRefine’s homepage for an overview of its features. Then, download

(https://github.com/OpenRefine/OpenRefine/releases/tag/3.3)and install OpenRefine release 3.3.

Do not use version 3.4 (which is in beta status)

b. Import Dataset

● Run OpenRefine and point your browser at 127.0.0.1:3333.

● We use a products dataset from Mercari, derived from a Kaggle competition (Mercari Price

Suggestion Challenge). If you are interested in the details, visit the data description page.

We have sampled a subset of the dataset provided as "properties.csv".

● Choose "Create Project" → This Computer → properties.csv". Click "Next".

● You will now see a preview of the data. Click "Create Project" at the upper right corner.

c. Clean/Refine the data

NOTE: OpenRefine maintains a log of all changes. You can undo changes. Use the "Undo/Redo"

button at the upper left corner. Follow the exact output format specified in every part below.

i. [0.5 point] Select the category_name column and choose ‘Facet by Blank’ (Facet → Customized

Facets → Facet by blank) to filter out the records that have blank values in this column. Provide the

number of rows that return True in Q4Observations.txt. Exclude these rows.

11 Version 1

Output format and sample values:

i.rows: 500

ii. [1 point] Split the column category_name into multiple columns without removing the original

column. For example, a row with “Kids/Toys/Dolls & Accessories” in the category_name column

would be split across the newly created columns as “Kids”, “Toys” and “Dolls & Accessories”. Use

the existing functionality in OpenRefine that creates multiple columns from an existing column based

on a separator (i.e., in this case ‘/’) and does not remove the original category_name column.

Provide the number of new columns that are created by this operation, excluding the original

category_name column.

Output format and sample values:

ii.columns: 10

NOTE: There are many possible ways to split the data. While we have provided one way to

accomplish this in step ii, some methods could create columns that are completely empty. In this

dataset, none of the new columns should be completely empty. Therefore, to validate your output,

we recommend that you verify that there are no columns that are completely empty, by sorting and

checking for null values.

iii. [0.5 points] Select the column name and apply the Text Facet (Facet → Text Facet). Cluster by

using (Edit Cells → Cluster and Edit …) this opens a window where you can choose different

“methods” and “keying functions” to use while clustering. Choose the keying function that produces

the smallest number of clusters under the “Key Collision” method. Click ‘Select All’ and ‘Merge

Selected & Close’. Provide the name of the keying function and the number of clusters that was

produced.

Output format and sample values:

iii.function: fingerprint, 200

NOTE: Use the default Ngram size when testing Ngram-fingerprint.

iv. [1 point] Replace the null values in the brand_name column with the text “Unknown” (Edit Cells -

> Transform). Provide the General Refine Evaluation Language (GREL) expression used.

Output format and sample values:

iv.GREL_categoryname: endsWith("food", "ood")

v. [1 point] Create a new column high_priced with the values 0 or 1 based on the “price” column

with the following conditions: if the price is greater than 90, high_priced should be set as 1, else

0. Provide the GREL expression used to perform this.

Output format and sample values:

v.GREL_highpriced: endsWith("food", "ood")

vi. [1 point] Create a new column has_offer with the values 0 or 1 based on the

item_description column with the following conditions: If it contains the text “discount” or “offer”

or “sale”, then set the value in has_offer as 1, else 0. Provide the GREL expression used to

perform this. Convert the text to lowercase before you search for the terms.

12 End of HW1

Version 1

Output format and sample values:

vi.GREL_hasoffer: endsWith("food", "ood")

Deliverables: Place all the files listed below in the Q4 folder

● properties_clean.csv : Export the final table as a comma-separated values (.csv) file.

● changes.json : Submit a list of changes made to file in json format. Use the “Extract Operation

History” option under the Undo/Redo tab to create this file.

● Q4Observations.txt : A text file with answers to parts c.i, c.ii, c.iii, c.iv, c.v, c.vi. Provide each

answer in a new line in the exact output format specified. Your file’s final formatting should result in a

.txt file that has each answer on a new line followed by one blank line (to help visually separately the

answers)

Q5 [5 points] Introduction to Python Flask

Flask is a lightweight web application framework written in Python that provides you with tools, libraries and

technologies to quickly build a web application. It allows you to scale up your application as needed.

You will modify the given file:

? wrangling_scripts/wrangling.py

NOTE: You must only use a version of Python ≥ 3.7.0 and < 3.8 for this question. You must not use any

other versions (e.g., Python 3.8).

NOTE: You must only use the modules and libraries provided at the top of submission.py and modules

from the Python Standard Library (except Flask). Pandas and Numpy CANNOT be used

Username()- Update the username() method inside wrangling.py by including your Username, e.g.

mhull32.

? Get started by installing Flask on your machine by running pip install Flask (Note that you

can optionally create a virtual environment by following the steps here. Creating a virtual

environment is purely optional and can be skipped.)

? To run the code, you must navigate to the Q5 folder in your terminal/command prompt and execute

the following command: python run.py. After running the command go to http://127.0.0.1:3001/

on your browser. This will open up index.html showing a table in which the rows returned by

data_wrangling() are displayed.

? You must solve the following 2 sub-questions:

a. [2 points] Read the top 100 rows using the data_wrangling() method.

NOTE: The skeleton code by default reads all the rows from movies.csv. You must add the

required code to ensure reading only the first 100 rows. The skeleton code already handles

reading the table header for you.

b. [3 points]: Sort the table in descending order of the values i.e., with larger values at the top

and smaller values at the bottom of the table in the last (3rd) column.

Deliverables: Place the file listed below in the Q5 folder

● wrangling.py : Submit wrangling.py file with your changes.


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