diff --git a/dash_plot_generation/app.py b/dash_plot_generation/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..904843dcaee54ed02a2a942b496d12352909e1e0
--- /dev/null
+++ b/dash_plot_generation/app.py
@@ -0,0 +1,110 @@
+import dash
+import pandas
+from dash import html, dash, Output, Input
+from dash.exceptions import PreventUpdate
+
+from dash_plot_generation.data_store import initialize_data, OWNER_RANGE_PARTS_SORTED, FULL_DATA
+from dash_plot_generation.utils import get_average_user_rating_label, get_game_count_label, get_top_revenue_game_labels, \
+ get_total_revenue_label, get_top_genre_labels, get_ccu_label, get_average_game_rev_label
+from dash_plot_generation.styles_and_handles import RATING_MIN_REVIEWS, RATING_SLIDER, RATING_TABLE, \
+ RATING_DISTRIBUTION_PLOT, DEV_AVERAGE_RATING_LABEL, DENSITY_LAYOUT_STYLE, WHITE_STEAM, TAB_COLOR, TAB_EDGE, \
+ TAB_HEADER_COLOR, DEVELOPER_DROPDOWN, DEV_TOP_GENRES_LABEL, DEV_CCU_LABEL, DEV_GAME_COUNT_LABEL, \
+ DEV_REV_PER_GAME_LABEL, DEV_REVENUE_LABEL, DEV_TOP_GAMES
+from visual_presentation.Distribution_of_review_rating import get_rating_density_plot
+
+APP = dash.Dash(
+ name=__name__,
+ use_pages=True,
+ external_stylesheets=['/assets/styles.css',
+ 'https://codepen.io/chriddyp/pen/bWLwgP.css']
+)
+
+# APP.css.append_css({
+# 'external_url': 'styles.css'
+# })
+
+APP.layout = html.Div([
+ html.Nav(className="navbar", children=[
+ html.H6("SteamSavvy - Steam game data insights",
+ style={"margin-left": "60px", "display": "inline-block"},
+ className="nav-item-1"),
+ html.A('About', className="nav-item nav-link btn", href='/about',
+ style={"margin-left": "150px"},),
+ html.A('Dashboard', className="nav-item nav-link btn", href='/dashboard',
+ style={"margin-left": "150px"}),
+ html.A('Technical report', className="nav-item nav-link active btn", href='/Documentation/Report',
+ style={"margin-left": "150px"})
+ ]),
+
+ dash.page_container
+], className="body")
+
+
+@APP.callback([Output(DEV_REVENUE_LABEL, "children"),
+ Output(DEV_TOP_GENRES_LABEL, "children"),
+ Output(DEV_CCU_LABEL, "children"),
+ Output(DEV_GAME_COUNT_LABEL, "children"),
+ Output(DEV_REV_PER_GAME_LABEL, "children"),
+ Output(DEV_TOP_GAMES, "children"),
+ Output(DEV_AVERAGE_RATING_LABEL, "children")],
+ inputs=[Input(DEVELOPER_DROPDOWN, "value")])
+def update_dev_info(dev_name):
+ global FULL_DATA, OWNER_RANGE_PARTS_SORTED
+ if not (dev_name and isinstance(FULL_DATA, pandas.DataFrame)):
+ raise PreventUpdate
+
+ # Remove empty rows
+ mask = FULL_DATA.developer.apply(lambda x: dev_name in x if isinstance(x, str) else False)
+ dev_data = FULL_DATA[mask]
+
+ # Engineer revenue data into the dataframe
+ # add_game_revenues_and_owner_means(dev_data)
+
+ # Top games
+ dev_top_games_label = get_top_revenue_game_labels(dev_data)
+
+ # Dev total revenue
+ dev_revenue = get_total_revenue_label(dev_data)
+
+ # Dev revenue per game
+ dev_game_revenue_per_game = get_average_game_rev_label(dev_data)
+
+ # Top genres
+ dev_top_genre_labels = get_top_genre_labels(dev_data)
+
+ # CCU
+ dev_ccu = get_ccu_label(dev_data)
+
+ # Game count
+ dev_game_count = get_game_count_label(dev_data)
+
+ user_rating_value = get_average_user_rating_label(dev_data)
+ return dev_revenue, dev_top_genre_labels, dev_ccu, dev_game_count, dev_game_revenue_per_game, dev_top_games_label, \
+ user_rating_value
+
+
+@APP.callback(Output(RATING_DISTRIBUTION_PLOT, "figure"),
+ Output("Game pop bottom_region", 'children'),
+ Input(RATING_SLIDER, "value"),
+ Input(RATING_MIN_REVIEWS, "value"))
+def update_density_filter_plot(rating_range, min_reviews):
+ allowed_indexes = [str_val for (val, str_val) in OWNER_RANGE_PARTS_SORTED[rating_range[0]:rating_range[1] + 1]]
+ allowed_ratings = [" .. ".join([val, allowed_indexes[i + 1]]) for (i, val) in enumerate(allowed_indexes)
+ if i < len(allowed_indexes) - 1]
+
+ output = get_rating_density_plot(FULL_DATA, allowed_ratings, min_reviews, layout=DENSITY_LAYOUT_STYLE)
+ table = html.Div(dash.dash_table.DataTable(output['top_games']['non_free'].to_dict('records'),
+ id=RATING_TABLE,
+
+ style_data={'backgroundColor': TAB_COLOR,
+ 'color': WHITE_STEAM,
+ 'border': '1px solid ' + TAB_EDGE},
+ style_header={'backgroundColor': TAB_HEADER_COLOR,
+ 'color': WHITE_STEAM,
+ 'border': '1px solid ' + TAB_EDGE}))
+ return output['fig'], [table]
+
+
+if __name__ == "__main__":
+ initialize_data()
+ APP.run_server(debug=True, host="0.0.0.0")
diff --git a/dash_plot_generation/dash_assets/background_image.jpg b/dash_plot_generation/assets/background_image.jpg
similarity index 100%
rename from dash_plot_generation/dash_assets/background_image.jpg
rename to dash_plot_generation/assets/background_image.jpg
diff --git a/dash_plot_generation/dash_assets/broken car.jpg b/dash_plot_generation/assets/broken car.jpg
similarity index 100%
rename from dash_plot_generation/dash_assets/broken car.jpg
rename to dash_plot_generation/assets/broken car.jpg
diff --git a/dash_plot_generation/dash_assets/dark city.jpg b/dash_plot_generation/assets/dark city.jpg
similarity index 100%
rename from dash_plot_generation/dash_assets/dark city.jpg
rename to dash_plot_generation/assets/dark city.jpg
diff --git a/dash_plot_generation/dash_assets/dark_computer_room.jpg b/dash_plot_generation/assets/dark_computer_room.jpg
similarity index 100%
rename from dash_plot_generation/dash_assets/dark_computer_room.jpg
rename to dash_plot_generation/assets/dark_computer_room.jpg
diff --git a/dash_plot_generation/dash_assets/radioactive.jpg b/dash_plot_generation/assets/radioactive.jpg
similarity index 100%
rename from dash_plot_generation/dash_assets/radioactive.jpg
rename to dash_plot_generation/assets/radioactive.jpg
diff --git a/dash_plot_generation/dash_assets/styles.css b/dash_plot_generation/assets/styles.css
similarity index 76%
rename from dash_plot_generation/dash_assets/styles.css
rename to dash_plot_generation/assets/styles.css
index 17e962942cf781541e0e4000207fff0e77af27aa..6ace39347d42eb69643a5811e90b705f26421abf 100644
--- a/dash_plot_generation/dash_assets/styles.css
+++ b/dash_plot_generation/assets/styles.css
@@ -1,8 +1,30 @@
-html, body {
- height: 100%;
- margin: 0;
- background-color: rgb(27,40,56);
+body {
+ margin: 0;
+ padding: 0;
+ object-fit: fill;
+ height: 100%;
+ width: 100%;
+ background-color: rgb(27,40,56);
+ color: rgb(235,235,235);
+
+}
+
+.navbar {
+ background-color: rgb(23,29,37);
+ color: rgb(197,195,192);
+ width: 100%;
+ margin:0;
+}
+
+.nav-item-1 {
+ color: rgb(197,195,192);
+ font-size: 30px;
+}
+
+.nav-link {
+ color: rgb(197,195,192);
+ font-size: 20px;
}
#app-container {
height: 100%;
@@ -43,9 +65,6 @@ html, body {
border-bottom: 2px solid rgb(37,55,77);
}
-.dark_plot{
- background-color: black;
-}
/* Track rgb(197,195,192) */
div.scrollable::-webkit-scrollbar {
@@ -55,10 +74,7 @@ div.scrollable::-webkit-scrollbar {
}
div.scrollable::-webkit-scrollbar-track {
- margin-top: 20px;
- margin-bottom: 20px;
- margin-left: 20px;
- margin-right: 20px;
+ margin: 20px;
}
div.scrollable::-webkit-scrollbar-thumb {
diff --git a/dash_plot_generation/dash_plot_generator.py b/dash_plot_generation/dash_plot_generator.py
deleted file mode 100644
index 419fba31ba28c1d6f7d57bfe950057e4981840c1..0000000000000000000000000000000000000000
--- a/dash_plot_generation/dash_plot_generator.py
+++ /dev/null
@@ -1,619 +0,0 @@
-
-import os
-
-import numpy
-import pandas
-from dash import dash, dcc, html, Output, Input
-from collections import Counter
-
-from dash_plot_generation.utils import split_companies, extract_unique_companies, convert_owners_to_limits, \
- get_owner_means, round_to_three_largest_digits, replace_owner_number_with_symbol_real_numeric, \
- convert_to_numeric_str, label_with_rev, label_with_text
-from dash.exceptions import PreventUpdate
-import plotly.graph_objects as go
-import plotly.express as px
-
-from visual_presentation.Distribution_of_review_rating import get_rating_density_plot
-
-RATING_MIN_REVIEWS = "min_reviews_id"
-
-RATING_SLIDER = "rating_slider"
-
-RATING_TABLE = "rating_data_table"
-
-RATING_DISTRIBUTION_PLOT = "game_popularity_density_plot"
-
-MAIN_PANEL_TAB_DICT = {'height': '550px', 'width': '100%', 'margin': '0', 'overflow': 'auto'}
-
-SPACE_NORMAL_ENTRY = 35
-
-DEV_AVERAGE_RATING_LABEL = "dev_average_rating"
-
-RIGHT_SIDE_TEXT_DICT = {'display': 'inline-block',
- 'float': 'right', 'margin-right': '0%', 'margin-bottom': '0px'}
-
-DEFAULT_PLOT_STYLE_DICT = dict(
- template="plotly_dark",
- plot_bgcolor="rgb(31,46,65)",
- paper_bgcolor="rgb(31,46,65)",
-)
-
-DENSITY_LAYOUT_STYLE = DEFAULT_PLOT_STYLE_DICT | dict(
- title='Distribution of Game Review Rating',
- xaxis_title="Game user rating",
- yaxis_title="Proportion"
-
-)
-
-DARK_STEAM = "rgb(23,29,37)"
-WHITE_STEAM = "rgb(235,235,235)"
-TITLE_WHITE_STEAM = "rgb(197,195,192)"
-DARK_BLUE_STEAM = "rgb(27,40,56)"
-TAB_COLOR = "rgb(31,46,65)"
-TAB_EDGE = "rgb(37,55,77)"
-DROPDOWN_COLOR = "rgb(50,70,101"
-SMALL_PANEL_COLOR = "rgb(22,32,45)"
-
-layout = dict(template="plotly_dark", plot_bgcolor="rgb(31,46,65)", paper_bgcolor="rgb(31,46,65)")
-DEFAULT_TABS_DICT = {'width': 'auto', 'display': 'flex',
- 'background-color': TAB_COLOR, 'border-color': TAB_EDGE}
-TAB_HEADER_COLOR = "rgb(45,96,150)"
-DEVELOPER_DROPDOWN = "developer_dropdown"
-TAB_NORMAL_DICT = {'background-color': TAB_COLOR, 'color': TITLE_WHITE_STEAM,
- 'border': '0px solid',
- 'font-size': '15px',
- 'border_bottom': '2px solid ' + TAB_EDGE}
-TAB_HIGHLIGHT_DICT = {'backgroundColor': TAB_HEADER_COLOR, 'color': 'white', "border-color": "transparent",
- 'font-size': '15px'}
-PANEL_DEFAULT_DICT = {'display': 'inline-block',
- 'background-color': TAB_COLOR, 'border': '2px solid', 'border-color': TAB_EDGE,
- 'color': WHITE_STEAM, 'height': '600px'}
-SMALL_PANEL_DICT = {'float': 'left', 'background-color': 'transparent', 'box-sizing': 'border-box',
- 'padding': '10px'}
-SMALL_TAB_PANEL_DICT = SMALL_PANEL_DICT | {'width': '48%', 'height': '100%',
- 'margin-bottom': '50px',
- 'padding-top': '4%', 'padding-bottom': '5%', 'padding-left': '5%',
- 'padding-right': '5%',
- 'margin-top': '20px'
- }
-SMALL_PANEL_HEADER_DICT = {'text-align': 'center', 'padding-top': '5%', 'padding-bottom': '2%'}
-
-DEV_TOP_GENRES_LABEL = "dev_top_genres"
-
-LIST_DICT = {'display': 'inline-block', 'margin-bottom': '0px', 'padding-right': '0px'}
-
-NORMAL_DIVISION_DICT = SMALL_PANEL_DICT | {'width': '60%', 'height': '100%', 'margin-right': '5%', 'padding-left': '5%',
- 'margin-bottom': '5%', 'background-color': TAB_COLOR}
-
-DEV_CCU_LABEL = "dev_ccu"
-
-DEV_GAME_COUNT_LABEL = "dev_game_count"
-
-DEV_REV_PER_GAME_LABEL = "dev_rev_per_game"
-
-DEV_REVENUE_LABEL = "dev_revenue"
-
-DEV_TOP_GAMES = "pub_top_games"
-
-PUB_TOP_GENRES_LABEL = "pub_top_genres"
-
-PUB_CCU_LABEL = "pub_ccu"
-
-PUB_GAME_COUNT_LABEL = "pub_game_count"
-
-PUB_REV_PER_GAME_LABEL = "pub_rev_per_game"
-
-PUB_REVENUE_LABEL = "pub_revenue"
-
-PUB_TOP_GAMES = "pub_top_games"
-DEMO_PLOT_LABELS = ["Action", "Adventure", "RPG", "Puzzle", "Strategy", "Other"]
-DEMO_PLOT_COLORS = list(zip(DEMO_PLOT_LABELS, px.colors.qualitative.G10))
-csv_path = os.path.normpath(os.getcwd() + os.sep + os.pardir + os.sep + "api_exploration")
-split_csv_path = os.path.join(csv_path, "file_segments")
-# steam_dark_template = dict(layout=go.Layout(title_font))
-
-FULL_DATA = None
-OWNER_RANGE_PARTS_SORTED = None
-
-DASH_ASSETS_PATH = "dash_assets"
-
-APP = dash.Dash(
- name=__name__,
- assets_folder=DASH_ASSETS_PATH,
- external_stylesheets=['https://codepen.io/chriddyp/pen/bWLwgP.css'],
-)
-
-
-def initialize_data():
- global FULL_DATA, OWNER_RANGE_PARTS_SORTED
- dataframe = None
- for file in os.listdir(split_csv_path):
- file_path = os.path.join(split_csv_path, file)
- dataframe = pandas.concat([dataframe, pandas.read_csv(file_path)]) if dataframe is not None \
- else pandas.read_csv(file_path)
-
- add_game_revenues_and_owner_means(dataframe)
- owner_ranges = {value_range for value_range in dataframe["owners"].unique()}
-
- ranges_test = [(convert_owners_to_limits(value_range), value_range.split(" .. ")) for value_range in owner_ranges]
- unique_owner_values = {(limits[i], limits_str[i]) for (limits, limits_str)
- in ranges_test for i in range(2)}
- sorted_owner_list = sorted(unique_owner_values, key=lambda range: range[0])
-
- OWNER_RANGE_PARTS_SORTED = sorted_owner_list
- FULL_DATA = dataframe
-
-
-@APP.callback(Output(RATING_DISTRIBUTION_PLOT, "figure"),
- Output("Game pop bottom_region", 'children'),
- Input(RATING_SLIDER, "value"),
- Input(RATING_MIN_REVIEWS, "value"))
-def update_density_filter_plot(rating_range, min_reviews):
- global FULL_DATA, OWNER_RANGE_PARTS_SORTED
-
- allowed_indexes = [str_val for (val, str_val) in OWNER_RANGE_PARTS_SORTED[rating_range[0]:rating_range[1] + 1]]
- allowed_ratings = [" .. ".join([val, allowed_indexes[i + 1]]) for (i, val) in enumerate(allowed_indexes)
- if i < len(allowed_indexes) - 1]
-
- output = get_rating_density_plot(FULL_DATA, allowed_ratings, min_reviews, layout=DENSITY_LAYOUT_STYLE)
- table = html.Div(dash.dash_table.DataTable(output['top_games']['non_free'].to_dict('records'),
- id=RATING_TABLE,
-
- style_data={'backgroundColor': TAB_COLOR,
- 'color': WHITE_STEAM,
- 'border': '1px solid ' + TAB_EDGE},
- style_header={'backgroundColor': TAB_HEADER_COLOR,
- 'color': WHITE_STEAM,
- 'border': '1px solid ' + TAB_EDGE}))
- return output['fig'], [table]
-
-
-@APP.callback([Output(DEV_REVENUE_LABEL, "children"),
- Output(DEV_TOP_GENRES_LABEL, "children"),
- Output(DEV_CCU_LABEL, "children"),
- Output(DEV_GAME_COUNT_LABEL, "children"),
- Output(DEV_REV_PER_GAME_LABEL, "children"),
- Output(DEV_TOP_GAMES, "children"),
- Output(DEV_AVERAGE_RATING_LABEL, "children")],
- inputs=[Input(DEVELOPER_DROPDOWN, "value")])
-def update_dev_info(dev_name):
- global FULL_DATA, OWNER_RANGE_PARTS_SORTED
- if not (dev_name and isinstance(FULL_DATA, pandas.DataFrame)):
- raise PreventUpdate
-
- # Remove empty rows
- mask = FULL_DATA.developer.apply(lambda x: dev_name in x if isinstance(x, str) else False)
- dev_data = FULL_DATA[mask]
-
- # Engineer revenue data into the dataframe
- # add_game_revenues_and_owner_means(dev_data)
-
- # Top games
- dev_top_games_label = get_top_revenue_game_labels(dev_data)
-
- # Dev total revenue
- dev_revenue = get_total_revenue_label(dev_data)
-
- # Dev revenue per game
- dev_game_revenue_per_game = get_average_game_rev_label(dev_data)
-
- # Top genres
- dev_top_genre_labels = get_top_genre_labels(dev_data)
-
- # CCU
- dev_ccu = get_ccu_label(dev_data)
-
- # Game count
- dev_game_count = get_game_count_label(dev_data)
-
- user_rating_value = get_average_user_rating_label(dev_data)
- return dev_revenue, dev_top_genre_labels, dev_ccu, dev_game_count, dev_game_revenue_per_game, dev_top_games_label, \
- user_rating_value
-
-
-def get_average_user_rating_label(dev_data):
- value_str = str(round(100 * dev_data["Review_rating"].mean())) + "%"
- label = label_with_text("Average game rating", value_str, SPACE_NORMAL_ENTRY, ".")
- return label
-
-
-def get_game_count_label(dev_data):
- return label_with_text("Number of games", str(dev_data.shape[0]), SPACE_NORMAL_ENTRY, ".")
-
-
-def add_game_revenues_and_owner_means(data):
- data["owner_means"] = data["owners"].apply(lambda x: get_owner_means(convert_owners_to_limits(x)))
- data["game_revenue"] = data.apply(lambda x: x["owner_means"] * x["price"] if
- not (pandas.isna(x["owner_means"]) or pandas.isna(x["price"]))
- else 0, axis=1)
-
-
-def get_top_revenue_game_labels(data):
- top_games = data.sort_values(by=["game_revenue"], ascending=False).head(3)
- top_games_processed = top_games.apply(lambda x: label_with_rev(x["name"], x["game_revenue"], SPACE_NORMAL_ENTRY,
- ".", "$"), axis=1)
- dev_top_games_with_dot = [" ".join(["•", game]) for game in top_games_processed]
- dev_top_games_label = html.Div("\n".join(dev_top_games_with_dot),
- style={'white-space': 'pre-line', 'padding-left': '5%'})
- return dev_top_games_label
-
-
-def get_total_revenue_label(data):
- top_games_processed = label_with_rev("• Total", numpy.nansum(data["game_revenue"]), SPACE_NORMAL_ENTRY, ".", "$")
- return top_games_processed
-
-
-def get_top_genre_labels(data):
- genre_totals = [genre for genre_list in data["genres"] if isinstance(genre_list, str)
- for genre in genre_list.split(", ")]
- genre_counts = Counter(genre_totals).most_common(3)
- top_genres_rows = [label_with_text(genre[0], str(genre[1]), 50, ".") for genre in genre_counts]
- top_genres_with_dot = [" ".join(["•", row]) for row in top_genres_rows]
- top_genre_labels = html.Div("\n".join(top_genres_with_dot),
- style={'white-space': 'pre-line', 'padding-left': '5%'})
- return top_genre_labels
-
-
-def get_ccu_label(data):
- ccu = sum(data["ccu"])
- dev_ccu = convert_to_numeric_str(ccu)
-
- return label_with_text("Concurrent users", dev_ccu, SPACE_NORMAL_ENTRY, ".")
-
-
-def get_genre_popularity_counts(df, group_after_largest=8):
- genre_df = df[["genres", "owner_means", "game_revenue"]]
- genre_owners = {}
- genre_revenue = {}
-
- for index, row in genre_df.iterrows():
- if not isinstance(row.genres, str):
- continue
- genre_list = row.genres.split(", ")
- for genre in genre_list:
- if genre in genre_owners.keys():
- genre_owners[genre] += row["owner_means"]
- genre_revenue[genre] += row["game_revenue"]
- else:
- genre_owners[genre] = row["owner_means"]
- genre_revenue[genre] = row["game_revenue"]
- top_owners = dict(Counter(genre_owners).most_common(group_after_largest))
- top_revenue = dict(Counter(genre_revenue).most_common(group_after_largest))
- top_owners["Other"] = sum([val for (key, val) in genre_owners.items()
- if key not in top_owners.keys()])
- top_revenue["Other"] = sum([val for (key, val) in genre_revenue.items()
- if key not in top_revenue.keys()])
-
- return top_owners, top_revenue
-
-
-def get_average_game_rev_label(data):
- game_revenue_per_game_raw = numpy.nansum(data["game_revenue"]) / len(data["game_revenue"])
- dev_game_revenue_per_game_row = label_with_rev("Average", game_revenue_per_game_raw, SPACE_NORMAL_ENTRY, ".", "$")
- dev_game_revenue_per_game = " ".join(["•", dev_game_revenue_per_game_row])
- return dev_game_revenue_per_game
-
-
-def initialize_dash(host: str = "0.0.0.0", **kwargs):
- """
- Runs the Dash server.
- Args:
- host: IP address of the server
- kwargs: Variables which are passed down to APP.run function as named arguments.
- Note:
- The server IP is not actually 0.0.0.0; if the dash app is not accessible via this address, use the same port
- number but replace the IP address with your local network IP instead.
- """
-
- global APP, FULL_DATA, DEMO_PLOT_COLORS, DEMO_PLOT_LABELS, OWNER_RANGE_PARTS_SORTED
-
- # unique_publishers = extract_unique_companies(df["publisher"].apply(lambda x: split_companies(x)))
- # unique_developers = extract_unique_companies(df["developer"].iloc[0:10].apply(lambda x: split_companies(x)))
- unique_publishers = ["Valve"]
- unique_developers = ["Valve"]
-
- # Genre performance table_values
- # genre_owners, genre_revenue = get_genre_popularity_counts(FULL_DATA, 6)
- genre_owners = {key: val for (key, val) in
- zip(["Action", "Adventure", "RPG", "Puzzle", "Strategy", "Other"],
- [0.7, 0.5, 0.1, 0.4, 0.3, 0.7])}
- genre_revenue = {key: val for (key, val) in
- zip(["Action", "Adventure", "RPG", "Puzzle", "Strategy", "Other"],
- [0.5, 0.4, 0.3, 0.4, 0.6, 0.7])}
-
- # Game popularity filter values
- max_reviews = numpy.nanmax(FULL_DATA.apply(lambda x: x["positive"] + x["negative"], axis=1))
- owner_range_dict = {index: val_str for (index, (val, val_str)) in enumerate(OWNER_RANGE_PARTS_SORTED)}
- min_owner = min(owner_range_dict.keys())
- max_owner = max(owner_range_dict.keys())
-
- APP.css.append_css({
- 'external_url': 'styles.css'
- })
- APP.layout = html.Div(
- children=[
- html.Nav(className="nav nav-pills", children=[
- html.H6("SteamSavvy - Steam game data insights",
- style={"margin-left": "60px", "width": "20%", "display": "inline-block"}),
- html.A('About', className="nav-item nav-link btn", href='/Documentation/About',
- style={"margin-left": "300px"}),
- html.A('Technical report', className="nav-item nav-link active btn", href='/Documentation/Report',
- style={"margin-left": "150px"})
- ],
- style={"background-color": "rgb(23,29,37)", "color": "rgb(197,195,192)", 'width': '100%'}),
- html.Div(className="row", children=[
- html.Div(children=[
- dcc.Tabs(id="main_plots", value="tab1", children=[
- dcc.Tab(label="Genre performance", value="tab1",
- style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT,
- children=[
- html.Div(children=[
- html.Div(
- children=[
- html.Div(style=NORMAL_DIVISION_DICT,
- children=[dcc.Graph(
- figure=go.Figure(data=[
- {'x': ["Action", "Adventure", "RPG", "Puzzle",
- "Strategy"],
- 'y': [0.7, 0.4, 0.8, 1.2, 1.3],
- 'type': 'bar'},
- ],
- layout=DEFAULT_PLOT_STYLE_DICT |
- dict(title="Relative genre perfomance")
- ),
-
- ),
- html.P(f"""Genre performance measures the assessed
- exploitability of the specific game genre. The assessment
- is done by estimating the genre popularity, and games
- developed in the next two years and showing the relative
- differences between the genres.""")]),
- html.Div(style=SMALL_PANEL_DICT | {'width': '35%', 'height': '100%',
- 'background-color': TAB_COLOR},
- children=[
- html.Div(children=[
- html.Div(style={'width': '100%', 'height': '50%'},
- children=[dcc.Graph(
- figure=go.Figure(data=[go.Pie(
- labels=list(genre_owners.keys()),
- values=list(genre_owners.values()),
- sort=False)],
- layout=DEFAULT_PLOT_STYLE_DICT |
- dict(title="Genre popularity",
- margin=dict(l=20, r=20,
- t=50, b=20))),
- style={'width': '100%',
- 'height': '100%'})]),
- html.Div(style={'width': '100%', 'height': '50%'},
- children=[dcc.Graph(
- figure=go.Figure(data=[go.Pie(
- labels=list(genre_revenue.keys()),
- values=list(genre_revenue.values()),
- sort=False)],
- layout=DEFAULT_PLOT_STYLE_DICT |
- dict(
- title="Genre revenue share",
- margin=dict(l=20, r=20,
- t=50, b=20))),
- style={'width': '100%',
- 'height': '100%'})]
- )
-
- ], style={'height': '540px'}
- ),
- ]
- )
- ]
- ),
-
- html.Div(children=[
- html.H5("Genre prediction", style={'margin-bottom': '50px'}),
- html.Div(children=[
- html.Div(children=[
- html.P("Selected genre:", style={'margin-bottom': '10px'}),
- dcc.Dropdown(id="genre_dropdown", value="action",
- options=[{"label": html.Span([genre],
- style={
- 'color': WHITE_STEAM}),
- "value": genre} for genre in
- ["action"]],
- style={'color': WHITE_STEAM, 'display': 'inline-block',
- 'width': '50%'},
- className='dash-dropdown',
- ),
-
- ],
- style={'width': '100%', 'margin-bottom': '50px'})
- ]),
- dcc.Graph(figure=go.Figure(layout=DEFAULT_PLOT_STYLE_DICT |
- dict(title="Genre prediction plot",
- margin=dict(l=20, r=20,
- t=50, b=20)))),
- html.P("""This is an individual regression estimate for the genre that represents
- the estimated amount of games to be produced in the next two years""")
-
- ], style=NORMAL_DIVISION_DICT | {'width': '90%'})
-
- ],
- style=MAIN_PANEL_TAB_DICT,
- className="scrollable")
- ]),
- dcc.Tab(label="Game popularity", value="tab2",
- style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT,
- children=[
- html.Div(id="Game pop general layout",
- style=MAIN_PANEL_TAB_DICT,
- className="scrollable",
- children=[
- html.Div(id="Game pop top div",
- children=[
- html.Div(id="game popularity filters",
- style=NORMAL_DIVISION_DICT | {'width': '100%',
- 'height': '100%',
- 'margin_left': '0px',
- 'margin_right': '0px',
- 'background-color': TAB_COLOR,
- 'display': 'inline-block'},
-
- children=[
- html.P("Filters"),
- html.Small("Number of game owners"),
- dcc.RangeSlider(id=RATING_SLIDER,
- min=min_owner, max=max_owner,
- marks=owner_range_dict,
- step=None,
- value=[min_owner,
- max_owner]),
- html.Small("Minimum amount of reviews"),
- dcc.Input(id=RATING_MIN_REVIEWS,
- type="number", min=0,
- max=max_reviews, step=1, value=0)
- ]
- ),
- html.Div(
- children=[dcc.Graph(id=RATING_DISTRIBUTION_PLOT)],
- style=NORMAL_DIVISION_DICT | {'width': '100%',
- 'display': 'inline-block'}
- )
- ]
- ),
- html.Div(id="Game pop bottom_region",
- style=NORMAL_DIVISION_DICT | {'width': '90%',
- 'overflow': 'auto',
- 'height': '70%'},
- className="scrollable",
- children=[
- html.Div(dash.dash_table.DataTable(id=RATING_TABLE
- ))
- ])
- ]
- ),
- ]
- ),
- dcc.Tab(label="Market performance", value="tab4",
- style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT),
- dcc.Tab(label="Market prediction tool", value="tab5",
- style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT),
- ],
- style=DEFAULT_TABS_DICT),
-
- ], style=PANEL_DEFAULT_DICT | {'width': '900px',
- 'margin-right': '100px', 'padding-left': '50px',
- 'padding-right': '50px', 'padding-bottom': '50px',
- 'padding-top': '50px', 'margin-bottom': '50px'
- }),
- html.Div(children=[
- dcc.Tabs(id="company_information", value="tab3", children=[
- dcc.Tab(label="Developer infromation", value="tab3", children=[
- html.Div(children=[
- dcc.Dropdown(id=DEVELOPER_DROPDOWN, value="Valve",
- options=[{"label": html.Span([developer], style={'color': WHITE_STEAM}),
- "value": developer} for developer in unique_developers],
- style={'margin-top': '20px', 'color': WHITE_STEAM},
- className='dash-dropdown',
- ),
- html.Div(children=[
- html.Div(
- children=[
- html.Div(
- children=[html.P("Revenue", style=SMALL_PANEL_HEADER_DICT)],
- style={'margin-bottom': '10%',
- 'border-bottom': '2px solid ' + TAB_EDGE}),
- html.Div(children=[
- html.Div(children=[
- html.P("Game sale revenue estimates"),
- html.Div(children=[
- html.Div(children=[
- html.P(id=DEV_REVENUE_LABEL, children="$524 M",
- style=LIST_DICT | {'padding-left': '5%'})
- ]),
- html.Div(children=[
- html.P(id=DEV_REV_PER_GAME_LABEL, children="$925 M",
- style=LIST_DICT | {'padding-left': '5%'})
- ]),
- ],
- style={'margin-bottom': '20px'}),
- html.Div(children=[
- html.P("Top games by revenue:"),
- html.Small(id=DEV_TOP_GAMES, children="Half life 2"),
- ])
- ]),
- ], style={'padding-left': '5%', 'padding-right': '5%',
- 'padding-bottom': '5%'})
- ],
- style=SMALL_TAB_PANEL_DICT | {'margin-right': '20px', 'margin-left': '0px'}
- ),
- html.Div(children=[
- html.Div(
- children=[
- html.Div(
- children=[html.P("General information",
- style=SMALL_PANEL_HEADER_DICT)],
- style={'margin-bottom': '30px',
- 'border-bottom': '2px solid ' + TAB_EDGE}),
- html.Div(children=[
- html.Div(children=[
- html.P(id=DEV_GAME_COUNT_LABEL, children="5",
- style=LIST_DICT),
- ],
- style={'margin-bottom': '10px'}
- ),
- html.Div(children=[
- html.P(id=DEV_CCU_LABEL, children="",
- style=LIST_DICT),
- ],
- style={'margin-bottom': '10px'}
- ),
- html.Div(children=[
- html.P("Popular game genres:"),
- html.Small(id=DEV_TOP_GENRES_LABEL,
- children="FPS, Action, Puzzle"),
- ],
- style={'margin-bottom': '10px'}
- ),
- html.Div(children=[
- html.P(id=DEV_AVERAGE_RATING_LABEL,
- children="",
- style=LIST_DICT)
- ])
- ])
- ])
-
- ], style=SMALL_TAB_PANEL_DICT | {'width': '45%', 'height': '100%',
- 'margin-right': '0px', 'margin-left': '20px'}
- )
- ], style={'height': '100%'})
- ], style={'margin-left': '20px', 'margin-right': '0px'}
- )
- ], style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT),
- dcc.Tab(label="Publisher information", value="tab4", children=[
-
- dcc.Dropdown(id="publisher_dropdown", value="Valve",
- options=[{"label": publisher, "value": publisher} for publisher in
- unique_publishers],
- ),
- ], style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT)
-
- ],
- style=DEFAULT_TABS_DICT),
- ],
- style=PANEL_DEFAULT_DICT | {'width': '700px',
- 'padding-left': '50px',
- 'padding-right': '50px', 'padding-bottom': '50px',
- 'padding-top': '50px', 'margin-bottom': '50px'
- })
- ],
- style={'width': '100%', "padding-top": "30px", 'padding-left': "50px"}),
- ],
- style={"font-family": "Tahoma"}
- )
-
- APP.run(host=host, **kwargs)
- print("The server has closed!")
-
-
-if __name__ == "__main__":
- initialize_data()
- initialize_dash(debug=True)
-
-print(csv_path)
diff --git a/dash_plot_generation/data_store.py b/dash_plot_generation/data_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..50a084abbdf9b4ba5c39ada128ab778e9ebfc1ed
--- /dev/null
+++ b/dash_plot_generation/data_store.py
@@ -0,0 +1,38 @@
+import os
+import pandas
+
+from dash_plot_generation.utils import convert_owners_to_limits, get_owner_means
+
+csv_path = os.path.normpath(os.getcwd() + os.sep + os.pardir + os.sep + "api_exploration")
+split_csv_path = os.path.join(csv_path, "file_segments")
+
+
+def initialize_data():
+ global FULL_DATA, OWNER_RANGE_PARTS_SORTED
+ dataframe = None
+ for file in os.listdir(split_csv_path):
+ file_path = os.path.join(split_csv_path, file)
+ dataframe = pandas.concat([dataframe, pandas.read_csv(file_path)]) if dataframe is not None \
+ else pandas.read_csv(file_path)
+
+ add_game_revenues_and_owner_means(dataframe)
+ owner_ranges = {value_range for value_range in dataframe["owners"].unique()}
+
+ ranges_test = [(convert_owners_to_limits(value_range), value_range.split(" .. ")) for value_range in owner_ranges]
+ unique_owner_values = {(limits[i], limits_str[i]) for (limits, limits_str)
+ in ranges_test for i in range(2)}
+ sorted_owner_list = sorted(unique_owner_values, key=lambda range: range[0])
+
+ OWNER_RANGE_PARTS_SORTED = sorted_owner_list
+ FULL_DATA = dataframe
+ return dataframe, sorted_owner_list
+
+
+def add_game_revenues_and_owner_means(data):
+ data["owner_means"] = data["owners"].apply(lambda x: get_owner_means(convert_owners_to_limits(x)))
+ data["game_revenue"] = data.apply(lambda x: x["owner_means"] * x["price"] if
+ not (pandas.isna(x["owner_means"]) or pandas.isna(x["price"]))
+ else 0, axis=1)
+
+
+FULL_DATA, OWNER_RANGE_PARTS_SORTED = initialize_data()
diff --git a/dash_plot_generation/pages/__pycache__/about.cpython-311.pyc b/dash_plot_generation/pages/__pycache__/about.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..adec7b2db59a047318316c250003e7dea481c0c0
Binary files /dev/null and b/dash_plot_generation/pages/__pycache__/about.cpython-311.pyc differ
diff --git a/dash_plot_generation/pages/__pycache__/dash_plot_generator.cpython-311.pyc b/dash_plot_generation/pages/__pycache__/dash_plot_generator.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c5d8cf4cd3f1ceae68109aef27d31b3ad4697280
Binary files /dev/null and b/dash_plot_generation/pages/__pycache__/dash_plot_generator.cpython-311.pyc differ
diff --git a/dash_plot_generation/pages/__pycache__/home.cpython-311.pyc b/dash_plot_generation/pages/__pycache__/home.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bcd74e231a6abc0348aa9a610418920a59f5b153
Binary files /dev/null and b/dash_plot_generation/pages/__pycache__/home.cpython-311.pyc differ
diff --git a/dash_plot_generation/pages/about.py b/dash_plot_generation/pages/about.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed454f0a671c0d0191fbff8bc16799b05eacac98
--- /dev/null
+++ b/dash_plot_generation/pages/about.py
@@ -0,0 +1,21 @@
+import dash
+from dash import html
+
+dash.register_page(__name__, path='/')
+
+layout = html.Div([
+ html.H1('About page'),
+ html.H4('SteamSavvy has the goal of giving company performance insights about the steam market data.'
+ 'Special focus is given to find exploitable sections in the steam game market.'),
+ html.H4('SteamSavvy is a project made by Linsen Gao, Sergei Panarin and Max Väistö created for the Introduction'
+ ' to Data Science course in Helsinki University.'),
+
+], className="custom_body")
+
+
+dash.register_page(
+ __name__,
+ title="Dashboard",
+ description="Main dashboard",
+ path="/about",
+)
\ No newline at end of file
diff --git a/dash_plot_generation/pages/dash_plot_generator.py b/dash_plot_generation/pages/dash_plot_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..a88940f25fc5bee10236656a8457a26f7d99e9b3
--- /dev/null
+++ b/dash_plot_generation/pages/dash_plot_generator.py
@@ -0,0 +1,314 @@
+import numpy
+import dash
+from dash import html, dcc
+from plotly import graph_objects as go
+
+from dash_plot_generation.styles_and_handles import RATING_MIN_REVIEWS, RATING_SLIDER, RATING_TABLE, \
+ RATING_DISTRIBUTION_PLOT, MAIN_PANEL_TAB_DICT, DEV_AVERAGE_RATING_LABEL, \
+ DEFAULT_PLOT_STYLE_DICT, WHITE_STEAM, TAB_COLOR, TAB_EDGE, DEFAULT_TABS_DICT, DEVELOPER_DROPDOWN, TAB_NORMAL_DICT, \
+ TAB_HIGHLIGHT_DICT, PANEL_DEFAULT_DICT, SMALL_PANEL_DICT, SMALL_TAB_PANEL_DICT, SMALL_PANEL_HEADER_DICT, \
+ DEV_TOP_GENRES_LABEL, LIST_DICT, NORMAL_DIVISION_DICT, DEV_CCU_LABEL, DEV_GAME_COUNT_LABEL, DEV_REV_PER_GAME_LABEL, \
+ DEV_REVENUE_LABEL, DEV_TOP_GAMES, DARK_BLUE_STEAM
+
+from dash_plot_generation.data_store import FULL_DATA, OWNER_RANGE_PARTS_SORTED
+
+global APP
+
+# unique_publishers = extract_unique_companies(df["publisher"].apply(lambda x: split_companies(x)))
+# unique_developers = extract_unique_companies(df["developer"].iloc[0:10].apply(lambda x: split_companies(x)))
+unique_publishers = ["Valve"]
+unique_developers = ["Valve"]
+
+# Genre performance table_values
+# genre_owners, genre_revenue = get_genre_popularity_counts(FULL_DATA, 6)
+genre_owners = {key: val for (key, val) in
+ zip(["Action", "Adventure", "RPG", "Puzzle", "Strategy", "Other"],
+ [0.7, 0.5, 0.1, 0.4, 0.3, 0.7])}
+genre_revenue = {key: val for (key, val) in
+ zip(["Action", "Adventure", "RPG", "Puzzle", "Strategy", "Other"],
+ [0.5, 0.4, 0.3, 0.4, 0.6, 0.7])}
+
+# Game popularity filter values
+max_reviews = numpy.nanmax(FULL_DATA.apply(lambda x: x["positive"] + x["negative"], axis=1))
+owner_range_dict = {index: val_str for (index, (val, val_str)) in enumerate(OWNER_RANGE_PARTS_SORTED)}
+min_owner = min(owner_range_dict.keys())
+max_owner = max(owner_range_dict.keys())
+
+
+layout = html.Div(
+ children=[
+ html.Div(className="row", children=[
+ html.Div(children=[
+ dcc.Tabs(id="main_plots", value="tab1", children=[
+ dcc.Tab(label="Genre performance", value="tab1",
+ style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT,
+ children=[
+ html.Div(children=[
+ html.Div(
+ children=[
+ html.Div(style=NORMAL_DIVISION_DICT,
+ children=[dcc.Graph(
+ figure=go.Figure(data=[
+ {'x': ["Action", "Adventure", "RPG", "Puzzle",
+ "Strategy"],
+ 'y': [0.7, 0.4, 0.8, 1.2, 1.3],
+ 'type': 'bar'},
+ ],
+ layout=DEFAULT_PLOT_STYLE_DICT |
+ dict(title="Relative genre perfomance")
+ ),
+
+ ),
+ html.P(f"""Genre performance measures the assessed
+ exploitability of the specific game genre. The assessment
+ is done by estimating the genre popularity, and games
+ developed in the next two years and showing the relative
+ differences between the genres.""")]),
+ html.Div(style=SMALL_PANEL_DICT | {'width': '35%', 'height': '100%',
+ 'background-color': TAB_COLOR},
+ children=[
+ html.Div(children=[
+ html.Div(style={'width': '100%', 'height': '50%'},
+ children=[dcc.Graph(
+ figure=go.Figure(data=[go.Pie(
+ labels=list(genre_owners.keys()),
+ values=list(genre_owners.values()),
+ sort=False)],
+ layout=DEFAULT_PLOT_STYLE_DICT |
+ dict(title="Genre popularity",
+ margin=dict(l=20, r=20,
+ t=50, b=20))),
+ style={'width': '100%',
+ 'height': '100%'})]),
+ html.Div(style={'width': '100%', 'height': '50%'},
+ children=[dcc.Graph(
+ figure=go.Figure(data=[go.Pie(
+ labels=list(genre_revenue.keys()),
+ values=list(genre_revenue.values()),
+ sort=False)],
+ layout=DEFAULT_PLOT_STYLE_DICT |
+ dict(
+ title="Genre revenue share",
+ margin=dict(l=20, r=20,
+ t=50, b=20))),
+ style={'width': '100%',
+ 'height': '100%'})]
+ )
+
+ ], style={'height': '540px'}
+ ),
+ ]
+ )
+ ]
+ ),
+
+ html.Div(children=[
+ html.H5("Genre prediction", style={'margin-bottom': '50px'}),
+ html.Div(children=[
+ html.Div(children=[
+ html.P("Selected genre:", style={'margin-bottom': '10px'}),
+ dcc.Dropdown(id="genre_dropdown", value="action",
+ options=[{"label": html.Span([genre],
+ style={
+ 'color': WHITE_STEAM}),
+ "value": genre} for genre in
+ ["action"]],
+ style={'color': WHITE_STEAM, 'display': 'inline-block',
+ 'width': '50%'},
+ className='dash-dropdown',
+ ),
+
+ ],
+ style={'width': '100%', 'margin-bottom': '50px'})
+ ]),
+ dcc.Graph(figure=go.Figure(layout=DEFAULT_PLOT_STYLE_DICT |
+ dict(title="Genre prediction plot",
+ margin=dict(l=20, r=20,
+ t=50, b=20)))),
+ html.P("""This is an individual regression estimate for the genre that represents
+ the estimated amount of games to be produced in the next two years""")
+
+ ], style=NORMAL_DIVISION_DICT | {'width': '90%'})
+
+ ],
+ style=MAIN_PANEL_TAB_DICT,
+ className="scrollable")
+ ]),
+ dcc.Tab(label="Game popularity", value="tab2",
+ style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT,
+ children=[
+ html.Div(id="Game pop general layout",
+ style=MAIN_PANEL_TAB_DICT,
+ className="scrollable",
+ children=[
+ html.Div(id="Game pop top div",
+ children=[
+ html.Div(id="game popularity filters",
+ style=NORMAL_DIVISION_DICT | {'width': '100%',
+ 'height': '100%',
+ 'margin_left': '0px',
+ 'margin_right': '0px',
+ 'background-color': TAB_COLOR,
+ 'display': 'inline-block'},
+
+ children=[
+ html.P("Filters"),
+ html.Small("Number of game owners"),
+ dcc.RangeSlider(id=RATING_SLIDER,
+ min=min_owner, max=max_owner,
+ marks=owner_range_dict,
+ step=None,
+ value=[min_owner,
+ max_owner]),
+ html.Small("Minimum amount of reviews"),
+ dcc.Input(id=RATING_MIN_REVIEWS,
+ type="number", min=0,
+ max=max_reviews, step=1, value=0)
+ ]
+ ),
+ html.Div(
+ children=[dcc.Graph(id=RATING_DISTRIBUTION_PLOT)],
+ style=NORMAL_DIVISION_DICT | {'width': '100%',
+ 'display': 'inline-block'}
+ )
+ ]
+ ),
+ html.Div(id="Game pop bottom_region",
+ style=NORMAL_DIVISION_DICT | {'width': '90%',
+ 'overflow': 'auto',
+ 'height': '70%'},
+ className="scrollable",
+ children=[
+ html.Div(dash.dash_table.DataTable(id=RATING_TABLE
+ ))
+ ])
+ ]
+ ),
+ ]
+ ),
+ dcc.Tab(label="Market performance", value="tab4",
+ style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT),
+ dcc.Tab(label="Market prediction tool", value="tab5",
+ style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT),
+ ],
+ style=DEFAULT_TABS_DICT),
+
+ ], style=PANEL_DEFAULT_DICT | {'width': '900px',
+ 'margin-right': '100px', 'padding-left': '50px',
+ 'padding-right': '50px', 'padding-bottom': '50px',
+ 'padding-top': '50px', 'margin-bottom': '50px'
+ }),
+ html.Div(children=[
+ dcc.Tabs(id="company_information", value="tab3", children=[
+ dcc.Tab(label="Developer infromation", value="tab3", children=[
+ html.Div(children=[
+ dcc.Dropdown(id=DEVELOPER_DROPDOWN, value="Valve",
+ options=[{"label": html.Span([developer], style={'color': WHITE_STEAM}),
+ "value": developer} for developer in unique_developers],
+ style={'margin-top': '20px', 'color': WHITE_STEAM},
+ className='dash-dropdown',
+ ),
+ html.Div(children=[
+ html.Div(
+ children=[
+ html.Div(
+ children=[html.P("Revenue", style=SMALL_PANEL_HEADER_DICT)],
+ style={'margin-bottom': '10%',
+ 'border-bottom': '2px solid ' + TAB_EDGE}),
+ html.Div(children=[
+ html.Div(children=[
+ html.P("Game sale revenue estimates"),
+ html.Div(children=[
+ html.Div(children=[
+ html.P(id=DEV_REVENUE_LABEL, children="$524 M",
+ style=LIST_DICT | {'padding-left': '5%'})
+ ]),
+ html.Div(children=[
+ html.P(id=DEV_REV_PER_GAME_LABEL, children="$925 M",
+ style=LIST_DICT | {'padding-left': '5%'})
+ ]),
+ ],
+ style={'margin-bottom': '20px'}),
+ html.Div(children=[
+ html.P("Top games by revenue:"),
+ html.Small(id=DEV_TOP_GAMES, children="Half life 2"),
+ ])
+ ]),
+ ], style={'padding-left': '5%', 'padding-right': '5%',
+ 'padding-bottom': '5%'})
+ ],
+ style=SMALL_TAB_PANEL_DICT | {'margin-right': '20px', 'margin-left': '0px'}
+ ),
+ html.Div(children=[
+ html.Div(
+ children=[
+ html.Div(
+ children=[html.P("General information",
+ style=SMALL_PANEL_HEADER_DICT)],
+ style={'margin-bottom': '30px',
+ 'border-bottom': '2px solid ' + TAB_EDGE}),
+ html.Div(children=[
+ html.Div(children=[
+ html.P(id=DEV_GAME_COUNT_LABEL, children="5",
+ style=LIST_DICT),
+ ],
+ style={'margin-bottom': '10px'}
+ ),
+ html.Div(children=[
+ html.P(id=DEV_CCU_LABEL, children="",
+ style=LIST_DICT),
+ ],
+ style={'margin-bottom': '10px'}
+ ),
+ html.Div(children=[
+ html.P("Popular game genres:"),
+ html.Small(id=DEV_TOP_GENRES_LABEL,
+ children="FPS, Action, Puzzle"),
+ ],
+ style={'margin-bottom': '10px'}
+ ),
+ html.Div(children=[
+ html.P(id=DEV_AVERAGE_RATING_LABEL,
+ children="",
+ style=LIST_DICT)
+ ])
+ ])
+ ])
+
+ ], style=SMALL_TAB_PANEL_DICT | {'width': '45%', 'height': '100%',
+ 'margin-right': '0px', 'margin-left': '20px'}
+ )
+ ], style={'height': '100%'})
+ ], style={'margin-left': '20px', 'margin-right': '0px'}
+ )
+ ], style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT),
+ dcc.Tab(label="Publisher information", value="tab4", children=[
+
+ dcc.Dropdown(id="publisher_dropdown", value="Valve",
+ options=[{"label": publisher, "value": publisher} for publisher in
+ unique_publishers],
+ ),
+ ], style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT)
+
+ ],
+ style=DEFAULT_TABS_DICT),
+ ],
+ style=PANEL_DEFAULT_DICT | {'width': '700px',
+ 'padding-left': '50px',
+ 'padding-right': '50px', 'padding-bottom': '50px',
+ 'padding-top': '50px', 'margin-bottom': '50px'
+ })
+ ],
+ style={'width': '100%', "padding-top": "30px", 'padding-left': "50px"}),
+ ],
+ style={"font-family": "Tahoma"},
+ className="body"
+)
+
+dash.register_page(
+ __name__,
+ path_template="/stocks/<ticker>",
+ title="Dashboard",
+ description="Main dashboard",
+ path="/dashboard",
+)
\ No newline at end of file
diff --git a/dash_plot_generation/pages/home.py b/dash_plot_generation/pages/home.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf74e8b0b078ed19a3c57021beca34db4947a59f
--- /dev/null
+++ b/dash_plot_generation/pages/home.py
@@ -0,0 +1,11 @@
+import dash
+from dash import html
+
+dash.register_page(__name__, path='/')
+
+layout = html.Div([
+ html.H1('SteamSavvy'),
+
+ html.H4('Discover the full potential of steam data for market analysis.'),
+
+], className="custom_body")
\ No newline at end of file
diff --git a/dash_plot_generation/pages/technical_report.py b/dash_plot_generation/pages/technical_report.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/dash_plot_generation/styles_and_handles.py b/dash_plot_generation/styles_and_handles.py
new file mode 100644
index 0000000000000000000000000000000000000000..354a4ae8db64a54f64e635e71a8375ba232b338c
--- /dev/null
+++ b/dash_plot_generation/styles_and_handles.py
@@ -0,0 +1,72 @@
+
+# html handles
+RATING_MIN_REVIEWS = "min_reviews_id"
+RATING_SLIDER = "rating_slider"
+RATING_TABLE = "rating_data_table"
+DEV_CCU_LABEL = "dev_ccu"
+DEV_GAME_COUNT_LABEL = "dev_game_count"
+DEV_REV_PER_GAME_LABEL = "dev_rev_per_game"
+DEV_REVENUE_LABEL = "dev_revenue"
+DEV_TOP_GAMES = "pub_top_games"
+PUB_TOP_GENRES_LABEL = "pub_top_genres"
+PUB_CCU_LABEL = "pub_ccu"
+PUB_GAME_COUNT_LABEL = "pub_game_count"
+PUB_REV_PER_GAME_LABEL = "pub_rev_per_game"
+PUB_REVENUE_LABEL = "pub_revenue"
+PUB_TOP_GAMES = "pub_top_games"
+DEV_TOP_GENRES_LABEL = "dev_top_genres"
+DEVELOPER_DROPDOWN = "developer_dropdown"
+RATING_DISTRIBUTION_PLOT = "game_popularity_density_plot"
+DEV_AVERAGE_RATING_LABEL = "dev_average_rating"
+
+
+MAIN_PANEL_TAB_DICT = {'height': '550px', 'width': '100%', 'margin': '0', 'overflow': 'auto'}
+SPACE_NORMAL_ENTRY = 35
+RIGHT_SIDE_TEXT_DICT = {'display': 'inline-block',
+ 'float': 'right', 'margin-right': '0%', 'margin-bottom': '0px'}
+
+DEFAULT_PLOT_STYLE_DICT = dict(
+ template="plotly_dark",
+ plot_bgcolor="rgb(31,46,65)",
+ paper_bgcolor="rgb(31,46,65)",
+)
+
+DENSITY_LAYOUT_STYLE = DEFAULT_PLOT_STYLE_DICT | dict(
+ title='Distribution of Game Review Rating',
+ xaxis_title="Game user rating",
+ yaxis_title="Proportion"
+
+)
+DARK_STEAM = "rgb(23,29,37)"
+WHITE_STEAM = "rgb(235,235,235)"
+TITLE_WHITE_STEAM = "rgb(197,195,192)"
+DARK_BLUE_STEAM = "rgb(27,40,56)"
+TAB_COLOR = "rgb(31,46,65)"
+TAB_EDGE = "rgb(37,55,77)"
+DROPDOWN_COLOR = "rgb(50,70,101"
+SMALL_PANEL_COLOR = "rgb(22,32,45)"
+TAB_HEADER_COLOR = "rgb(45,96,150)"
+
+DEFAULT_TABS_DICT = {'width': 'auto', 'display': 'flex',
+ 'background-color': TAB_COLOR, 'border-color': TAB_EDGE}
+TAB_NORMAL_DICT = {'background-color': TAB_COLOR, 'color': TITLE_WHITE_STEAM,
+ 'border': '0px solid',
+ 'font-size': '15px',
+ 'border_bottom': '2px solid ' + TAB_EDGE}
+TAB_HIGHLIGHT_DICT = {'backgroundColor': TAB_HEADER_COLOR, 'color': 'white', "border-color": "transparent",
+ 'font-size': '15px'}
+PANEL_DEFAULT_DICT = {'display': 'inline-block',
+ 'background-color': TAB_COLOR, 'border': '2px solid', 'border-color': TAB_EDGE,
+ 'color': WHITE_STEAM, 'height': '600px'}
+SMALL_PANEL_DICT = {'float': 'left', 'background-color': 'transparent', 'box-sizing': 'border-box',
+ 'padding': '10px'}
+SMALL_TAB_PANEL_DICT = SMALL_PANEL_DICT | {'width': '48%', 'height': '100%',
+ 'margin-bottom': '50px',
+ 'padding-top': '4%', 'padding-bottom': '5%', 'padding-left': '5%',
+ 'padding-right': '5%',
+ 'margin-top': '20px'
+ }
+SMALL_PANEL_HEADER_DICT = {'text-align': 'center', 'padding-top': '5%', 'padding-bottom': '2%'}
+LIST_DICT = {'display': 'inline-block', 'margin-bottom': '0px', 'padding-right': '0px'}
+NORMAL_DIVISION_DICT = SMALL_PANEL_DICT | {'width': '60%', 'height': '100%', 'margin-right': '5%', 'padding-left': '5%',
+ 'margin-bottom': '5%', 'background-color': TAB_COLOR}
diff --git a/dash_plot_generation/utils.py b/dash_plot_generation/utils.py
index 6d380af871fae79d7b108a061f93ee172bcd7264..4fd831a70532f1f64173e153d6007d3b93f78acc 100644
--- a/dash_plot_generation/utils.py
+++ b/dash_plot_generation/utils.py
@@ -1,7 +1,13 @@
import math
import re
+from collections import Counter
from typing import Sequence, Optional, Any
+
+import numpy
import pandas
+from dash import html
+
+from dash_plot_generation.styles_and_handles import SPACE_NORMAL_ENTRY
DEFAULT_ILLEGAL_CONTINUATIONS = {"INC.", "LLC", "CO.", "LTD.", "S.R.O."}
@@ -120,3 +126,79 @@ def round_to_three_largest_digits(number, accuracy=2):
round_val = -(len(str(round(number))) - accuracy)
return_val = round(round(number), min(round_val, 0))
return return_val
+
+
+def get_average_user_rating_label(dev_data):
+ value_str = str(round(100 * dev_data["Review_rating"].mean())) + "%"
+ label = label_with_text("Average game rating", value_str, SPACE_NORMAL_ENTRY, ".")
+ return label
+
+
+def get_game_count_label(dev_data):
+ return label_with_text("Number of games", str(dev_data.shape[0]), SPACE_NORMAL_ENTRY, ".")
+
+
+def get_top_revenue_game_labels(data):
+ top_games = data.sort_values(by=["game_revenue"], ascending=False).head(3)
+ top_games_processed = top_games.apply(lambda x: label_with_rev(x["name"], x["game_revenue"], SPACE_NORMAL_ENTRY,
+ ".", "$"), axis=1)
+ dev_top_games_with_dot = [" ".join(["•", game]) for game in top_games_processed]
+ dev_top_games_label = html.Div("\n".join(dev_top_games_with_dot),
+ style={'white-space': 'pre-line', 'padding-left': '5%'})
+ return dev_top_games_label
+
+
+def get_total_revenue_label(data):
+ top_games_processed = label_with_rev("• Total", numpy.nansum(data["game_revenue"]), SPACE_NORMAL_ENTRY, ".", "$")
+ return top_games_processed
+
+
+def get_top_genre_labels(data):
+ genre_totals = [genre for genre_list in data["genres"] if isinstance(genre_list, str)
+ for genre in genre_list.split(", ")]
+ genre_counts = Counter(genre_totals).most_common(3)
+ top_genres_rows = [label_with_text(genre[0], str(genre[1]), 50, ".") for genre in genre_counts]
+ top_genres_with_dot = [" ".join(["•", row]) for row in top_genres_rows]
+ top_genre_labels = html.Div("\n".join(top_genres_with_dot),
+ style={'white-space': 'pre-line', 'padding-left': '5%'})
+ return top_genre_labels
+
+
+def get_ccu_label(data):
+ ccu = sum(data["ccu"])
+ dev_ccu = convert_to_numeric_str(ccu)
+
+ return label_with_text("Concurrent users", dev_ccu, SPACE_NORMAL_ENTRY, ".")
+
+
+def get_genre_popularity_counts(df, group_after_largest=8):
+ genre_df = df[["genres", "owner_means", "game_revenue"]]
+ genre_owners = {}
+ genre_revenue = {}
+
+ for index, row in genre_df.iterrows():
+ if not isinstance(row.genres, str):
+ continue
+ genre_list = row.genres.split(", ")
+ for genre in genre_list:
+ if genre in genre_owners.keys():
+ genre_owners[genre] += row["owner_means"]
+ genre_revenue[genre] += row["game_revenue"]
+ else:
+ genre_owners[genre] = row["owner_means"]
+ genre_revenue[genre] = row["game_revenue"]
+ top_owners = dict(Counter(genre_owners).most_common(group_after_largest))
+ top_revenue = dict(Counter(genre_revenue).most_common(group_after_largest))
+ top_owners["Other"] = sum([val for (key, val) in genre_owners.items()
+ if key not in top_owners.keys()])
+ top_revenue["Other"] = sum([val for (key, val) in genre_revenue.items()
+ if key not in top_revenue.keys()])
+
+ return top_owners, top_revenue
+
+
+def get_average_game_rev_label(data):
+ game_revenue_per_game_raw = numpy.nansum(data["game_revenue"]) / len(data["game_revenue"])
+ dev_game_revenue_per_game_row = label_with_rev("Average", game_revenue_per_game_raw, SPACE_NORMAL_ENTRY, ".", "$")
+ dev_game_revenue_per_game = " ".join(["•", dev_game_revenue_per_game_row])
+ return dev_game_revenue_per_game