def _update_histo(
virus_name,
mumps,
dengue,
lassa,
avian_opt1,
avian_opt2,
flu_opt1,
flu_opt2,
flu_opt3,
id_year,
):
virus_name = virus_name.lower()
if virus_name == "ebola" or virus_name == "zika" or virus_name == "measles":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1="", level2="", level3="")
elif virus_name == "mumps":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1=mumps, level2="", level3="")
elif virus_name == "dengue":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1=dengue, level2="", level3="")
elif virus_name == "lassa":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1=lassa, level2="", level3="")
elif virus_name == "avian":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name,
level1=avian_opt1,
level2=avian_opt2,
level3="")
elif virus_name == "flu":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name,
level1=flu_opt1,
level2=flu_opt2,
level3=flu_opt3)
df = pd.read_csv(metadata_file_stat_filtred)
min_date, max_date = id_year
# To select only the data between min_date and max_date
df = df[df["Year"] >= min_date]
df = df[df["Year"] <= max_date]
# Count the number of viruses by Country
df_group_by_country = df.groupby(["Country"])["Value"].sum()
# Translate groupby object in dataframe
df_group_by_country = df_group_by_country.to_frame()
# Rename the first column in Value
df_group_by_country.columns = ["Value"]
# Move the index values (i.e. Country column) in column and reset index
df_group_by_country = df_group_by_country.reset_index()
return {
"data": [{
"x": df_group_by_country["Country"],
"y": df_group_by_country["Value"],
"type": "bar",
"marker": dict(color="#8380ff"),
}],
"layout": {
"autosize":
True,
"margin":
dict(l=30, r=30, b=70, t=40),
"title":
"<br>Distribution of {} <br>Between {} and {}".format(
virus_name.title(), min_date, max_date),
"font":
dict(family="Open Sans"),
},
}
def _update_curve(
virus_name,
mumps,
dengue,
lassa,
avian_opt1,
avian_opt2,
flu_opt1,
flu_opt2,
flu_opt3,
id_year,
):
virus_name = virus_name.lower()
if virus_name == "ebola" or virus_name == "zika" or virus_name == "measles":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1="", level2="", level3="")
elif virus_name == "mumps":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1=mumps, level2="", level3="")
elif virus_name == "dengue":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1=dengue, level2="", level3="")
elif virus_name == "lassa":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1=lassa, level2="", level3="")
elif virus_name == "avian":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name,
level1=avian_opt1,
level2=avian_opt2,
level3="")
elif virus_name == "flu":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name,
level1=flu_opt1,
level2=flu_opt2,
level3=flu_opt3)
df = pd.read_csv(metadata_file_stat_filtred)
min_date, max_date = id_year
# To select only the data between min_date and max_date
df = df[df["Year"] >= min_date]
df = df[df["Year"] <= max_date]
return create_curve_line(df, virus_name, min_date, max_date)
def update_phylogeny_tree(
virus_name,
mumps,
dengue,
lassa,
avian_opt1,
avian_opt2,
flu_opt1,
flu_opt2,
flu_opt3,
):
virus_name = virus_name.lower()
ord_by_elt = "Country"
if virus_name == "ebola" or virus_name == "zika" or virus_name == "measles":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1="", level2="", level3="")
elif virus_name == "mumps":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1=mumps, level2="", level3="")
elif virus_name == "dengue":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat,
) = create_paths_file(virus_name, level1=dengue, level2="", level3="")
elif virus_name == "lassa":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1=lassa, level2="", level3="")
elif virus_name == "avian":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name,
level1=avian_opt1,
level2=avian_opt2,
level3="")
elif virus_name == "flu":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name,
level1=flu_opt1,
level2=flu_opt2,
level3=flu_opt3)
if tree_file_filtred in tree_fig:
fig = tree_fig[tree_file_filtred]
else:
if ord_by_elt == "Country" or ord_by_elt == "Division" or ord_by_elt == "Date":
fig = create_tree(virus_name, tree_file_filtred,
metadata_file_filtred, ord_by_elt)
tree_fig[tree_file_filtred] = fig
return fig
def _update_slicer(
virus_name,
mumps,
dengue,
lassa,
avian_opt1,
avian_opt2,
flu_opt1,
flu_opt2,
flu_opt3,
):
virus_name = virus_name.lower()
if virus_name == "ebola" or virus_name == "zika" or virus_name == "measles":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1="", level2="", level3="")
elif virus_name == "mumps":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1=mumps, level2="", level3="")
elif virus_name == "dengue":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1=dengue, level2="", level3="")
elif virus_name == "lassa":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name, level1=lassa, level2="", level3="")
elif virus_name == "avian":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name,
level1=avian_opt1,
level2=avian_opt2,
level3="")
elif virus_name == "flu":
(
tree_file_filtred,
metadata_file_filtred,
metadata_file_stat_filtred,
) = create_paths_file(virus_name,
level1=flu_opt1,
level2=flu_opt2,
level3=flu_opt3)
df = pd.read_csv(metadata_file_stat_filtred)
min_date, max_date = min_max_date(df)
# create the dictionary of slider
marks_data = slicer(min_date, max_date)
min_max_date_value = [min_date, max_date]
# To select only the data between min_date and max_date
df = df[df["Year"] >= min_date]
df = df[df["Year"] <= max_date]
return dcc.RangeSlider(
id="id-year",
min=min_date,
max=max_date,
step=1,
marks=marks_data,
value=min_max_date_value,
)
slicer,
create_tree,
create_map_bubble_year,
create_curve_line,
)
app = dash.Dash(__name__)
server = app.server
virus_name = "measles"
species = ["Avian", "Ebola", "Lassa", "Measles", "Mumps", "Zika"]
tree_fig = {}
mapbox_access_token = "pk.eyJ1IjoicGxvdGx5bWFwYm94IiwiYSI6ImNrOWJqb2F4djBnMjEzbG50amg0dnJieG4ifQ.Zme1-Uzoi75IaFbieBDl3A"
tree_file, metadata_file, metadata_file_stat = create_paths_file(virus_name,
level1="",
level2="",
level3="")
# To know the minimum and the maximum values of date for slicer
df_stat_metadata = pd.read_csv(metadata_file_stat)
min_date, max_date = min_max_date(df_stat_metadata)
# create the dictionary of slider
marks_data = slicer(min_date, max_date)
min_max_date_value = [min_date, max_date]
fig = create_tree(virus_name, tree_file, metadata_file, "Country")
tree_fig[tree_file] = fig
fig_map_bubble = create_map_bubble_year(virus_name, metadata_file_stat, 2,
min_date, max_date)
