def line_plots(data: pd.DataFrame, lang: NullTranslations) -> None:
"""Renders line plots, both general and regional, of data argument. Usually it is the resulting DataFrame from the website of the Protezione civile."""
_ = lang.gettext
# Group data by date
general = calculate_positive_tests_ratio(
data.groupby("data", as_index=False).sum(), lang)
st.title(_("COVID-19 in Italy - Temporal trend"))
st.markdown("### " + _("14-day cases per 100.000:"))
# Get today
today = general["data"].sort_values(ascending=False).iloc[0]
# Filter for most recent 14 days and write calculation
fourteen_day_new_positves = general[today - datetime.timedelta(
days=14) < general["data"]][_("new_positive")]
st.write(
float(
f"{fourteen_day_new_positves.sum() * 100000 / ITALIAN_POPULATION:.2f}"
))
# Indicator chooser
st.markdown(_("What indicator would you like to visualise?"))
features = get_features(general)
feature = st.selectbox(label=_("Choose..."),
options=features,
format_func=formatter,
index=6)
# Add checkbox for diff with most recent data for an indicator
diff = st.checkbox(label=_("Difference with previous datapoint"))
st.markdown(
_("By checking the above box, the indicator will be replaced by the difference of its value between two consecutive days. This helps in untangling cumulative data such as deaths and total tests."
))
if diff:
general = diff_over_previous_datapoint(general, "data", feature)
# Choose log scale or linear, defines what feature to use
general_choice = st.radio(label=_("Scale"),
options=[_("linear"),
_("logarithmic")])
if general_choice == _("logarithmic"):
general = general[general[feature] > 0]
general_scale = alt.Scale(type="log")
else:
general_scale = alt.Scale(type="linear")
st.markdown(("## " + _("General data")))
# Average calculation if needed
is_general_average = st.checkbox(label=_("Average over days"),
key="avg1",
value=True)
if is_general_average:
avg_days = st.slider(
label=_("Days to average over"),
min_value=1,
max_value=21,
value=7,
key="slider1",
)
general_average = average_over_days(general[[feature, "data"]],
categorical_columns=["data"],
avg_days=avg_days)
general = general_average
general_chart = generate_global_chart(general, feature, general_scale,
_("Month and day"))
st.altair_chart(general_chart)
todays_latest = general[general["data"] == today][feature].iloc[0]
st.markdown(
_("Latest data on ") + f"**{formatter(feature).lower()}**: " +
f"{todays_latest:.2f}")
st.markdown(("## " + _("Situation in different regions")))
# Get list of regions and select the ones of interest
region_options = data[_(
"denominazione_regione")].sort_values().unique().tolist()
regions = st.multiselect(
label=_("Regions"),
options=region_options,
default=["Lombardia", "Veneto", "Campania", "Lazio"],
)
# Filter regions in selection
selected_regions = data[data[_("denominazione_regione")].isin(regions)]
if selected_regions.empty:
st.warning(_("No region selected!"))
else:
# Need to handle positive test percentage in if
if feature == _("positivi_per_tampone_%"):
selected_regions = (selected_regions.groupby([
_("denominazione_regione")
]).apply(lambda group: calculate_positive_tests_ratio(group, lang)
).sort_values(by="data", ascending=True).reset_index(
level=0, drop=True).reset_index(drop=True))
if diff:
selected_regions = (selected_regions.groupby([
_("denominazione_regione")
]).apply(lambda group: diff_over_previous_datapoint(
group, "data", feature)).sort_values(
by="data", ascending=True).reset_index(
level=0, drop=True).reset_index(drop=True))
regional_choice = st.radio(label=_("Regional Scale"),
options=[_("linear"),
_("logarithmic")])
if regional_choice == _("logarithmic"):
selected_regions = selected_regions[selected_regions[feature] > 0]
regional_scale = alt.Scale(type="log")
else:
regional_scale = alt.Scale(type="linear")
is_regional_average = st.checkbox(label=_("Average over days"),
key="avg2",
value=True)
if is_regional_average:
avg_days = st.slider(
label=_("Days to average over"),
min_value=1,
max_value=21,
value=7,
key="slider2",
)
regional_average = (selected_regions.groupby(
[_("denominazione_regione")],
as_index=False).apply(lambda group: average_over_days(
group[[feature, "data",
_("denominazione_regione")]],
["data", _("denominazione_regione")],
avg_days,
)).reset_index(level=0, drop=True).reset_index(drop=True))
regional_average_chart = generate_regional_chart(
regional_average,
feature,
regional_scale,
x_title=_("Month and day"),
color_title=_("Region"),
)
st.altair_chart(regional_average_chart)
else:
regional_chart = generate_regional_chart(
selected_regions,
feature,
regional_scale,
x_title=_("Month and day"),
color_title=_("Region"),
)
st.altair_chart(regional_chart)
data = pd.read_csv(DATA_URL,
nrows=nrows,
parse_dates=[['CRASH DATE', 'CRASH TIME']])
data.dropna(subset=['LATITUDE', 'LONGITUDE'], inplace=True)
lowercase = lambda x: str(x).lower()
data.rename(lowercase, axis='columns', inplace=True)
data.rename(columns={'crash date_crash time': 'date/time'}, inplace=True)
data.columns = [column.replace(" ", "_") for column in data.columns]
return data
data = load_data(100000)
original_data = data
st.header("Where are the most numebr of persons injured in NYC?")
injured_people = st.slider("Number of persons injured in vehicle collisions",
0, 19)
st.map(
data.query('number_of_persons_injured >= @injured_people')[[
'latitude', 'longitude'
]].dropna(how="any"))
st.header("How many collisions occur during a given time of day?")
# hour = st.selectbox("Hour to look at", range(1,24), 1)
hour = st.slider("Hour to look at", 0, 23)
data = data[data['date/time'].dt.hour == hour]
st.markdown("Collisions between %i:00 and %i:00" % (hour, (hour + 1) % 24))
#initialize the map with correct points
midpoint = (np.average(data['latitude']), np.average(data['longitude']))
st.write(
pdk.Deck(
# parsing dates and making a year and month col
df['last_review'] = pd.to_datetime(df['last_review'], format="%Y-%m-%d")
df['year'] = df['last_review'].dt.year.astype("Int64")
df['month'] = df['last_review'].dt.month.astype("Int64")
df['day'] = df['last_review'].dt.day.astype("Int64")
return df.dropna().sample(frac=fraction)
df = load_data(fraction=0.1)
st.header('Data')
n_groups = list(df.neighbourhood_group.unique())
neighbourhood_group = st.multiselect(label="Select Neighbourhood Groups",
options=n_groups, default=n_groups)
(min_price, max_price) = st.slider("Price Range", min_value=0, max_value=df.price.max(),
value=(0,300))
st.write(f"Min Price: {min_price}, Max Price: {max_price}")
df = df.query("neighbourhood_group == @neighbourhood_group and @min_price < price < @max_price")
st.text(f'Looking at {df.shape[0]:,} rows selected using above options')
st.dataframe(df.head(5))
st.header("Price ")
fig = px.scatter(df.query("price < 2000"), x="year", y="price", color="room_type",
title="Price over years with price outliers removed")
#fig.update_layout(legend_orientation='h')
st.plotly_chart(fig)
img_placeholder = st.empty()
col1, col2 = st.beta_columns(2)
with col1:
st.write("**Pixel spacing**")
st.write(f"x: {scan.PixelSpacing:.2f} mm")
st.write(f"y: {scan.PixelSpacing:.2f} mm")
st.write(f"z: {scan.SliceSpacing:.2f} mm")
st.write("**Device**")
st.write(f"{scan.ManufacturerModelName} (by {scan.Manufacturer})")
with col2:
overlay_nodules = st.checkbox("Show nodule overlay", value=True)
z = st.slider("Slice:",
min_value=1,
max_value=img_arr.shape[2],
value=int(img_arr.shape[2] / 2))
level = st.number_input("Window level:", value=-600)
width = st.number_input("Window width:", value=1500)
img = get_img_slice(img_arr, z - 1, window=(level, width))
if overlay_nodules:
mask = get_mask_slice(mask_arr, z - 1)
overlay = get_overlay()
ct = Image.composite(overlay, img, mask)
img_placeholder.image(ct, use_column_width=True)
else:
img_placeholder.image(img, use_column_width=True)
st.subheader("Detected nodules")
index=(160))
countries_for_comparison = []
countries_for_comparison.append(country_1_selected)
countries_for_comparison.append(country_2_selected)
countries_for_comparison.append(country_3_selected)
subselect_of_gdp = gdp_df[gdp_df['country'].isin(countries_for_comparison)]
countries_for_plotting = subselect_of_gdp.pivot(index='year',
columns='country',
values='gdppc')
countries_for_plotting = countries_for_plotting.reset_index()
value = st.slider('test',
min_value=1000,
max_value=2018,
value=(1900, 2018),
step=100)
for_plotting = countries_for_plotting[
(countries_for_plotting['year'] >= value[0])
& (countries_for_plotting['year'] <= value[1])]
# Chart title and legends
x_axis_title = 'Date'
y_axis_title = 'GDP per capita'
# State 1 Chart
fig1 = px.line(
for_plotting,
x="year",
y=[country_1_selected, country_2_selected, country_3_selected],
def load_data(nrows):
data = pd.read_csv(DATA_URL,
nrows=nrows,
parse_dates=[['CRASH_DATE', 'CRASH_TIME']])
data.dropna(subset=['LATITUDE', 'LONGITUDE'], inplace=True)
lowercase = lambda x: str(x).lower()
data.rename(lowercase, axis='columns', inplace=True)
data.rename(columns={'crash_date_crash_time': 'date/time'}, inplace=True)
return data
data = load_data(100000)
original_data = data
st.header('Where are the most people injured in NYC?')
injured_people = st.slider('Number of persons injured in vehicle collisions',
0, 19)
st.map(
data.query('injured_persons >= @injured_people')[['latitude', 'longitude'
]].dropna(how='any'))
st.header('How many collisions occur during a given time of day?')
hour = st.slider('How to look up', 0, 23)
data = data[data['date/time'].dt.hour == hour]
st.markdown('Vehicle collisions between %i:00 and %i:00' % (hour,
(hour + 1) % 24))
midpoint = (np.average(data['latitude']), np.average(data['longitude']))
st.write(
pdk.Deck(
map_style='mapbox://styles/mapbox/light-v9',
def main():
# Title and Subheader
st.title("Iris Dataset EDA App")
st.subheader("EDA Web App with Streamlit ")
DATA_URL = (
'https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv'
)
@st.cache(persist=True, show_spinner=True)
def load_data():
data = pd.read_csv(DATA_URL)
data.columns = ('sepal_length', 'sepal_width', 'petal_length',
'petal_width', 'species')
# lowercase = lambda x: str(x).lower()
# data.rename(lowercase, axis='columns', inplace=True)
# data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])
return data
# Create a text element and let the reader know the data is loading.
data_load_state = st.text('Loading data...')
# Load 10,000 rows of data into the dataframe.
data = load_data()
# Notify the reader that the data was successfully loaded.
data_load_state.text('Loading data...Completed!')
# Show Dataset
# if st.checkbox("Preview DataFrame: Head or Tail"):
# if st.button("Head"):
# st.write(data.head())
# if st.button("Tail"):
# st.write(data.tail())
# Show Entire Dataframe
if st.checkbox("View DataFrame"):
st.dataframe(data)
# Show Description
if st.checkbox("View All Column Names"):
st.text("Columns:")
st.write(data.columns)
# Dimensions - Radio Buttonss
# data_dim = st.radio('Check the dimensions of the dataframe',('Rows','Columns'))
# if data_dim == 'Rows':
# st.write("There are", len(data), "Rows in the dataset")
# if data_dim == 'Columns':
# st.write("There are", data.shape[1], "Columns in the dataset")
if st.checkbox("Show Summary of Dataset"):
st.write(data.describe())
st.write("There are", len(data), "rows and", data.shape[1],
"columns in the dataset")
# Selection
if st.checkbox("View Single Column's Data"):
species_option = st.selectbox(
'Select Columns', ('sepal_length', 'sepal_width', 'petal_length',
'petal_width', 'species'))
if species_option == 'sepal_length':
st.write(data['sepal_length'])
elif species_option == 'sepal_width':
st.write(data['sepal_width'])
elif species_option == 'petal_length':
st.write(data['petal_length'])
elif species_option == 'petal_width':
st.write(data['petal_width'])
elif species_option == 'species':
st.write(data['species'])
else:
st.write("Select A Column")
# Show Plots
if st.checkbox("Show Plots"):
st.write("_" * 10)
data.plot(kind='scatter', x='sepal_length', y='sepal_width')
st.pyplot()
st.write(
"---------------- 2D Scatter Plot of Sepal_length vs Sepal_width for all the Species ---------------- "
)
st.write("_" * 10)
st.write(sns.pairplot(data, hue="species", size=3))
# Use Matplotlib to render seaborn
st.pyplot()
st.write(
"---------------- Pairplot of different species ----------------")
st.write("_" * 10)
v_counts = data.groupby('species')
st.bar_chart(v_counts)
st.write(
"---------------- Bar Plot of Groups or Counts ----------------")
st.write("_" * 10)
# Iris Image Manipulation
@st.cache
def load_image(img):
im = Image.open(os.path.join(img))
return im
# Image Type
if st.checkbox("Show/Hide Images"):
species_type = st.radio(
'Have a look at the images of different Iris Species!',
('Setosa', 'Versicolor', 'Virginica'))
if species_type == 'Setosa':
st.text("Showing Setosa Species")
my_image = load_image('images/setosa.png')
elif species_type == 'Versicolor':
st.text("Showing Versicolor Species")
my_image = load_image('images/versicolor.png')
elif species_type == 'Virginica':
st.text("Showing Virginica Species")
my_image = load_image('images/virginica.png')
if st.checkbox("Enhance Image"):
enh = ImageEnhance.Contrast(my_image)
num = st.slider("Contrast", 1.0, 2.0)
img_width = st.slider(
"Zoom in the Image (Set Image Width in Pixels)", 300, 700)
st.image(enh.enhance(num), width=img_width)
else:
img_width = 300
num = 1.2
enh = ImageEnhance.Contrast(my_image)
st.image(enh.enhance(num), width=img_width)
# About
if st.button("About App"):
st.subheader("Iris Dataset EDA App - Developed by Deepankar Kotnala")
st.text("Built with Streamlit")
left_column, right_column = st.beta_columns(2)
button = left_column.button("å³å“ć«ć°ć©ććč”Øē¤ŗ")
with right_column:
if button:
st.line_chart(dg)
# st.area_chart(dg)
# st.bar_chart(dg)
# Map
dm = pd.DataFrame({"lat":[35.702202], "lon":[139.414096]})
# map.token("pk.eyJ1IjoiaGlyb3Rha2VrMDYiLCJhIjoiY2tpbzI2NGgwMTh5dTJyanpxczBtNGZsdyJ9.8WcugSD91Zq4M5KFqPzwvg")
st.map(dm)
# Interactive Wedgets
# input box
st.title("interactive wedgets")
text = st.text_input("ććŖćć®č¶£å³ćęćć¦äøććć")
# text = st.sidebar.text_input("ććŖćć®č¶£å³ćęćć¦äøććć") # sidbarč”Øē¤ŗć®å “å
"ććŖćć®č¶£å³:",text
condition = st.slider("ććŖćć®čŖæåćÆļ¼", 0, 100, 50)
# condition = st..sidebar.slider("ććŖćć®čŖæåćÆļ¼", 0, 100, 50) # sidbarč”Øē¤ŗć®å “å
"ććŖćć®čŖæå:", condition
# expander
expander1 = st.beta_expander("č³Ŗå")
expander1.write("åē")
import streamlit as st
x = st.slider('Select a value')
st.write(x, 'squared id', x**2)
def main():
# Newspaper
def paperHeadlines(paper, number):
top_news = []
if paper == 'Dainik Bhaskar':
web_content = requests.get("https://www.bhaskar.com/coronavirus/")
soup = BeautifulSoup(web_content.text, "html.parser")
for a in soup.findAll('a', attrs={'class': "list_thumb"}):
x = a.get('title')
top_news.append(x)
elif paper == 'Patrika':
web_content = requests.get(
'https://www.patrika.com/topic/coronavirus/')
soup = BeautifulSoup(web_content.text, "html.parser")
top = soup.find_all('div', attrs={'class': 'ctbl-text'})
for i in top:
top_news.append(i.text.strip())
elif paper == 'Navbharat':
web_content = requests.get(
'https://navbharattimes.indiatimes.com/coronavirus/trending/74460387.cms'
)
soup = BeautifulSoup(web_content.text, "html.parser")
top = soup.find_all('a', attrs={'class': 'cor_rest_art'})
for i in top:
top_news.append(i.text.strip())
elif paper == 'Amarujala':
web_content = requests.get(
'https://www.amarujala.com/tags/corona-special-news?page=1')
soup = BeautifulSoup(web_content.text, "html.parser")
top = soup.find_all('h3')
for i in top:
top_news.append(i.text.strip())
elif paper == 'India Today':
web_content = requests.get('https://www.indiatoday.in/coronavirus')
soup = BeautifulSoup(web_content.text, "html.parser")
top = soup.find_all('h3')
for i in top:
top_news.append(i.text.strip())
return top_news[:number]
# return top_news[:number]
# Front Page -----------------------------------------------------------------------
st.markdown(
"<body style='background-color:white;'><h1 style='text-align: center; color: blue;'>REAL TIME COVID-19 ANALYSIS</h1></body>",
unsafe_allow_html=True)
img = Image.open('covid1.PNG')
st.image(img, width=700)
st.markdown(
"<body style='background-color:CornflowerBlue;'><h3 style='text-align: center; color: green;'>Helpline Number for Corona Virus : +91-11-23978046 or 1075</h3></body>",
unsafe_allow_html=True)
st.markdown(
"<a href='https://www.mohfw.gov.in//'><marquee>Click here for Guidelines by Health Ministry of India</marquee></a>",
unsafe_allow_html=True)
st.markdown(
"<body style='background-color:CornflowerBlue;'><h3 style='text-align: center; color: red;'>#INDIAFIGHTSCORONA</h3></body>",
unsafe_allow_html=True)
st.markdown(
"<body style='background-color:DarkTurquoise;'><h3 style='text-align: center; color: black;'>#StayHome_StaySafe</h3></body>",
unsafe_allow_html=True)
day = ['Select', 'Today', 'Yesterday', '2 Days Ago']
st.markdown(
"<body style='background-color:white;'><h1 style='text-align: center; color: green;'>SELECT ACTIVITIES FROM THE SIDEBAR Ć°ÅøāĖ</h1></body>",
unsafe_allow_html=True)
activities = ["Select", "Indian News Paper Headlines"]
st.sidebar.markdown(
"<body style='background-color:CornflowerBlue;'><h3 style='text-align: center; color: black;'>Please Select the Activities</h3></body>",
unsafe_allow_html=True)
# task 1 Newspaper
#*********************
activity = st.sidebar.selectbox("", activities)
if activity == activities[1]:
st.markdown(
"<body style='background-color:white;'><h1 style='text-align: center; color: #a84c32;'>Covid19 Newspaper Headlines</h1></body>",
unsafe_allow_html=True)
news = [
'Select', 'Dainik Bhaskar', 'Patrika', 'Navbharat', 'India Today',
'Amarujala'
]
paper = st.selectbox('', news)
if paper == 'Select':
pass
else:
st.markdown(
"<body style='background-color:white;'><h3 style='text-align: center; color: green;'>Slide through the slider to see the COVID19 news</h3></body>",
unsafe_allow_html=True)
number = st.slider(" ", 1, 15)
headlines = paperHeadlines(paper, number)
for i in headlines:
st.info(i)
else:
pass
st.markdown(
"<body style='background-color:white;'><h3 style='text-align: center; color: RED;'>*********By Ayush kumar*******<h3></body>",
unsafe_allow_html=True)
import streamlit as st
from scraping_news import pull_articles
import pandas as pd
def make_clickable(link, text):
return f'<a target="_blank" href="{link}">{text}</a>'
st.title("Planet Mountain News Roundup")
user_input = st.text_input(
"What would you like to search for? (Default: '9b')", '9b')
num_pages = st.slider("Number of pages to scan", min_value=1, max_value=20)
scan_button = st.button("Scan")
# c1, c2 = st.beta_columns(2)
# if scan_button:
# articles, thumbs, links = pull_articles(num_pages, user_input)
# for article in range(len(articles)):
# c1.image(thumbs[article])
# link = make_clickable(links[article], articles[article])
# df = pd.DataFrame(link, columns=['Link'])
# c2.write(df['Link'].to_html())
# else:
# pass
if scan_button:
articles, thumbs, links = pull_articles(num_pages, user_input)
data = list(zip(thumbs, articles, links))
def main():
session_state = session.SessionState.get(
key=session.get_user_id(),
update=False,
state_name="Acre",
state_id="AC",
city_name="Todos",
administrative_level="Todos",
refresh=False,
reset=False,
already_generated_user_id=None,
pages_open=None,
amplitude_events=None,
button_styles=dict(),
continuation_selection=None,
button_simule=0,
section1_organize=False,
section2_manage=False,
)
utils.localCSS("style.css")
config = yaml.load(open("config/config.yaml", "r"), Loader=yaml.FullLoader)
df = get_data(config)
genSelectBox(df, session_state)
params = dict()
main_icon = utils.load_image("imgs/simulation_main_icon.png")
st.write(
f"""
<div class="text-title-section minor-padding">
Quantos <span class="bold main-orange-span">estudantes e professores(as)</span> retornam Ć s salas de aula em diferentes modelos?
</div>
<div class="container main-padding" style="padding-left:0px;">
<div class="container minor-padding main-orange-span" style="font-size: 20px; color:#FF934A; font-weight: bold;">
<img class="minor-icon" src="data:image/png;base64,{main_icon}" alt="Fonte: Flaticon">
Simule o retorno
</div>
<div class="minor-padding">
O retorno Ć s atividades presenciais deve ser pensado em etapas para definir nĆ£o sĆ³ <b>quem pode retornar</b>, mas tambĆ©m <b>como</b>. Trazemos abaixo um passo a passo para construir a simulaĆ§Ć£o da sua rede - experimente!
</div>
<div class="minor-padding" style="font-size: 20px; color:#FF934A; font-weight: bold;">
<br>Para qual etapa de ensino vocĆŖ estĆ” planejando?
</div>
""",
unsafe_allow_html=True,
)
# TODO: colocar por estado somente tambƩm
# if city_name:
data = df[
(df["city_name"] == session_state.city_name)
& (df["administrative_level"] == session_state.administrative_level)]
col1, col2 = st.beta_columns([0.9, 0.2])
with col1:
education_phase = st.selectbox(
"", data["education_phase"].sort_values().unique())
data = data[data["education_phase"] == education_phase]
with col2:
st.write(
f"""<div class="container">
<br>
</div>
<br>
""",
unsafe_allow_html=True,
)
st.write(
f"""<br>
<div class="container" style="padding-left:0px;">
<div class="minor-padding" style="font-size: 20px; color:#FF934A;"><b>1. Escolha o modelo de retorno Ć s atividades</b></div>
<div class="minor-padding">
Existem diversos modelos possĆveis de retorno avaliadas de acordo com as etapas de aprendizado. Separamos abaixo 5 opƧƵes possĆveis indicadas pela UNESCO.
</div>
</div>
""",
unsafe_allow_html=True,
)
UNESCO_models = {
'Totalmente Presencial': {
"description":
"""Neste modelo, todos os estudantes <b>retornam Ć s aulas
presenciais padrĆ£o</b>, isto Ć©, os mesmos horĆ”rios em sala de
aula, porƩm seguindo os novos protocolos de distanciamento e seguranƧa
sanitƔria.
<br><br><b>Por que este modelo?</b><br>
Modelo tradicional, onde os estudantes e docentes estĆ£o habituados.""",
"hours_per_day": 5,
"priority": False
},
'Aulas presenciais + Tarefas remota': {
"description": """Neste modelo professores(as) <b>transmitem
conceitos para os estudantes presencialmente</b>, e, em seguida,
<b>estudantes completam exercĆcios e tarefas em casa</b>.
<br><br><b>Por que este modelo?</b><br>
Alunos e professores mantĆŖm um contato prĆ³ximo, e estudantes podem tirar dĆŗvidas durante a exposiĆ§Ć£o da matĆ©ria.""",
"hours_per_day": 3,
"priority": False
},
'Aulas por vĆdeo + Tarefas presenciais': {
"description": """Neste modelo estudantes <b>aprendem
novos conceitos de forma remota</b> e, em seguida, <b>concluem exercĆcios e
tarefas presencialmente</b> com o(a) professor(a).
<br><br><b>Por que este modelo?</b><br>
Alunos e professores mantĆŖm o convĆvio, e os estudantes podem tirar dĆŗvidas
urante a realizaĆ§Ć£o dos exercĆcios e se beneficiarem com as dĆŗvidas dos colegas.""",
"hours_per_day": 2,
"priority": False
},
'Grupo prioritƔrio presencial': {
"description":
"""Neste modelo, os professores tĆŖm uma <b>aula normal completa com um grupo
de estudantes presencial, enquanto outro grupo acompanha remotamente
por meio de videoconferĆŖncia (VC)</b>.
<br><br><b>Por que este modelo?</b>
Turma mantĆ©m o convĆvio, mesmo que virtual, e os professores atentem todos da turma no mesmo momento.""",
"hours_per_day": 5,
"priority": True
}
}
col1_1, col1_2, col1_3, col1_4 = st.beta_columns([0.35, 0.05, 0.85, 0.3])
with col1_1:
params["education_model"] = st.selectbox("",
list(UNESCO_models.keys()))
params["priority"] = UNESCO_models[
params["education_model"]]["priority"]
with col1_2:
st.write(
f"""
<div class="container main-padding">
<br>
</div>
""",
unsafe_allow_html=True,
)
with col1_3:
# Sobre o modelo
st.write(
f"""
<div class="col light-green-simulator-bg card-simulator" style="border-radius:30px;">
<div style="font-family: 'Roboto Condensed', sans-serif; padding:10px; margin-bottom:0px; margin-top: 16px;margin-left: 16px; margin-right: 16px;">
<b>{params["education_model"]}</b>
<br><br>{UNESCO_models[params["education_model"]]["description"]}
<br><br><b><a href="https://en.unesco.org/sites/default/files/unesco-covid-19-response-toolkit-hybrid-learning.pdf">FONTE: UNESCO</a></b>
</div>
<div class="button-position" style="margin-bottom: 0px;padding: 10px;margin-top: 16px;margin-right: 16px;margin-left: 16px;">
<a href="#entenda-modelo">
<button class="button-protocolos" style="border-radius: .25rem; font-size:16px; margin-right: 10px;margin-left: 10px;">
leia sobre todos os modelos >
</button>
</a>
</div>
<div class="button-position" style="margin-bottom: 0px;padding: 10px;margin-top: 16px;margin-right: 16px;margin-left: 16px;">
<a href="#entenda-etapa">
<button class="button-protocolos" style="border-radius: .25rem; font-size:16px; margin-right: 10px;margin-left: 10px;">
veja consideraƧƵes por etapa de ensino >
</button>
</a>
</div>
</div>
<div id="entenda-modelo" class="info-modal-window" style="width: 80%; height: 70%;">
<div>
<a href="#" title="Close" class="info-btn-close" style="color: white;">×</a>
<h1 class="main-orange-span bold" style="padding: 0px 50px 0px 50px;">Modelos</h1>
<div style="font-size: 16px; padding: 0px 50px 0px 50px;">
Abaixo hĆ” o quadro completo. Caso nĆ£o consiga ver a imagem, clique na imagem para baixa-la ou <a href="https://drive.google.com/u/1/uc?id=1tqBItM8XkLdY9u2wk0ZcPrVcHccgdp1f&export=download">[AQUI]</a>.
</div>
<a href="https://drive.google.com/u/1/uc?id=1tqBItM8XkLdY9u2wk0ZcPrVcHccgdp1f&export=download"><img style="padding: 50px 50px 50px 50px;" class="images" src="https://i.imgur.com/ZByy47a.jpg"></a>
</div>
</div>
<div id="entenda-etapa" class="info-modal-window" style="width: 80%; height: 70%;">
<div>
<a href="#" title="Close" class="info-btn-close" style="color: white;">×</a>
<h1 class="main-orange-span bold" style="padding: 0px 50px 0px 50px;">Etapas de Ensino</h1>
<div style="font-size: 16px; padding: 0px 50px 0px 50px;">
<br>
<b>4 - 8 anos</b><br>
Pontos principais para consideraĆ§Ć£o:<br>
<li>CrianƧas desta faixa etƔria possuem menor risco de apresentar sintomas graves.</li>
<li>Pais e responsƔveis necessitam de creches e suporte para manter demais atividades do dia a dia</li>
<li>EficƔcia muito baixa do ensino remoto</li><br>
<b>8 - 12 anos</b><br>
Pontos principais para consideraĆ§Ć£o:<br>
<li>CrianƧas desta faixa etƔria possuem menor risco de apresentar sintomas graves, mas hƔ maior dificuldade em adotar medidas sanitƔrias.</li>
<li>JĆ” possuem maior autonomia no cotidiano e pode</li><br>
<b>12 - 17 anos</b><br>
Pontos principais para consideraĆ§Ć£o:<br>
<li>CrianƧas desta faixa etĆ”ria possuem maior risco intrĆnseco de contrair e desenvolver sintomas, mas apresentam maior aderĆŖncia aos protocolos sanitĆ”rios</li>
<li>LogĆstica de agendamento presencial pode ser mais complexa, pois os anos possuem matĆ©rias e professores diversos.</li><br>
<b>17 - 18 anos</b><br>
Pontos principais para consideraĆ§Ć£o:<br>
<li>CrianƧas desta faixa etĆ”ria possuem maior risco intrĆnseco de contrair e desenvolver sintomas, mas apresentam maior aderĆŖncia aos protocolos sanitĆ”rios.</li>
<li>Alta eficĆ”cia e adesĆ£o ao mĆ©todo remoto</li>
<br>Abaixo hĆ” o quadro completo. Caso nĆ£o consiga ver a imagem, clique na imagem para baixa-la ou <a href="https://drive.google.com/u/1/uc?id=1Sj65MXPkRcw6VxojYBLsJ8otIuvpLfq_&export=download">[AQUI]</a>.
</div>
<a href="https://drive.google.com/u/1/uc?id=1Sj65MXPkRcw6VxojYBLsJ8otIuvpLfq_&export=download"><img style="padding: 50px 50px 50px 50px;" class="images" src="https://i.imgur.com/FyoIFe9.jpg"></a>
</div>
</div>
""",
unsafe_allow_html=True,
)
with col1_4:
st.write(
f"""<div class="container">
<br>
</div>
<br>
""",
unsafe_allow_html=True,
)
st.write(
f"""<br>
<div class="container" style="padding-left:0px;">
<div class="minor-padding" style="font-size: 20px; color:#FF934A;"><b>2. Escolha quem pode retornar</b></div>
</div>
""",
unsafe_allow_html=True,
)
col2a_1, col2a_2, col2a_3, col2a_4 = st.beta_columns(
[0.35, 0.05, 0.85, 0.3])
with col2a_1:
params["number_students"] = st.number_input(
"Quantos estudantes retornam Ć s aulas presenciais?",
format="%d",
value=data["number_students"].values[0],
step=1,
)
if params["priority"]:
params["number_remote_students"] = st.number_input(
"Quantos estudantes acompanham Ć s aulas somente de forma remota?",
format="%d",
value=data["number_students"].values[0],
step=1,
)
with col2a_2:
st.write(
f"""
<div class="container main-padding">
<br>
</div>
""",
unsafe_allow_html=True,
)
with col2a_3:
st.write(
f"""
<div class="col light-green-simulator-bg card-simulator" style="border-radius:30px;">
<div class="row" style="font-family: 'Roboto Condensed', sans-serif; margin-bottom:0px; padding:10px;">
<b>Iniciamos com total de estudantes reportados no Censo Escolar 2019 (INEP).</b>
<br>VocĆŖ pode alterar esse valor ao lado. Leve em consideraĆ§Ć£o quais grupos de estudantes podem ser vulnerĆ”veis ou ter prioridade.
</div>
<div class="button-position" style="padding-bottom: 15px;">
<a href="#entenda-estudantes">
<button class="button-protocolos" style="border-radius: .25rem; font-size:16px; margin-right: 10px;margin-left: 10px;">
grupos que requerem atencĆ£o especial >
</button>
</a>
</div>
</div>
<div id="entenda-estudantes" class="info-modal-window" style="width: 80%; height: 70%;">
<div>
<a href="#" title="Close" class="info-btn-close" style="color: white;">×</a>
<h1 class="main-orange-span bold" style="padding: 0px 50px 0px 50px;">Estudantes</h1>
<div style="font-size: 20px; padding: 0px 50px 0px 50px;">
<b>Grupos que requerem atencĆ£o especial</b>
</div>
<br>
<div style="font-size: 16px; padding: 0px 50px 0px 50px;">
<b>Exemplos de grupos vulnerƔveis ou/e marginalizados</b>
<li>Minorias</li>
<li>Meninas adolescentes</li>
<li>CrianƧas com deficiĆŖncia de aprendizagem</li>
<li>CrianƧas que vivem em instituiƧƵes de abrigo</li>
<li>CrianƧas vivendo em condiĆ§Ć£o de pobreza, em residĆŖncias com alta ocupĆ¢ncia ou improvisadas</li>
<li>OrfĆ£os</li>
<li>CrianƧas separadas de seus responsƔveis</li>
<li>CrianƧas e adolescentes em risco de abandono escolar</li>
</div>
</div>
</div>
""",
unsafe_allow_html=True,
)
with col2a_4:
st.write(
f"""<div class="container">
<br>
</div>
<br>
""",
unsafe_allow_html=True,
)
st.write(
f"""
<div class="container main-padding">
<br>
</div>
""",
unsafe_allow_html=True,
)
col2b_1, col2b_2, col2b_3, col2b_4 = st.beta_columns(
[0.35, 0.05, 0.85, 0.3])
with col2b_1:
params["number_teachers"] = st.number_input(
"Quantos professores(as) retornam?",
format="%d",
value=data["number_teachers"].values[0],
step=1,
)
col2b_2 = col2a_2
with col2b_3:
st.write(
f"""
<div class="col light-green-simulator-bg card-simulator" style="border-radius:30px;">
<div class="row" style="font-family: 'Roboto Condensed', sans-serif; margin-bottom:0px; padding:10px;">
<b>Iniciamos com total de professores reportados no Censo Escolar 2019 (INEP).</b>
<br>VocĆŖ pode alterar esse valor ao lado. Leve em consideraĆ§Ć£o quais grupos de professores podem ser de risco, confortĆ”veis para retorno e outros.
</div>
<div class="button-position" style="padding-bottom: 15px;">
<a href="#entenda-professores">
<button class="button-protocolos" style="border-radius: .25rem; font-size:16px; margin-right: 10px;margin-left: 10px;">
como retornar professores(as) >
</button>
</a>
</div>
<div id="entenda-professores" class="info-modal-window" style="width: 80%; height: 70%;">
<div>
<a href="#" title="Close" class="info-btn-close" style="color: white;">×</a>
<h1 class="main-orange-span bold" style="padding: 0px 50px 0px 50px;">Professores</h1>
<div style="font-size: 16px; padding: 0px 50px 0px 50px;">
<b>Fatores a serem considerados:</b> grupos vulnerĆ”veis, nĆŗmero de casos suspeitos, desconforto da rede com o retorno presencial, dificuldade logĆstica e a disponibilidade de retorno presencial.
<br><br>O quadro explicativo traz para cada fator um desafio e uma aĆ§Ć£o sugerida.
<br><br>Caso nĆ£o consiga ver a imagem, clique na imagem para baixa-la ou <a href="https://drive.google.com/u/1/uc?id=1lLtbEMau4nIj8tZ5rQF51ThV2Q8K1DzE&export=download">[AQUI]</a>.
</div>
<a href="https://drive.google.com/u/1/uc?id=1lLtbEMau4nIj8tZ5rQF51ThV2Q8K1DzE&export=download"><img style="padding: 50px 50px 50px 50px;" class="images" src="https://i.imgur.com/4ai7xDK.jpg"></a>
</div>
</div>
</div>
""",
unsafe_allow_html=True,
)
col2b_4 = col2a_4
st.write(
f"""
<br>
<div class="container" style="padding-left:0px;">
<div class="minor-padding" style="font-size: 20px; color:#FF934A;"><b>3. Defina as restriƧƵes de retorno</b></div><br>
</div>
</div>
</div>
""",
unsafe_allow_html=True,
)
col3_1, col3_2, col3_3, col3_4, col3_5, col3_6 = st.beta_columns(
[0.35, 0.05, 0.4, 0.05, 0.4, 0.3])
with col3_1:
params["number_classrooms"] = st.number_input(
"Quantas salas de aula disponĆveis?",
format="%d",
value=data["number_classroms"].values[0],
step=1,
)
st.write(
f"""
<div class="row" style="margin:0px; padding:10px; background:#DDFBF0; border-radius: 1rem 1rem 1rem 1rem;">
OĀ nĆŗmero de salas restringeĀ o nĆŗmero de turmas que podem voltar de forma simultĆ¢nea.
</div>
""",
unsafe_allow_html=True,
)
col3_2 = col2a_2
with col3_3:
params["max_students_per_class"] = st.slider(
"Selecione o mƔximo de estudantes por turma:", 0, 20, 20, 1)
st.write(
f"""
<div class="row" style="margin:0px; padding:10px; background:#DDFBF0; border-radius: 1rem 1rem 1rem 1rem;">
Limitamos em 20 estudantes por sala para diminiuir o risco de transmissĆ£o seguindo critĆ©rios sanitĆ”rios.
</div>
""",
unsafe_allow_html=True,
)
col3_4 = col2a_2
with col3_5:
params["hours_per_day"] = int(
st.slider(
"Selecione o nĆŗmero de horas presenciais diĆ”rias na escola por turma:",
min_value=1,
max_value=5,
value=UNESCO_models[params["education_model"]]
["hours_per_day"],
step=1,
))
st.write(
f"""
<div class="row" style="margin:0px; padding:10px; background:#DDFBF0; border-radius: 1rem 1rem 1rem 1rem;">
As restriƧƵes sanitĆ”rias limitam a quantidade de tempo e estudantes que conseguem retornar Ć sala de aula.
</div>
<div class="container">
<br>
</div>
<br>
""",
unsafe_allow_html=True,
)
col3_6 = col2a_4
with st.beta_expander("simular retorno"):
user_analytics = amplitude.gen_user(utils.get_server_session())
opening_response = user_analytics.safe_log_event(
"clicked simule retorno", session_state, is_new_page=True)
print(params)
genSimulationResult(params, config)
'''if st.button("Simular retorno"):
if st.button("Esconder"):
pass
genSimulationResult()
utils.stylizeButton(
name="SIMULAR RETORNO",
style_string="""
box-sizing: border-box;
border-radius: 15px;
width: 150px;padding: 0.5em;
text-transform: uppercase;
font-family: 'Oswald', sans-serif;
background-color: #0097A7;
font-weight: bold;
text-align: center;
text-decoration: none;font-size: 18px;
animation-name: fadein;
animation-duration: 3s;
margin-top: 1.5em;""",
session_state=session_state,
)'''
# TODO: escrever metodologia v1.2
with st.beta_expander("ler metodologia"):
user_analytics = amplitude.gen_user(utils.get_server_session())
opening_response = user_analytics.safe_log_event(
"clicked simule metodologia", session_state, is_new_page=True)
methodology_text = load_markdown_content("methodology_short.md")
st.write(methodology_text)
def app():
def text_on_page(dict_var, id_json, list_res, page):
if type(dict_var) is dict:
for k, v in dict_var.items():
if k == id_json and v == page:
if v > page: return list_res
list_res.append(dict_var["text"])
elif isinstance(v, dict):
text_on_page(v, id_json, list_res, page)
elif isinstance(v, list):
for item in v:
text_on_page(item, id_json, list_res, page)
return list_res
def get_page(data, page):
lines = []
for chunk in data["elements"]:
lines.extend(text_on_page(chunk, "page", [], page))
return lines
def get_histogram(docs, top=20):
tokens = []
for s in docs.values():
tokens += s.split()
uniques, counts = np.unique(tokens, return_counts=True)
sorted_inds = np.argsort(counts)
uniques_sorted = uniques[sorted_inds[-top:]][::-1]
counts_sorted = counts[sorted_inds[-top:]][::-1]
return (uniques_sorted, counts_sorted)
file = st.file_uploader("test", type="pdf", key=2)
start = 1
max_val = 1000
end = 5
slider_val = st.slider('Page range:',
min_value=start,
max_value=max_val,
value=(1, end),
step=1)
if file is not None:
file_details = {
"FileName": file.name,
"FileType": file.type,
"FileSize": str(file.size / 1000000) + 'mb'
}
data_load_state = st.text('Loading data... Thank you for waiting š')
parser = HierarchyParser()
source = FileSource(file, page_numbers=list(range(start - 1, end)))
@st.cache(suppress_st_warning=True)
def fetch_doc(source):
return parser.parse_pdf(source)
document = fetch_doc(source)
printer = JsonFilePrinter()
file_path = pathlib.Path('pdf.json')
printer.print(document, file_path=str(file_path.absolute()))
with open('pdf.json') as json_file:
data = json.load(json_file)
json_file.close()
pages = {
i: get_page(data, i)
for i in range(slider_val[0], slider_val[1])
}
(formatted_docs,
paragraph_page_idx) = preprocessing2.get_formatted_docs(
pages, max_paragraphs=5)
preprocessed_docs = preprocessing2.get_preprocessed_docs(
formatted_docs)
data_load_state.text("Done!")
st.write(file_details)
with st.beta_expander("PDF Extraction details"):
st.subheader('First paragraphs on page ' + str(slider_val[0]))
if len(pages[slider_val[0]]) >= 5:
for i in range(5):
st.markdown("<u>Ā¶ " + str(i + 1) + "</u>: " +
pages[slider_val[0]][i],
unsafe_allow_html=True)
else:
for i in range(len(pages[slider_val[0]])):
st.markdown("<u>Ā¶ " + str(i + 1) + "</u>: " +
pages[slider_val[0]][i],
unsafe_allow_html=True)
st.subheader('PDF word distribution')
(uniques, counts) = get_histogram(preprocessed_docs)
fig = px.bar(x=uniques, y=counts)
fig.update_xaxes(title_text='words')
fig.update_yaxes(title_text='occurances')
st.plotly_chart(fig)
st.subheader('Paragraph similarity heatmap')
tfidf_vectorizer = cosine2.get_tfidf_vectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(
list(preprocessed_docs.values())).toarray()
query1 = st.text_input("Cosine-SVD Search")
if query1:
q = cosine2.get_query_vector(query1, tfidf_vectorizer)
cos_sims = cosine2.get_cosine_sim(q, tfidf_matrix)
(rankings, scores) = cosine2.get_rankings(cos_sims)
idx = rankings[0]
score = scores[0]
page_num = paragraph_page_idx[idx] + 1
doc = formatted_docs[idx]
if score > 0.0:
st.subheader("Similarity: " + str(score))
st.markdown("<u>Match</u>: " + str(doc),
unsafe_allow_html=True)
st.markdown("<u>Page Number</u>: " + str(page_num),
unsafe_allow_html=True)
#write match and query to the db
doc_ref = db.collection("queries").document()
doc_ref.set({
"query": query1,
"topMatch": str(doc),
"timeStamp": firestore.SERVER_TIMESTAMP,
"upvote": 0
})
else:
st.subheader("No matches found.")
st.write("Following methods are under construction š Stay tuned!")
query2 = st.text_input("Synonymized Query Search")
query3 = st.text_input("Verbatim Search")
st.subheader("Recent search results:")
q_ref = db.collection("queries").order_by(
u'timeStamp', direction=firestore.Query.DESCENDING)
counter = 0
yesButtons = []
noButtons = []
for doc in q_ref.stream():
counter += 1
doc_dict = doc.to_dict()
st.markdown("<strong>Query " + str(counter) + "</strong>: \n",
unsafe_allow_html=True)
st.markdown("<u>Query</u>: " + doc_dict["query"] + "\n",
unsafe_allow_html=True)
st.markdown("<u>Top Match</u>: " + doc_dict["topMatch"] + "\n",
unsafe_allow_html=True)
st.markdown(" ")
if doc_dict["upvote"] < 0:
st.markdown("<small>So far " + str(abs(doc_dict["upvote"])) +
"people don't think it's a good match.</small>",
unsafe_allow_html=True)
else:
st.markdown("<small>So far " + str(doc_dict["upvote"]) +
" people think it's a good match.</small>",
unsafe_allow_html=True)
st.markdown("<i><small>Do you think this is a good match?</small></i>",
unsafe_allow_html=True)
yesButtons.append(st.button("š", key="YesButton" + str(counter)))
noButtons.append(st.button("š", key="NoButton" + str(counter)))
st.markdown("<hr>", unsafe_allow_html=True)
if counter == 5:
break
st.subheader('made with ā¤ļø by:')
st.markdown(
'[Vince Bartle](https://bartle.io) (vb344) | [Dubem Ogwulumba](https://www.linkedin.com/in/dubem-ogwulumba/) (dao52) | [Erik Ossner](https://erikossner.com/) (eco9) | [Qiyu Yang](https://github.com/qiyuyang16/) (qy35) | [Youhan Yuan](https://github.com/nukenukenukelol) (yy435)'
)
mn = df[col].min().item()
mx = df[col].max().item()
defv = df[col].mean()
if mx > mn:
sp = (mx - mn) / 100
sp = round(sp, 1 - int(floor(log10(abs(sp)))) - 1)
if df[col].dtype == np.int64:
defv = np.ceil(defv).astype(int).item()
sp = np.ceil(sp).astype(int).item()
val = st.slider(
"{} ({})".format(col, df[col].dtype),
min_value=mn,
max_value=mx,
step=sp,
value=defv,
)
else:
val = mx
else:
defv = df[col].mode().item()
uniq = pd.unique(df[col]).tolist()
val = st.selectbox(
"{} ({})".format(col, df[col].dtype), options=uniq, index=uniq.index(defv)
)
list_form += [val]
default_form += [defv]
import numpy as np
from scipy import optimize
from matplotlib import pyplot as pl
def dynes(E, gamma, delta, N0):
numerator = E - gamma*1j
denominator = np.sqrt( (E-gamma*1j)**2 - delta**2 )
return N0*np.abs(np.real(numerator/denominator))
f = 'stm.csv'
stm = pd.read_csv(f, names=['bias', 'didv'])
# try:
# f = 'C:/Users/Kuri y Rizu/Documents/Synced Folders/UW-Madison/Oxide Lab/UW Papers/RF Q0 paper/stm.csv'
# stm = pd.read_csv(f, names=['bias', 'didv'])
# except:
# f = '/home/chris/Documents/Synced Folders/UW-Madison/Oxide Lab/UW Papers/RF Q0 paper/stm.csv'
# stm = pd.read_csv(f, names=['bias', 'didv'])
p, cov = optimize.curve_fit(dynes, stm.bias, stm.didv, p0=[0.2, 3, 1])
gamma = st.slider('gamma', min_value=0.0, max_value=1.0, step=0.01, value=float(p[0]))
delta = st.slider('delta', min_value=0.0, max_value=4.0, step=0.1, value=float(p[1]))
N0 = st.slider('N0', min_value=0.0, max_value=1.2, step=0.01, value=float(p[2]))
fit = dynes(stm['bias'], gamma, delta, N0)
fig = pl.figure()
pl.plot(stm['bias'], stm['didv'], '-o', color='blue')
pl.plot(stm['bias'], fit, '-', color='red')
st.pyplot(fig)
def write():
"""Used to write the page in the app.py file"""
with st.spinner("Loading Map ..."):
#ast.shared.components.title_awesome("") #Title Awesome Streamlit ausgeblendet
# read CSV
# CSV for Choropleth Map
df = pd.read_csv(
"https://raw.githubusercontent.com/hannahkruck/awesome-test/master/Map.csv",
encoding="utf8",
sep=";")
# CSV for Line Map
df2 = pd.read_csv(
"https://raw.githubusercontent.com/hannahkruck/awesome-test/master/Map.csv",
encoding="utf8",
sep=";")
# Title
st.title("Map view")
#----------------- Side bar (filter options) -------------------
# Select map (Choropleth or Line Map)
selectedMapType = st.sidebar.radio("Map",
('Choropleth Map', 'Line Map'))
if selectedMapType == 'Choropleth Map':
showChoropleth = True
showLine = False
else:
showLine = True
showChoropleth = False
# General filter (Age, Gender)
st.sidebar.header("Filters")
selectedAge = st.sidebar.multiselect(
"Select Age", ("under 18", "18 - 34", "35 - 64", "over 65"))
selectedGender = st.sidebar.selectbox("Select Gender",
("All", "Male", "Female"))
# Special filter for Choropleth Map
st.sidebar.header("Filter for Choropleth Map")
# Drop down menu for Choropleth Map Information
selectedMapChoropleth = st.sidebar.selectbox(
"Select Map Information", ('Applications to target countries',
'Applicants by country of origin'))
# Information for Choropleth Map based on the chosen map information
if 'target' in selectedMapChoropleth:
selectedMapChoropleth = 'destinationCountry'
selectedCode = 'geoCodeDC'
mapColor = 'Blues'
else:
selectedMapChoropleth = 'homeCountry'
selectedCode = 'geoCodeHC'
mapColor = 'Reds'
# Special filter for Line Map
st.sidebar.header("Filter for Line Map")
# Select type (show routes of asylum seeker from a particular origin country or to a particular target country)
selectedType = st.sidebar.radio("Select type",
('Target country', 'Origin country'))
if selectedType == 'Target country':
selectedType = df.destinationCountry.unique()
countryCategory = 'destinationCountry'
namesToShow = 'homeCountry'
selectedLon = 'lonDC'
selectedLat = 'latDC'
else:
selectedType = df.homeCountry.unique()
countryCategory = 'homeCountry'
namesToShow = 'destinationCountry'
selectedLon = 'lonHC'
selectedLat = 'latHC'
# Drop down menu for selected country
selectedCountryMapLine = st.sidebar.selectbox("Select country",
(selectedType))
#----------------- Website content (Year slider, i-Button) -------------------
# Markdown for i-Button
# CSS and HTML Code
st.markdown('''
<!-- https://www.w3schools.com/css/tryit.asp?filename=trycss_tooltip_transition & https://www.w3schools.com/css/tryit.asp?filename=trycss_tooltip_right-->
<style>
.tooltip {
position: relative;
display: inline-block;
font-size:1.6rem;
}
.tooltip .tooltiptext {
visibility: hidden;
width: 50vw;
background-color: #f1f3f7;
color: #262730;
text-align: justify;
border-radius: 6px;
padding: 5px;
font-size:0.9rem;
/* Position the tooltip */
position: absolute;
z-index: 1;
top: -5px;
left: 105%;
opacity: 0;
transition: opacity 0.8s;
}
.tooltip:hover .tooltiptext {
visibility: visible;
opacity: 1;
}
</style>
''',
unsafe_allow_html=True)
# Text for tooltip
st.markdown('''
<div class="tooltip">ⓘ
<span class="tooltiptext">
<b>Choropleth Map</b><br>The Choropleth Map shows the number of asylum applications per country in Europe and the number of refugees per country worldwide for the selected year (see filter 'Select Map Information' for Choropleth Map).
<br><br>
<b>Line Map</b><br>The Line Map presents the routes of the refugees depending on the selected type. The type 'target country' shows from which countries the asylum seekers originate based on a specific target country. The type 'origin country' indicates where the asylum seekers are fleeing to from a specific country of origin.
</span></div>
''',
unsafe_allow_html=True)
# Slider to choose the year
selected_year = st.slider("", (int(df["year"].min())),
(int(df["year"].max())))
# Title for map regarding the chosen year
st.subheader('Asylum seekers in the year %s' % selected_year)
#----------------- Data preparation (general) -------------------
# Remove 'overall' and 'Ćberseeische LƤnder und Hoheitsgebiet' for both CSV
indexNames = df[df['destinationCountry'] == 'Overall'].index
df.drop(indexNames, inplace=True)
indexNames = df[df['homeCountry'] == 'Overall'].index
df.drop(indexNames, inplace=True)
indexNames = df[df['destinationCountry'] ==
'Ćberseeische LƤnder und Hoheitsgebiete'].index
df.drop(indexNames, inplace=True)
indexNames = df[df['homeCountry'] ==
'Ćberseeische LƤnder und Hoheitsgebiete'].index
df.drop(indexNames, inplace=True)
indexNames = df2[df2['destinationCountry'] == 'Overall'].index
df2.drop(indexNames, inplace=True)
indexNames = df2[df2['homeCountry'] == 'Overall'].index
df2.drop(indexNames, inplace=True)
indexNames = df2[df2['destinationCountry'] ==
'Ćberseeische LƤnder und Hoheitsgebiete'].index
df2.drop(indexNames, inplace=True)
indexNames = df2[df2['homeCountry'] ==
'Ćberseeische LƤnder und Hoheitsgebiete'].index
df2.drop(indexNames, inplace=True)
# Delete all cells, except one year (both maps)
indexNames = df[df['year'] != selected_year].index
df.drop(indexNames, inplace=True)
indexNames = df2[df2['year'] != selected_year].index
df2.drop(indexNames, inplace=True)
#----------------- Data preparation (Choropleth Map) -------------------
# Information for Choropleth Map (df) based on the chosen gender and age
df['subtotal'] = 0
# Check selected gender
if selectedGender == 'Female':
# if an age is selected
if selectedAge:
# selectedAge is a list of strings
# Therefore, we have to check every entry in the list and sum up partial results in new column subtotal
for i in selectedAge:
if i == 'under 18':
df['subtotal'] = df['subtotal'] + df['fu18']
elif i == '18 - 34':
df['subtotal'] = df['subtotal'] + df['f18']
elif i == '35 - 64':
df['subtotal'] = df['subtotal'] + df['f35']
elif i == 'over 65':
df['subtotal'] = df['subtotal'] + df['fo65']
else: # no age is selected, that means the user wants to see all women
df['subtotal'] = df['subtotal'] + df['womenTotal']
a = 'subtotal'
elif selectedGender == 'Male':
if selectedAge:
for i in selectedAge:
if i == 'under 18':
df['subtotal'] = df['subtotal'] + df['mu18']
elif i == '18 - 34':
df['subtotal'] = df['subtotal'] + df['m18']
elif i == '35 - 64':
df['subtotal'] = df['subtotal'] + df['m35']
elif i == 'over 65':
df['subtotal'] = df['subtotal'] + df['mo65']
else:
df['subtotal'] = df['subtotal'] + df['menTotal']
a = 'subtotal'
else: # if no gender is selected, that means the user wants to see all
if selectedAge:
for i in selectedAge:
if i == 'under 18':
df['subtotal'] = df['subtotal'] + df['mu18'] + df[
'fu18']
elif i == '18 - 34':
df['subtotal'] = df['subtotal'] + df['m18'] + df['f18']
elif i == '35 - 64':
df['subtotal'] = df['subtotal'] + df['m35'] + df['f35']
elif i == 'over 65':
df['subtotal'] = df['subtotal'] + df['fo65'] + df[
'mo65']
a = 'subtotal'
else:
a = 'total'
# Group the countries by year and sum up the number (total) in a new column sum (df['sum']
df['sum'] = df.groupby([selectedMapChoropleth,
'year'])[a].transform('sum')
#----------------- Data preparation (Line Map) -------------------
# countryCategory = homeCountry or destinationCountry
# selectedCountryMapLine is the selected country for the map line (for example Syria (homeCountry))
indexNames = df2[df2[countryCategory] != selectedCountryMapLine].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = 0
if selectedGender == 'Female':
# if an age is selected
if selectedAge:
# selectedAge is a list of strings
# Therefore, we have to check every entry in the list and delete the row if the value in the column for the age is null
for i in selectedAge:
if i == 'under 18':
indexNames = df2[df2['fu18'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['fu18']
elif i == '18 - 34':
indexNames = df2[df2['f18'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['f18']
elif i == '35 - 64':
indexNames = df2[df2['f35'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['f35']
elif i == 'over 65':
indexNames = df2[df2['fo65'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['fo65']
else:
indexNames = df2[df2['womenTotal'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['womenTotal']
elif selectedGender == 'Male':
if selectedAge:
# selectedAge is a list of strings
# Therefore, we have to check every entry in the list and delete the row if the value in the column for the age is null
for i in selectedAge:
if i == 'under 18':
indexNames = df2[df2['mu18'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['mu18']
elif i == '18 - 34':
indexNames = df2[df2['m18'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['m18']
elif i == '35 - 64':
indexNames = df2[df2['m35'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['m35']
elif i == 'over 65':
indexNames = df2[df2['mo65'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['mo65']
else:
indexNames = df2[df2['menTotal'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['menTotal']
else: # if no gender is selected, that means the user wants to see all
if selectedAge:
for i in selectedAge:
if i == 'under 18':
indexNames = df2[df2['mu18'] == 0].index
df2.drop(indexNames, inplace=True)
indexNames = df2[df2['fu18'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['mu18'] + df2[
'fu18']
elif i == '18 - 34':
indexNames = df2[df2['m18'] == 0].index
df2.drop(indexNames, inplace=True)
indexNames = df2[df2['f18'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['m18'] + df2[
'f18']
elif i == '35 - 64':
indexNames = df2[df2['m35'] == 0].index
df2.drop(indexNames, inplace=True)
indexNames = df2[df2['f35'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['m35'] + df2[
'f35']
elif i == 'over 65':
indexNames = df2[df2['mo65'] == 0].index
df2.drop(indexNames, inplace=True)
indexNames = df2[df2['fo65'] == 0].index
df2.drop(indexNames, inplace=True)
df2['subtotal'] = df2['subtotal'] + df2['mo65'] + df2[
'fo65']
else: # all people are considered
indexNames = df2[df2['total'] == 0].index
df2.drop(indexNames, inplace=True)
# Create list of origin or target countries to display them in hover text
# Every second index must contain the country name, so a placeholder is necessary in front of it
# Structur: [placeholder,name+number,placeholder,name+number,...]
# name = listPlaceholderNames
# number = listPlaceholderNumber
listPlaceholderNames = df2[namesToShow].values.tolist()
listPlaceholderNumber = df2[a].values.tolist()
nameList = []
i = 0
if namesToShow == 'homeCountry':
for x in listPlaceholderNames:
nameList.append(i)
x = x + ': ' + str(listPlaceholderNumber[i])
nameList.append(x)
i = i + 1
if len(nameList) != 0:
nameList[-2] = None
else:
for x in listPlaceholderNames:
x = x + ': ' + str(listPlaceholderNumber[i])
nameList.append(x)
nameList.append(i)
i = i + 1
if len(nameList) != 0:
nameList[-1] = None
st.write(
'<style>div.Widget.row-widget.stRadio > div{flex-direction:row;}</style>',
unsafe_allow_html=True)
#----------------Create Maps with Plotly (Choropleth and Line Map)---------------------------
#Link Toggle Map https://plotly.com/python/custom-buttons/
fig = go.Figure()
# Choropleth Map
fig.add_trace(
go.Choropleth(
locations=df[selectedCode],
visible=showChoropleth,
z=df['sum'],
text=df[selectedMapChoropleth],
colorscale=mapColor,
autocolorscale=False,
reversescale=False,
name="",
marker_line_color='darkgray',
marker_line_width=0.5,
colorbar_tickprefix='',
colorbar_title='Number of<br>asylum<br>applications<br>',
))
# Line Map
fig.add_trace(
go.Scattergeo(locationmode='country names',
lon=df2[selectedLon],
lat=df2[selectedLat],
hoverinfo='text',
name=selectedCountryMapLine,
text=df2[countryCategory],
line=dict(width=1, color='red'),
opacity=0.510,
visible=showLine,
mode='markers',
marker=dict(size=3,
color='rgb(255, 0, 0)',
line=dict(
width=3,
color='rgba(68, 68, 68, 0)',
))))
lons = []
lats = []
lons = np.empty(2 * len(df2))
lons[::2] = df2['lonDC']
lons[1::2] = df2['lonHC']
lats = np.empty(2 * len(df2))
lats[::2] = df2['latDC']
lats[1::2] = df2['latHC']
#hallo = 'testi'
fig.add_trace(
go.Scattergeo(locationmode='country names',
visible=showLine,
name='route and number <br>of asylum seekers',
text=nameList,
hovertemplate=nameList,
lon=lons,
lat=lats,
mode='markers+lines',
line=dict(width=1, color='red'),
opacity=0.5))
fig.update_layout(
showlegend=True,
geo=go.layout.Geo(
scope='world',
#projection_type = 'azimuthal equal area',
showland=True,
showcountries=True,
landcolor='rgb(243, 243, 243)',
countrycolor='rgb(105,105,105)',
),
)
fig.update_layout(
geo=dict(showframe=False,
showcoastlines=False,
projection_type='equirectangular'),
autosize=True,
margin=dict(
l=0,
r=0,
b=0,
t=20,
),
)
# Display figure
st.plotly_chart(
fig,
use_container_width=True,
config={
'modeBarButtonsToRemove':
['lasso2d', 'select2d', 'pan2d', 'hoverClosestGeo']
})
def get_data(nrows):
data = pd.read_csv(DATA_URL,
nrows=nrows,
parse_dates=[["CRASH_DATE", "CRASH_TIME"]])
data.dropna(subset=['LATITUDE', 'LONGITUDE'], inplace=True)
lowercase = lambda x: str(x).lower()
data.rename(lowercase, axis="columns", inplace=True)
data.rename(columns={"crash_date_crash_time": "date/time"}, inplace=True)
return data
data = get_data(100000)
original_data = data
st.header("Where are the most people injured in NYC?")
injured_people = st.slider("No of people injured", 0, 19)
st.map(
data.query("injured_persons >= @injured_people")[["latitude", "longitude"
]].dropna(how="any"))
st.header("How many collisions occur during a givn time of a day?")
#hour = st.sidebar.slider("Hour to look at", 0, 23)
hour = st.slider("Hour to look at", 0, 23)
data = data[data['date/time'].dt.hour == hour]
midpoint = (np.average(data["latitude"]), np.average(data["longitude"]))
st.markdown("Vehicle collision between %i:00 and and %i:00" %
(hour, (hour + 1) % 24))
st.write(
pdk.Deck(
with row0_1:
hist_x = st.selectbox("Select a feature",
options=df.columns,
index=df.columns.get_loc("age"))
with row0_2:
bar_mode = st.selectbox("Select barmode", ["relative", "group"], 0)
with set_hist_color:
hist_color = st.selectbox(
'Select categorical color option',
["sex", 'cp', 'fbs', 'restecg', 'exang', 'slope', 'thal', 'target'], 0)
hist_bins = st.slider(label="Histogram bins",
min_value=5,
max_value=50,
value=25,
step=1,
key='h1')
# hist_cats = df['Outcome'].sort_values().unique()
hist_cats = df[hist_x].sort_values().unique()
hist_fig1 = px.histogram(df,
x=hist_x,
nbins=hist_bins,
title="Histogram of " + hist_x,
template="plotly_white",
color=f'{hist_color}',
barmode=bar_mode,
color_discrete_map=dict(noDM='green', DM='red'),
category_orders={hist_x: hist_cats})
st.write(hist_fig1)
# main.py
import streamlit as st
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
iris = datasets.load_iris() # data
# begin our app with a markdown text
st.markdown("""# Iris Cluster App
It's so easy to build Machine Learning applications!
""")
# add a slider
n = st.slider("n clusters: ", min_value=2, max_value=10, value=3)
# run model
k_means = KMeans(n_clusters=n)
cluster_labels = k_means.fit_predict(iris.data)
# visualize results
pca = PCA(n_components=2).fit_transform(iris.data) # transform to 2D
plt.scatter(pca[:, 0], pca[:, 1], c=cluster_labels)
st.pyplot() # display graph in app
for value in np.arange(0, 1, .05):
if pt >= value and pt < value + .05:
p_dict[round(value, 2)] += 1
for key in p_dict.keys():
p_dict[key] /= len(x)
return p_dict
if page == "Gambler's Ruin II":
st.title("Gambler's Ruin II")
starting = st.number_input("Enter a dollar amount", min_value=0, value=10)
win = st.number_input("Enter the number you would like to stop at",
min_value=0,
value=20)
p = st.slider("Enter the probability of winning (p)",
min_value=.01,
max_value=1.0,
value=.5)
n = st.number_input("Enter the number of matches to play",
min_value=1,
value=100)
initial = np.zeros(win + 1)
initial[starting] = 1
matrix = np.zeros((win + 1, win + 1))
for loc in range(win + 1):
if loc == 0 or loc == win:
matrix[loc, loc] = 1
else:
matrix[loc, loc + 1] = p
matrix[loc, loc - 1] = 1 - p
DATE_COLUMN = 'date/time'
DATA_URL = ('https://s3-us-west-2.amazonaws.com/'
'streamlit-demo-data/uber-raw-data-sep14.csv.gz')
@st.cache
def load_data(nrows):
data = pd.read_csv(DATA_URL, nrows=nrows)
lowercase = lambda x: str(x).lower()
data.rename(lowercase, axis='columns', inplace=True)
data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])
return data
data_load_state = st.text('loading data...')
data = load_data(1000)
data_load_state.text('loading data... done!')
st.write('Done! with cache')
if st.checkbox('show raw data'):
st.subheader('Raw data')
data
st.subheader('number of pickups per hour')
hist_values = np.histogram(data[DATE_COLUMN].dt.hour, bins=24,
range=(0, 24))[0]
st.bar_chart(hist_values)
hour_to_filter = st.slider('hour', 0, 23, 17)
st.subheader('all pickup data at {}:00'.format(hour_to_filter))
data[data[DATE_COLUMN].dt.hour == hour_to_filter]
def main():
# st.title('Data Viz')
# st.subheader('AnĆ”lise ExploratĆ³ria de Dados')
# Carregando a imagem de tĆtulo e definindo o texto do header
st.image(
'https://sc.movimentoods.org.br/wp-content/uploads/2019/10/qualirede.png',
width=400)
st.header('Um jeito simples de visualizar e analisar seus dados')
st.sidebar.title('Data Viz')
# Lendo o arquivo upado pelo usuƔrio
file = st.file_uploader('Escolha a base de dados CSV', type='csv')
# Checando se o arquivo nĆ£o estĆ” vazio
if file is not None:
# Inserindo os menus laterais
st.sidebar.image(
'https://media.giphy.com/media/1klslCB8tbUmN4QoD4/giphy.gif',
width=250)
st.sidebar.header("Selecione:")
# Checando quantas linhas do dataset o usuƔrio quer ver
columns = st.slider('Quantas linhas deseja ver?',
min_value=1,
max_value=50)
# Lendo e exibindo o dataset
st.markdown('**Seu arquivo**:')
data = pd.read_csv(file)
st.dataframe(data.head(columns))
# Menu lateral para chegar o shape do dataset
if st.sidebar.checkbox('Quero ver o shape dos meus dados'):
st.markdown('**Quantidade de linhas:** ')
st.markdown(data.shape[0])
st.markdown('**Quantidade de colunas:**')
st.markdown(data.shape[1])
#Menu lateral para chegar visualizar as colunas do dataset
if st.sidebar.checkbox('Quero analisar as colunas'):
all_columns = data.columns.tolist()
selected_columns = st.multiselect('Selecione', all_columns)
new_df = data[selected_columns].drop_duplicates(subset=None,
keep='first')
st.dataframe(new_df)
#Menu lateral para contar o nĆŗmero de variĆ”veis
if st.sidebar.checkbox('Quero contas a quantidade de target/classes'):
selected_plot = st.selectbox('Selecione o tipo de visualizaĆ§Ć£o',
list(data.columns))
st.markdown('**Contagem de Alvos/Classes**')
st.write(
data.iloc[:,
data.columns.get_loc(selected_plot)].value_counts())
#Menu lateral para ver os tipos de dados
if st.sidebar.checkbox('Quero ver os tipos dos dados'):
st.markdown('**Tipos de dados**')
st.write(data.dtypes)
#Menu lateral para ver a descriĆ§Ć£o dos dados
if st.sidebar.checkbox('Quero a descriĆ§Ć£o dos meus dados'):
st.markdown('**DescriĆ§Ć£o**')
st.write(data.describe())
#Menu lateral para selecionar o tipo de grƔfico
if st.sidebar.checkbox('Quero visualizar meus dados'):
columns_names = data.columns.tolist()
viz = ('line', 'bar', 'pie', 'hist', 'correlation', 'box')
selected_plot = st.sidebar.selectbox(
'Selecione o tipo de visualizaĆ§Ć£o', viz)
selected_columns_names = st.multiselect('Selecione as colunas',
columns_names)
# GrƔfico de linha
if selected_plot == 'line':
custom_data = data[selected_columns_names]
st.line_chart(custom_data)
# GrƔfico de barra
elif selected_plot == 'bar':
custom_data = data[selected_columns_names]
st.bar_chart(custom_data)
# GrƔfico de pizza
elif selected_plot == 'pie':
st.write(
data.iloc[:,
-1].value_counts().plot.pie(autopct="%1.1f%%"))
st.pyplot()
# GrĆ”fico de correlaĆ§Ć£o
elif selected_plot == 'correlation':
corr = data.corr()
st.write(
sns.heatmap(corr,
xticklabels=corr.columns,
yticklabels=corr.columns,
annot=True))
st.pyplot()
# Outros grƔficos
else:
custom_plot = data[selected_columns_names].plot(
kind=selected_plot)
st.pyplot()
# Menu sobre mim
#if st.sidebar.checkbox('Sobre'):
html = """
<br><br><br><br><br>
<div>
Desenvolvido pela equipe <a href="mailto:[email protected]">NGI</a>,
na <a href="https://www.qualirede.com.br/">Qualirede</a>.
</div></b>
"""
st.markdown(html, unsafe_allow_html=True)
def presentation():
st.markdown("## 2.2 Mode Choice Model")
# Show Probability Matrix
if st.checkbox("Show Probability Matrixes"):
st.markdown("Probability of choosing mode given origin and destination")
probabilities = mp.get_probability()
st.markdown("Probability for **CAR**")
st.write(pd.DataFrame(probabilities[0], index=["Zone 1", "Zone 2"], columns=["Zone 1", "Zone 2"]))
st.markdown("Probability for **PUBLIC TRANSPORT (PT)**")
st.write(pd.DataFrame(probabilities[1], index=["Zone 1", "Zone 2"], columns=["Zone 1", "Zone 2"]))
st.markdown("Probability for **SLOW**")
st.write(pd.DataFrame(probabilities[2], index=["Zone 1", "Zone 2"], columns=["Zone 1", "Zone 2"]))
# ------------------------- Write Up for the section ------------------------- #
# ---------------------------------------------------------------------------- #
st.markdown("""
## Mode Choice Model
The model calculates the probability of each alternative mode of transport given the origin and destination.
Hence, we calculate **$Pr(m|i,j)$**, where $m$ is mode and $i$ and $j$ are the origin and destination zones.
""")
st.write("""
Assuming the utility of choosing the alternative is given by,
$$
U_{j,m}^i = V_{j,m}^i + \epsilon_{j,m}^i
$$
where, $\epsilon_{j,m}^i$ is I.I.D Gumble distributed error term representing uncertinity.
""")
st.write("""
The probability that the utility for alternative $m$ given $i$ and $j$ is given by,
$$
Pr(m|i,j) = Pr(U_{j,m}^i > U_{j,m'}^i) = \dfrac{\exp^{V_{j,m}^i}}{ \sum_{m'={1,2,3}} \exp^{V_{j,m'}^i} }
$$
""")
# ------------------------------------ EXERCISES ------------------------------#
# ---------------------------------------------------------------------------- #
st.markdown("## EXERCISES")
# ------------------------------------ Ques2 ------------------------------#
if st.checkbox("(ii) Probabilites of each zone given mode"):
orig_zone = st.radio("Select Origin Zone", list(ZONE.keys()))
dest_zone = st.radio("Select Destination Zone", list(ZONE.keys()))
mode = st.radio("Select Mode", list(MODE.keys()), key=1)
probability = mp.get_probability(ZONE[orig_zone], ZONE[dest_zone], MODE[mode])
st.markdown("The probability of travelling from **{}** to **{}** using a **{}** is,".format(orig_zone, dest_zone, mode))
st.markdown("$P(m|i,j)$ = **{}**".format(round(probability, 4)))
# ------------------------------------ Ques3 ------------------------------#
if st.checkbox("(iii) Probability of travelling using choosen mode from zone 1 to any destination"):
# Explanation
st.write("""
Using the law of large numbers,
$$
Pr(m|i) = \sum_{j={1,2}} Pr(m|i,j)*P(j)
$$
where, $P(j)$ is 0.5 for each zone and $i = 1$
""")
# Answer
mode = st.radio("Select Mode", list(MODE.keys()), key=2)
zone11 = mp.get_probability(ZONE["Zone 1"], ZONE["Zone 1"], MODE[mode])
zone12 = mp.get_probability(ZONE["Zone 1"], ZONE["Zone 2"], MODE[mode])
st.markdown("The probability of travelling from **Zone 1** using **{}** is,".format(mode))
st.markdown("$P(m|i=1)$ = **{}**".format(round(zone11*0.5 + zone12*0.5, 4)))
# ------------------------------------ Ques4 ------------------------------#
if st.checkbox("(iv) Effect on probabilites choosing u_mode"):
# Explanation
st.markdown("""
Let us assume that the utility function is given by,
$$
U_{j,m}^i = V_{j,m}^i + \mu_{mode}*\epsilon_{j,m}^i
$$
where, $\mu_{mode}$ is a scaling factor for error term
""")
st.markdown("""
As value for $\mu_{mode}$ increases, uncertinity in the model increases. Hence the probabilties for each mode becomes more and more uncertain.
As value for $\mu_{mode}$ decreases, we are more sure of the deterministic values. Hence the probabilties for each mode becomes more and more certain.
""")
# Answer
u_mode = st.slider("Select u_mode value", min_value=float(0.1), max_value=float(10), value=float(1), step=float(0.1))
# Plotting
umode_prob = mp.get_probability(u_mode=u_mode)
index = ["Car", "Pt", "Slow"]
zone11= [umode_prob[i][0][0] for i in range(3)]
zone12= [umode_prob[i][0][1] for i in range(3)]
zone21= [umode_prob[i][1][0] for i in range(3)]
zone22= [umode_prob[i][1][1] for i in range(3)]
fig = go.Figure(data=[
go.Bar(name="Zone 11", x=index, y=zone11),
go.Bar(name="Zone 12", x=index, y=zone12),
go.Bar(name="Zone 21", x=index, y=zone21),
go.Bar(name="Zone 22", x=index, y=zone22)])
fig.update_layout(barmode='group',
title="Mode Choice probabilities for u_mode: {}".format(u_mode),
title_font_size=20)
fig.update_yaxes(range=[0, 1], title_text='Probability')
st.plotly_chart(fig)
# ------------------------------------ Ques5 ------------------------------#
if st.checkbox("(v) Effect on probabilites changing number of employees"):
# Explanation
st.markdown("""
Due to **Equivalent Difference Property**, any change in number of employee does not affect the probablities.
""")
# Answer
zone1_emp = st.slider("Change for Zone 1 employee", min_value=int(-5000), max_value=int(5000), value=int(0), step=int(1000))
zone2_emp = st.slider("Change for Zone 2 employee", min_value=int(-5000), max_value=int(5000), value=int(0), step=int(1000))
# Plotting
emp_prob = mp.get_probability(emp_change_z1=zone1_emp, emp_change_z2=zone2_emp)
index = ["Car", "Pt", "Slow"]
zone11= [emp_prob[i][0][0] for i in range(3)]
zone12= [emp_prob[i][0][1] for i in range(3)]
zone21= [emp_prob[i][1][0] for i in range(3)]
zone22= [emp_prob[i][1][1] for i in range(3)]
fig = go.Figure(data=[
go.Bar(name="Zone 11", x=index, y=zone11),
go.Bar(name="Zone 12", x=index, y=zone12),
go.Bar(name="Zone 21", x=index, y=zone21),
go.Bar(name="Zone 22", x=index, y=zone22)])
fig.update_layout(barmode='group',
title="Number of Employees - Zone 1: {} Zone 2: {}".format(10_000+zone1_emp, 15_000+zone2_emp),
title_font_size=20)
fig.update_yaxes(range=[0, 1], title_text='Probability')
st.plotly_chart(fig)
@st.cache(persist=True)
def load_data(nrows):
data = pd.read_csv(DATA_URL,
nrows=nrows,
parse_dates=[['CRASH_DATE', 'CRASH_TIME']])
data.dropna(subset=['LATITUDE', 'LONGITUDE'], inplace=True)
lowercase = lambda x: str(x).lower()
data.rename(lowercase, axis="columns", inplace=True)
data.rename(columns={"crash_date_crash_time": "date/time"}, inplace=True)
return data
data = load_data(100000)
st.header("Where are the most people injured in NYC?")
injured_people = st.slider("Number of persons injured in vehicle collisions",
0, 19)
st.map(
data.query("injured_persons >= @injured_people")[["latitude", "longitude"
]].dropna(how="any"))
st.header("How many collisions occur during a given time of day?")
hour = st.slider("Hour to look at", 0, 23)
original_data = data
data = data[data['date/time'].dt.hour == hour]
st.markdown("Vehicle collisions between %i:00 and %i:00" % (hour,
(hour + 1) % 24))
midpoint = (np.average(data["latitude"]), np.average(data["longitude"]))
st.write(
pdk.Deck(
st.pyplot()
#Drop the columns selected from the multiselect
#Select multiple columns
cols_drop = st.multiselect("Select columns to drop", data.columns)
#Drop columns
data = data.drop(cols_drop, axis=1)
#Show the new dataframe
#Text to show it's the new dataframe
st.text("Once features have been engineered, it looks like this:")
#Show the new dataframe
st.dataframe(data)
#Change the number of clusters you want the data to be broken up into
k = st.slider("Select the number of clusters", 2, 10)
#A button that clusters when you press it
if st.button("Cluster Results"):
#Dropping the diagnosis column so that the features can be used to predict it
X = data.drop('diagnosis', axis=1).values
#Scaling the values of the dataframe
X = StandardScaler().fit_transform(X)
#Creating a KMeans clusterer called km
km = KMeans(n_clusters=k, init="k-means++", n_init=10)
#Passing our transformed dataframe into our KMeans clusterer
km_pred = km.fit_predict(X)
#Plotting the data
###########################
#### Cluster 1 - Model ####
###########################
st.markdown('---')
st.markdown('## **Regional Cluster 1 - Forecast:**')
st.markdown("""**Cluster Characteristics:**
- Lowest average regional temperature: ($49^oF$)
- Low Avg. Consumer Price Index (CPI): $133$
- Highest Sales Cluster
- Highest Regional Population
""")
clust1_model_steps = st.slider('Forecast Range (Weeks):',
1,
51,
12,
key='slider1')
clust1_model_pred = np.exp(
clust1_model.predict(start=cluster_1_test.index[0],
end=cluster_1_test.index[0] + clust1_model_steps,
exog=test_plus_future_holidays[:clust1_model_steps +
1],
dynamic=True,
plot_insample=False))
clust_1_model_ci = np.exp(clust1_model.get_forecast(steps=clust1_model_steps+1,
exog=test_plus_future_holidays[:clust1_model_steps+1])\
.conf_int(alpha=0.05))
def main():
# -----------------------------
# Initial description
st.image('./dataset-cover.png', width=900)
st.title('Analysis of e-commerce dataset')
st.markdown("""Here, I will introduce some basic descriptive analysis of
the Olist brazilian e-commerce dataset. This dataset contains
more than 110K orders from 2016 to 2018 with detailed costumer
transactions. The dataset consists of **9 files**, which
describe orders, products and their categories, user reviews,
information about delivery estimate date, payment method,
geolocation, and much, much more. \n """)
st.markdown("""All datasets are available
on [Kaggle](https://www.kaggle.com/olistbr/brazilian-ecommerce)
.""")
st.markdown("""Initially, I will start using **4 datasets**:
*Items, Orders, Products description and Payment.*""")
st.markdown("Here, I will cover some basic info about the datasets "
"(number of orders, how many variables are available, possible"
" payment methods, etc.) and explore some data visualization"
" (histogram, bar plot, and boxplot). \n So, let's start!")
# -----------------------------
# Import dataset
st.header("**Dataset investigation**")
st.markdown("Let's take a look on data...")
items = pd.read_csv("./olist_dataset/olist_order_items_dataset.csv")
orders = pd.read_csv("./olist_dataset/olist_orders_dataset.csv")
products = pd.read_csv("./olist_dataset/olist_products_dataset.csv")
payment = pd.read_csv("./olist_dataset/olist_order_payments_dataset.csv")
# Slider for dataframe.head
slider_bar = st.slider(label='Select a number of rows to take a look on datasets?',
min_value=1, max_value=10)
st.markdown('**Items**')
st.markdown("""This dataset describes the relationship among orders, sellers,
order price, shipping cost and date.""")
st.dataframe(items.head(slider_bar))
st.markdown('**Orders **')
st.markdown("""Here, We have info about order status: purchased time,
order status, if was already shipped, for example...""")
st.dataframe(orders.head(slider_bar))
st.markdown('**Products**')
st.markdown("""Products dataset describes products by categories, weight,
and dimensions""")
st.dataframe(products.head(slider_bar))
st.markdown('**Payment**')
st.markdown("""Last but not least, this dataset shows payment value and
method.""")
st.dataframe(payment.head(slider_bar))
# Columns description
add_info = st.checkbox('Want additional info about variables?')
if add_info:
st.markdown("""Basically, keep in mind that with these variables bellow,
we can track orders along the datasets: \n"""
"* order_id: identify products that are in the same basket. \n"
"* product_id: identify unique products within the dataset. \n"
"* customerid: identify unique customers within the dataset. \n"
"* seller_id: identify unique sellers within the dataset. \n ")
# -----------------------------
# Dataset shape
st.header("**Common questions**")
cols_box = st.checkbox("How many columns and rows are in datasets?")
if cols_box:
st.markdown(f"Items: {items.shape}")
st.markdown(f"Orders: {orders.shape}")
st.markdown(f"Products: {products.shape}")
st.markdown(f"Payments: {payment.shape}")
order_box = st.checkbox("How many orders are in Order dataset?")
if order_box:
st.markdown(f"""Total number of orders:
{orders['order_id'].nunique()}""")
order_customers = st.checkbox("How many customers are?")
if order_customers:
st.markdown(f"""Total number of customers:
{orders['order_id'].nunique()}""")
products_box = st.checkbox("What are the product categories?")
if products_box:
products_categories = products['product_category_name'].unique().tolist()
st.write(f'There are {len(products_categories)} categories.')
st.dataframe(products_categories)
payment_type = st.checkbox("""What are the different payment
methods?""")
if payment_type:
st.markdown(f"""There are {payment['payment_type'].nunique()}
payment options:""")
st.dataframe(payment['payment_type'].unique())
order_customers = st.checkbox("""What are the possible delievery status
on sales orders?""")
if order_customers:
order_status = orders['order_status'].unique().tolist()
st.dataframe(order_status)
# -----------------------------
# Merging dataset ---- add code to streamlit
st.header("**Descriptive analysis**")
st.markdown("""First, let's merge our datasets:""")
orders_items = pd.merge(orders, items, on='order_id')
products_slice = products.drop(['product_name_lenght',
'product_description_lenght'],
axis='columns')
merge_df = pd.merge(orders_items, products_slice, on='product_id')
merge_df = pd.merge(merge_df, payment, on='order_id')
# -----------------------------
# Select columns to .describe()
st.markdown("""A common task is to extract basic information about the
dataset, as the maximum and minimum value per variable,
find the mean, quantiles, etc. So, you can choose some
columns to investigate it.""")
cols = ['price', 'freight_value', 'product_weight_g', 'product_length_cm',
'product_photos_qty', 'product_length_cm', 'product_height_cm',
'product_width_cm', 'payment_installments', 'payment_value']
columns_box = st.multiselect("""Select columns to calculate max, min,
mean, median and quantiles""", cols)
if columns_box:
df_columns_box = merge_df[columns_box]
st.dataframe(df_columns_box.describe().T)
# -----------------------------
# Missing data
st.markdown("""Another important task is to check types present on the
dataset and if exist any missing values. Keep in mind that
it is important to handle this effectively, because missing
values can impact our interpretation.""")
missing = pd.DataFrame({'missing count': merge_df.isnull().sum(),
'dtype': merge_df.dtypes,
'missing %': (merge_df.isnull().sum()/merge_df.shape[0])*100})
st.dataframe(missing.head(25))
# Filling missing data
missing_box = st.checkbox("Do you want to fill missing data?")
if missing_box:
st.markdown("""Great, since we have variables with different
types, let's focus focus on numeric types""")
# Filling in numeric columns
missing_op = st.selectbox('How do you want to fill missing values',
('Mean', '0'))
if missing_op == '0':
st.markdown('')
numeric_cols = ['product_photos_qty', 'product_weight_g',
'product_length_cm', 'product_height_cm',
'product_width_cm']
for col in numeric_cols:
merge_df[col] = merge_df[col].fillna(value=0)
if missing_op == 'Mean':
st.markdown('')
numeric_cols = ['product_photos_qty', 'product_weight_g',
'product_length_cm', 'product_height_cm',
'product_width_cm']
for col in numeric_cols:
merge_df[col] = merge_df[col].fillna(value=merge_df[col].mean())
# Filling in objetc columns
st.markdown("""Also, Let's handle columns which have
object' types:""")
missing_obj = st.selectbox("""Do you want to drop rows with missing
data or ignore it?""", ('Drop', 'Ignore'))
if missing_obj == 'Drop':
st.markdown('Sorry, this feature is under construction :(')
merge_df['product_category_name'] = merge_df['product_category_name'].fillna(value='no_info')
# object_cols = ['order_approved_at', 'order_delivered_carrier_date', 'order_delivered_customer_date']
# merge_df = merge_df[merge_df[object_cols].notna()]
if missing_obj == 'Ignore':
st.markdown('')
# Recheck missing data
missing_box2 = st.checkbox("Want to check the result?")
if missing_box2:
missing2 = pd.DataFrame({'missing count': merge_df.isnull().sum(),
'dtype': merge_df.dtypes,
'missing %': (merge_df.isnull().sum()/merge_df.shape[0])*100})
st.dataframe(missing2.head(25))
# -----------------------------
# Data visualization
st.header("""**Visualization**""")
st.markdown("""Data visualization it's an important task on data analysis,
which allows extracting interesting patterns from data and,
making it easier to understand. So, Let's start plotting""")
# Histogram - columns
st.subheader("Histogram")
numeric_cols = ['price', 'product_photos_qty', 'product_weight_g',
'freight_value', 'product_length_cm', 'product_height_cm',
'product_width_cm', 'payment_value', 'payment_installments']
hist_col = st.selectbox('What column do you want to create a histogram?:',
numeric_cols)
if hist_col:
fig_hist = px.histogram(merge_df, x=hist_col)
st.write(fig_hist)
# Boxplot - columns
st.subheader("Boxplot")
boxplot_col = st.multiselect("""What column do you want to create
boxplot?""", numeric_cols)
if boxplot_col:
fig_boxplot = px.box(merge_df, x=boxplot_col)
st.write(fig_boxplot)
# Barplot - Products Ordered
st.subheader("How many products people generally order?")
number_orders = merge_df.groupby('order_id')['order_item_id'].aggregate('sum').reset_index()
number_orders = number_orders['order_item_id'].value_counts()
number_orders.index += 1
fig_bar = px.bar(number_orders, x=number_orders.index, y=number_orders.values)
st.write(fig_bar)
# Barplot - Most bought products
st.subheader("**Which categories people buy at most?**")
categories_prods = merge_df.groupby('product_category_name').count().reset_index().sort_values('order_id')
fig_bar_p = px.bar(categories_prods, y='product_category_name', x='order_id',
orientation='h')
st.write(fig_bar_p)
# Money spent
# Barplot - Payment methods
st.subheader("What is the most common payment method?")
pay_type = merge_df.groupby('payment_type')['order_id'].count().reset_index()
pay_type = pay_type.sort_values(by='order_id', ascending=False)
pay_type = pay_type.rename(columns={'order_id': 'value_count'})
fig_pay = px.bar(pay_type, y='value_count', x='payment_type',
orientation='v')
st.write(fig_pay)
# -----------------------------
# The end!
st.header("That's all folks!")
# st.balloons()
st.markdown("""This work was developed using these excellent Kaggle repositories
[A] (https://www.kaggle.com/gsdeepakkumar/e-commerce-dataset-analysis/notebook),
[B] (https://www.kaggle.com/kabure/simple-eda-sales-and-customer-patterns/notebook).
\n So, if you want to dive in this dataset, you totally should check them.""")
st.markdown("""Thank you so much for checking my job! If you liked, please,
check my [github]
(https://github.com/cavalcante-l?tab=repositories)
and my [linkedin]
(https://www.linkedin.com/in/laizacavalcante/). """)
st.markdown("Developed by LaĆza Cavalcante.")
def write():
st.markdown("""
# SugarTime
### Model Performance
This page lets you visualize how the model performs on data that
it hasn't seen yet.
""")
with st.beta_expander("CLICK HERE to expand discussion"):
st.markdown("""
The dataset is split into two sets: a training set and a
testing set. The model has been trained on the training set, and
we can use the model to perform inference on data from the
testing set here.
The time series model is auto-regressive with exogenous variables
(ARX). The base algorithm used in such a model can be any
regression algorithm; here I currently use a support vector
machine.
The full model actually consists of several models, each
individually
fit to a different lag of the target variable. In other words,
there
is one model fit to the glucose data at time *t+1*, another fit to
the
glucose data at time *t+2*, another at *t+3*, etc.,
all the way up to the
selected horizon of the model (which defaults to 12 steps of 5
minutes
each, i.e., one hour). Each model represents the best performing
model
after optimizing the time-series design hyperparameters (e.g.,
order of
the *endogenous* or *target* variable, order of the *exogenous*
variables, and/or delay of the exogenous variables) at that time
step.
Note that this model has essentially learned to revert to the mean.
Since there is considerable autocorrelation in data from continuous
glucose monitors, inference becomes less acurrate as the inference
step gets farther away from the current time *t*.
Here, instead of relying on the exogenous variables (i.e.,
carbohydrates and insulin),
the model does a better job by increasingly bringing the predicted
value back to the mean, which for this patient is a blood glucose
level of approximately 100 mg/dL.
This is obviously not what we want the model to learn. But I have
yet
to find an estimator/algorithm that doesn't converge on this
strategy
to some extent, which suggests that these two exogenous variables
are simply not predictive enough to account for significant
variance beyond the autoregressive component of this model.
""")
st.markdown("""
*Instructions:*
Use the slider to select a time within the test set. The model
will use the data up to that point to generate a forecast for
the next hour.
***
""")
st.markdown("# Select date/time to show forecast.")
# load patient data and fit model
vm = load_saved_model()
patient = vm.patient
# make datetime selection slider
x_index = patient.Xtest.index
start_time = st.slider(
"Move the slider to select the forecast date/time",
min_value=x_index[40].to_pydatetime(),
max_value=x_index[-40].to_pydatetime(),
value=x_index[45].to_pydatetime(),
step=timedelta(minutes=60),
format="MM/DD/YY - hh:mm",
)
# plot glucose values for the test set
fig = plot_test_set(patient, start_time)
st.plotly_chart(fig)
# plot performance of model
st.markdown("# Show forecast vs actual")
start_time_index = (x_index == pd.Timestamp(start_time)).argmax()
nsteps = vm.horizon
ypred = vm.multioutput_forecast(patient.Xtest[:start_time_index],
patient.ytest[:start_time_index])
idx = pd.date_range(
start=start_time,
end=start_time + timedelta(minutes=5 * (len(ypred) - 1)),
freq="5T",
)
ypred = pd.DataFrame(ypred, columns=["ypred"], index=idx)
fig = core.plot_forecast(
patient.ytest[(start_time_index - 40):(start_time_index + nsteps)],
ypred,
return_flag=True,
)
start_time_text = datetime.datetime.strftime(start_time, "%m/%d/%m %H:%M")
fig.update_layout(
title={
"text": "start time: " + start_time_text,
"y": 0.88,
"x": 0.5,
"xanchor": "center",
"yanchor": "top",
})
st.plotly_chart(fig)
def show_results():
st.write(results_text)
# MAE graph
image_mae = Image.open(str(main_path / 'Results/image/mae.png'))
st.image(image_mae, caption='Figure 1: Mean Absolute Error')
# RMSE graph
image_rmse = Image.open(str(main_path / 'Results/image/rmse.png'))
st.image(image_rmse, caption='Figure 2: Root Mean Square Error')
# R Square praph
image_r_square = Image.open(str(main_path / 'Results/image/r_square.png'))
st.image(image_r_square, caption='Figure 3: R Square')
# Random Forest slider graph
st.write("Random Forest model performance:")
year_rf = st.slider('Number of years after publication?', 1, 10)
year_image = 'Results/image/' + rf_images[year_rf]
rf_image = Image.open(str(main_path / year_image))
st.image(rf_image,
caption='Figure 4: Random Forest Citation Frequency Histogram',
width=600)
# Random Forest performance table
st.write(
"The following are the performance matrix for Random Forest Regression model:"
)
performance_df = pd.DataFrame(np.array(
[[1.168999, 1.89990, 2.66408, 3.43690, 4.19492, 5.62216, 6.94813],
[
43.38860, 71.76869, 124.98958, 216.97061, 356.03481, 86.33918,
1421.48745
],
[6.58700, 8.47164, 11.17987, 14.72992, 18.86888, 28.04174, 37.70262],
[0.16119, 0.22493, 0.27972, 0.303861, 0.30706, 0.29510, 0.22525]]),
columns=[
'1 year', '2 year', '3 year', '4 year',
'5 year', '7 year', '10 year'
])
performance_df['index'] = ["MAE", "MSE", "RMSE", "R Square"]
performance_df = performance_df.set_index("index")
st.table(performance_df)
# Benchmark Models slider graphs
st.write("Benchmark model performance:")
year_lr = st.slider('Number of years after publication?', 0, 10)
col1, col2, col3 = st.beta_columns(3)
with col1:
lr_year_image = 'Results/image/lr/' + lr_images[year_lr]
lr_image = Image.open(str(main_path / lr_year_image))
st.image(lr_image, caption='Figure 5: LR Citation Frequency Histogram')
with col2:
svm_year_image = 'Results/image/svm/' + svm_images[year_lr]
svm_image = Image.open(str(main_path / svm_year_image))
st.image(svm_image,
caption='Figure 6: SVM Citation Frequency Histogram')
with col3:
km_year_image = 'Results/image/kmeans/' + km_images[year_lr]
km_image = Image.open(str(main_path / km_year_image))
st.image(km_image,
caption='Figure 7: K-Means Citation Frequency Histogram')
def in_bounding_box(point):
lng, lat = point
in_lng_bounds = DOWNTOWN_BOUNDING_BOX[0] <= lng <= DOWNTOWN_BOUNDING_BOX[2]
in_lat_bounds = DOWNTOWN_BOUNDING_BOX[1] <= lat <= DOWNTOWN_BOUNDING_BOX[3]
return in_lng_bounds and in_lat_bounds
df = pd.read_csv(DATA_URL)
# Filter to bounding box
df = df[df['LAT_DESTINO'] < -33]
st.write(df)
df = df[df['NUM_EST'] < 23]
num_est = st.slider("Cantidad de estudiantes traspasados entre colegios", 1,
30)
GREEN_RGB = [0, 255, 0, 40]
RED_RGB = [240, 100, 0, 40]
arc_layer = pydeck.Layer(
"ArcLayer",
data=df.query("NUM_EST >= @num_est"),
get_width="NUM_EST * 2",
get_source_position=["LON_ORIGEN", "LAT_ORIGEN"],
get_target_position=["LON_DESTINO", "LAT_DESTINO"],
get_tilt=15,
get_source_color=RED_RGB,
get_target_color=GREEN_RGB,
pickable=True,
auto_highlight=True,
)
