diff --git a/dash_plot_generation/dash_assets/styles.css b/dash_plot_generation/dash_assets/styles.css
index c9d0dc4b9ef5aae95f245ae3ed9794869a2dbccb..e2f7895cc48d409988f7a34353daebc672b0105d 100644
--- a/dash_plot_generation/dash_assets/styles.css
+++ b/dash_plot_generation/dash_assets/styles.css
@@ -46,3 +46,32 @@ html, body {
.dark_plot{
background-color: black;
}
+
+/* Track rgb(197,195,192) */
+div.scrollable::-webkit-scrollbar {
+ width: 6px; /* width of the vertical scrollbar */
+ border-radius: 6px;
+}
+
+div.scrollable::-webkit-scrollbar-track {
+ margin-top: 20px;
+ margin-bottom: 20px;
+}
+
+
+div.scrollable::-webkit-scrollbar-thumb {
+ background-color: rgb(235, 235, 235);
+ border-right: none;
+ border-left: none;
+ border-radius: 6px;
+}
+
+div.scrollable::-webkit-scrollbar-track-piece:end {
+ background: rgb(37,55,77);
+ margin-bottom: 10px;
+}
+
+div.scrollable::-webkit-scrollbar-track-piece:start {
+ background: rgb(37,55,77);
+ margin-top: 10px;
+}
\ No newline at end of file
diff --git a/dash_plot_generation/dash_plot_generator.py b/dash_plot_generation/dash_plot_generator.py
index 6be3390e21e5e14a95185da8de039cf32550f5a6..e88bddb9bec75c6688c0df97c755bd4e292895a6 100644
--- a/dash_plot_generation/dash_plot_generator.py
+++ b/dash_plot_generation/dash_plot_generator.py
@@ -6,7 +6,8 @@ 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
+ 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
@@ -14,7 +15,7 @@ import plotly.express as px
DEV_AVERAGE_RATING_LABEL = "dev_average_rating"
RIGHT_SIDE_TEXT_DICT = {'display': 'inline-block',
- 'float': 'right', 'margin-right': '0%'}
+ 'float': 'right', 'margin-right': '0%', 'margin-bottom': '0px'}
DARK_STEAM = "rgb(23,29,37)"
WHITE_STEAM = "rgb(235,235,235)"
@@ -37,8 +38,8 @@ TAB_HIGHLIGHT_DICT = {'backgroundColor': TAB_HEADER_COLOR, 'color': 'white', "bo
'font-size': '15px'}
PANEL_DEFAULT_DICT = {'display': 'inline-block',
'background-color': TAB_COLOR, 'border': '2px solid', 'border-color': TAB_EDGE,
- 'color': WHITE_STEAM}
-SMALL_PANEL_DICT = {'float': 'left', 'background-color': SMALL_PANEL_COLOR, 'box-sizing': 'border-box',
+ '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',
@@ -50,7 +51,7 @@ SMALL_PANEL_HEADER_DICT = {'text-align': 'center', 'padding-top': '5%', 'padding
DEV_TOP_GENRES_LABEL = "dev_top_genres"
-LIST_DICT = {'display': 'inline-block', 'margin-bottom': '0px'}
+LIST_DICT = {'display': 'inline-block', 'margin-bottom': '0px', 'padding-right': '0px'}
DEV_CCU_LABEL = "dev_ccu"
@@ -91,7 +92,6 @@ APP = dash.Dash(
def initialize_data():
global df
- files = os.listdir(split_csv_path)
dataframe = None
for file in os.listdir(split_csv_path):
file_path = os.path.join(split_csv_path, file)
@@ -110,36 +110,93 @@ def initialize_data():
inputs=[Input(DEVELOPER_DROPDOWN, "value")])
def update_dev_info(dev_name):
global df
- if not dev_name:
+ if not (dev_name and isinstance(df,pandas.DataFrame)):
raise PreventUpdate
+
+ # Remove empty rows
mask = df.developer.apply(lambda x: dev_name in x if isinstance(x, str) else False)
dev_data = df[mask]
- ccu = sum(dev_data["ccu"])
- dev_data["owner_means"] = dev_data["owners"].apply(lambda x: get_owner_means(convert_owners_to_limits(x)))
- dev_data["game_prices"] = dev_data["price"]
- dev_data["game_revenue"] = dev_data.apply(lambda x: x["owner_means"] * x["game_prices"] if
- not (pandas.isna(x["owner_means"]) or pandas.isna(x["game_prices"]))
+
+ # 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 = "$" + get_total_revenue(dev_data)
+
+ # Dev revenue per game
+ dev_game_revenue_per_game = get_average_game_rev_label(dev_data)
+
+ # Top genres
+ dev_top_genre_labels = get_top_genre_labels(dev_data)
+
+ # CCU
+ dev_ccu = get_ccu_label(dev_data)
+
+ # Game count
+ dev_game_count = get_game_count_label(dev_data)
+
+ user_rating_value = get_average_user_rating_label(dev_data)
+ return dev_revenue, dev_top_genre_labels, dev_ccu, dev_game_count, dev_game_revenue_per_game, dev_top_games_label, \
+ user_rating_value
+
+
+def get_average_user_rating_label(dev_data):
+ value_str = str(round(100 * dev_data["Review_rating"].mean())) + "%"
+ label = label_with_text("Average game rating", value_str, 40, ".")
+ return label
+
+
+def get_game_count_label(dev_data):
+ return label_with_text("Number of games", str(dev_data.shape[0]), 40, ".")
+
+
+def add_game_revenues_and_owner_means(data):
+ data["owner_means"] = data["owners"].apply(lambda x: get_owner_means(convert_owners_to_limits(x)))
+ data["game_revenue"] = data.apply(lambda x: x["owner_means"] * x["price"] if
+ not (pandas.isna(x["owner_means"]) or pandas.isna(x["price"]))
else 0, axis=1)
- # dev_data["game_revenue"] = pandas.Series([owner_count * game_price for (owner_count, game_price) in
- # zip(dev_data["owner_means"], dev_data["game_prices"]) if
- # not (pandas.isna(game_price) or pandas.isna(owner_count))])
- genre_totals = [genre for genre_list in dev_data["genres"] if isinstance(genre_list, str)
+
+
+def get_top_revenue_game_labels(data):
+ top_games = data.sort_values(by=["game_revenue"], ascending=False).head(3)
+ top_games_processed = top_games.apply(lambda x: label_with_rev(x["name"], x["game_revenue"], 35,
+ ".", "$"), 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(data):
+ return convert_to_numeric_str(numpy.nansum(data["game_revenue"]))
+
+
+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_games = dev_data.sort_values(by=["game_revenue"], ascending=False)["name"].head(3)
- # top_genres = dict(sorted(genre_totals.items(), key=lambda x: x[1], reverse=True)[:4])
- dev_revenue = "$" + replace_owner_number_with_symbol_real_numeric(round_to_three_largest_digits(
- int(round(numpy.nansum(dev_data["game_revenue"]), -1))))
- dev_top_genre_labels = html.Div("\n".join([genre_c[0] for genre_c in genre_counts]),
+ top_genres_rows = [label_with_text(genre[0], str(genre[1]), 50, ".") for genre in genre_counts]
+ top_genres_with_dot = [" ".join(["•", row]) for row in top_genres_rows]
+ top_genre_labels = html.Div("\n".join(top_genres_with_dot),
style={'white-space': 'pre-line', 'padding-left': '5%'})
- dev_ccu = replace_owner_number_with_symbol_real_numeric(round_to_three_largest_digits(ccu))
- dev_game_count = str(dev_data.shape[0])
- dev_game_revenue_per_game = "$" + replace_owner_number_with_symbol_real_numeric(round_to_three_largest_digits(
- int(round(numpy.nansum(dev_data["game_revenue"]) / len(dev_data["game_revenue"]), -1))))
- dev_top_games_label = html.Div("\n".join(top_games), style={'white-space': 'pre-line', 'padding-left': '5%'})
- user_rating_value = str(round(100 * dev_data["Review_rating"].mean())) + "%"
- return dev_revenue, dev_top_genre_labels, dev_ccu, dev_game_count, dev_game_revenue_per_game, dev_top_games_label, \
- user_rating_value
+ 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, 40, ".")
+
+
+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, 35, ".", "$")
+ 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):
@@ -181,70 +238,124 @@ def initialize_dash(host: str = "0.0.0.0", **kwargs):
style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT,
children=[
html.Div(children=[
- html.Div(style=SMALL_PANEL_DICT | {'width': '60%', 'height': '100%',
- 'margin-right': '5%',
- 'padding-left': '5%',
- 'background-color': TAB_COLOR},
- 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=dict(template="plotly_dark",
- title="Relative genre perfomance",
- plot_bgcolor=TAB_COLOR,
- paper_bgcolor=TAB_COLOR)),
-
- ),
- html.P(f"""Genre performance measures the assessed exploitability of the
+ html.Div(
+ children=[
+ html.Div(style=SMALL_PANEL_DICT | {'width': '60%', 'height': '100%',
+ 'margin-right': '5%',
+ 'padding-left': '5%',
+ 'margin-bottom': '5%',
+ 'background-color': TAB_COLOR},
+ 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=dict(template="plotly_dark",
+ title="Relative genre perfomance",
+ plot_bgcolor=TAB_COLOR,
+ paper_bgcolor=TAB_COLOR)),
+
+ ),
+ html.P(f"""Genre performance measures the assessed exploitability of the
specific game genre. The assesment 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(style={'width': '100%', 'height': '50%'},
- children=[dcc.Graph(
- figure=go.Figure(data=[go.Pie(
- labels=["Action", "Adventure", "RPG", "Puzzle",
- "Strategy",
- "Other"],
- values=[0.8, 0.3, 0.4, 0.4, 0.3, 0.55],
- sort=False)],
- layout=dict(template="plotly_dark",
- title="Genre popularity",
- plot_bgcolor=TAB_COLOR,
- paper_bgcolor=TAB_COLOR,
- 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=["Action", "Adventure", "RPG", "Puzzle",
- "Strategy",
- "Other"],
- values=[0.7, 0.5, 0.1, 0.4, 0.3, 0.7],
- sort=False, )],
- layout=dict(template="plotly_dark",
- title="Genre revenue share",
- plot_bgcolor=TAB_COLOR,
- paper_bgcolor=TAB_COLOR,
- margin=dict(l=20, r=20, t=50, b=20))),
- style={'width': '100%', 'height': '100%'})]
- )])],
- style={'height': '600px', 'width': '100%', 'margin': '0'})
+ 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=["Action", "Adventure", "RPG",
+ "Puzzle",
+ "Strategy",
+ "Other"],
+ values=[0.8, 0.3, 0.4, 0.4, 0.3,
+ 0.55],
+ sort=False)],
+ layout=dict(template="plotly_dark",
+ title="Genre popularity",
+ plot_bgcolor=TAB_COLOR,
+ paper_bgcolor=TAB_COLOR,
+ 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=["Action", "Adventure", "RPG",
+ "Puzzle",
+ "Strategy",
+ "Other"],
+ values=[0.7, 0.5, 0.1, 0.4, 0.3, 0.7],
+ sort=False)],
+ layout=dict(template="plotly_dark",
+ title="Genre revenue share",
+ plot_bgcolor=TAB_COLOR,
+ paper_bgcolor=TAB_COLOR,
+ 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=dict(template="plotly_dark",
+ title="Genre prediction plot",
+ plot_bgcolor=TAB_COLOR,
+ paper_bgcolor=TAB_COLOR,
+ 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=SMALL_PANEL_DICT | {'height': '100%', 'margin-right': '5%',
+ 'padding-left': '5%', 'margin-bottom': '5%',
+ 'margin-top': '5%', 'background-color': TAB_COLOR})
+
+ ],
+ style={'height': '550px', 'width': '100%', 'margin': '0', 'overflow': 'auto'},
+ className="scrollable")
]),
dcc.Tab(label="Game popularity", value="tab2",
style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT),
- dcc.Tab(label="Company revenues", value="tab3",
- style=TAB_NORMAL_DICT, selected_style=TAB_HIGHLIGHT_DICT),
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': 'calc(45% - 10px)', 'height': '600px',
+ ], style=PANEL_DEFAULT_DICT | {'width': 'calc(45% - 10px)',
'margin-right': '100px', 'padding-left': '4%',
'padding-right': '4%', 'padding-bottom': '4%',
'padding-top': '3%', 'margin-bottom': '20px'
@@ -271,16 +382,17 @@ def initialize_dash(host: str = "0.0.0.0", **kwargs):
html.P("Game sale revenue estimates"),
html.Div(children=[
html.Div(children=[
- html.P("Total: ", style=LIST_DICT | {'padding-left': '5%'}),
+ html.P("• Total ....................",
+ style=LIST_DICT | {'padding-left': '5%'}),
html.P(id=DEV_REVENUE_LABEL, children="$524 M",
style=RIGHT_SIDE_TEXT_DICT)
]),
html.Div(children=[
- html.P("Game average:",
- style=LIST_DICT | {'padding-left': '5%'}),
+ # html.P("• Average ...............",
+ # style=LIST_DICT | {'padding-left': '5%'}),
html.P(id=DEV_REV_PER_GAME_LABEL, children="$925 M",
- style=RIGHT_SIDE_TEXT_DICT)
- ])
+ style=LIST_DICT | {'padding-left': '5%'})
+ ]),
],
style={'margin-bottom': '20px'}),
html.Div(children=[
@@ -296,7 +408,6 @@ def initialize_dash(host: str = "0.0.0.0", **kwargs):
html.Div(children=[
html.Div(
children=[
-
html.Div(
children=[html.P("General information",
style=SMALL_PANEL_HEADER_DICT)],
@@ -304,16 +415,14 @@ def initialize_dash(host: str = "0.0.0.0", **kwargs):
'border-bottom': '2px solid ' + TAB_EDGE}),
html.Div(children=[
html.Div(children=[
- html.P("Number of games:", style=LIST_DICT),
html.P(id=DEV_GAME_COUNT_LABEL, children="5",
- style=RIGHT_SIDE_TEXT_DICT),
+ style=LIST_DICT),
],
style={'margin-bottom': '10px'}
),
html.Div(children=[
- html.P("Concurrent users:", style=LIST_DICT),
- html.P(id=DEV_CCU_LABEL, children="92.625.000€",
- style=RIGHT_SIDE_TEXT_DICT),
+ html.P(id=DEV_CCU_LABEL, children="",
+ style=LIST_DICT),
],
style={'margin-bottom': '10px'}
),
@@ -325,10 +434,9 @@ def initialize_dash(host: str = "0.0.0.0", **kwargs):
style={'margin-bottom': '10px'}
),
html.Div(children=[
- html.P("Average game rating", style=LIST_DICT),
html.P(id=DEV_AVERAGE_RATING_LABEL,
- children="0%",
- style=RIGHT_SIDE_TEXT_DICT)
+ children="",
+ style=LIST_DICT)
])
])
])
@@ -350,7 +458,7 @@ def initialize_dash(host: str = "0.0.0.0", **kwargs):
],
style=DEFAULT_TABS_DICT),
],
- style=PANEL_DEFAULT_DICT | {'width': 'calc(30% - 10px)', 'height': '600px', 'margin-right': '4%',
+ style=PANEL_DEFAULT_DICT | {'width': 'calc(30% - 10px)', 'margin-right': '4%',
'padding-left': '3%',
'padding-right': '3%', 'padding-bottom': '4%',
'padding-top': '3%', 'margin-bottom': '20px'
@@ -366,6 +474,6 @@ def initialize_dash(host: str = "0.0.0.0", **kwargs):
if __name__ == "__main__":
initialize_data()
- initialize_dash(debug=True)
+ initialize_dash(debug=False)
print(csv_path)
diff --git a/dash_plot_generation/utils.py b/dash_plot_generation/utils.py
index a9b47bae5f3e89bdf711903906e18fd90a005548..6d380af871fae79d7b108a061f93ee172bcd7264 100644
--- a/dash_plot_generation/utils.py
+++ b/dash_plot_generation/utils.py
@@ -94,7 +94,29 @@ def replace_owner_number_with_symbol_real_numeric(value):
return value_str
-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))
+def update_dots(n):
+ num_dots = (n % 10) + 1
+ dots = "." * num_dots
+ return [dots]
+
+
+def convert_to_numeric_str(value, **kwargs):
+ return replace_owner_number_with_symbol_real_numeric(round_to_three_largest_digits(value, **kwargs))
+
+
+def label_with_rev(label, rev, space, char=".", currency_symbol=""):
+ processed_rev = convert_to_numeric_str(int(rev))
+ return_val = label_with_text(label, "".join([currency_symbol, processed_rev]), space, char)
+ return return_val
+
+
+def label_with_text(first_str, second_str, space, char="."):
+ white_space_filler = char * (space - (len(first_str) + len(second_str)) - 2)
+ return_val = " ".join([first_str, white_space_filler, second_str])
+ return return_val
+
+
+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