def __init__(self):
self.df_fra, self.df_fra_2, self.df_fra_backup = df_fct.import_df(
['Fra_GenData', 'Fra_GenData2', 'Fra_Backup'],
['raw', 'raw', 'raw'])
self.df_fra_test = df_fct.import_df(['Fra_Testing2'], ['raw'])
self.df_dpt_shp = df_fct.import_df(['Fra_Departements_shapefile'],
['raw'])[0]
self.df_fra_nat = pandas.DataFrame()
self.df_fra_reg = pandas.DataFrame()
self.df_fra_dpt = pandas.DataFrame()
def __init__(self):
variables = [
'Fra_GenData', 'Fra_Regions_shapefile',
'Fra_Departements_shapefile', 'Fra_pop', 'Dept_reg'
]
self.df_fra_reg = df_fct.import_df(['Fra_Reg'], ['processed'])[0]
list_prop_import = ['raw', 'raw', 'raw', 'raw', 'raw']
for a_var, a_prop in zip(variables, list_prop_import):
setattr(self, a_var, df_fct.import_df([a_var], [a_prop])[0])
self.df_fra = pandas.DataFrame()
self.df_fra_dpt = pandas.DataFrame()
def __init__(self, prop_df, off_sets):
self.world_df = df_fct.import_df(['World_JH'], ['processed'])[0]
self.prop_df = prop_df
self.result_reg = pandas.DataFrame(index=prop_df.index,
columns=['df_cases', 'df_death'])
self.off_sets = off_sets
self.list_y = []
self.list_x_fit = []
def __init__(self):
variables = ['World_JH', 'Countries_LatLong', 'World_pop']
list_prop_import = ['processed', 'raw', 'raw']
for a_var, a_prop in zip(variables, list_prop_import):
setattr(self, a_var, df_fct.import_df([a_var], [a_prop])[0])
self.world_shpe = gpd.read_file(
gpd.datasets.get_path('naturalearth_lowres'))
def __init__(self):
variables = [
'US_JH_cases', 'US_JH_death', 'US_States_shapefile', 'US_pop'
]
list_prop_import = ['raw', 'raw', 'raw', 'raw']
for a_var, a_prop in zip(variables, list_prop_import):
setattr(self, a_var, df_fct.import_df([a_var], [a_prop])[0])
self.df_us = pandas.DataFrame()
def __init__(self, style_cycle, intv, fig_size, plotting_dates, df_title):
self.data_vax = df_fct.import_df(['Fra_Vax'],['processed'])[0]
self.style_cycle = style_cycle
self.intv = intv
self.plotting_dates = [pandas.to_datetime(plotting_dates[0])]
self.fig_size = fig_size
self.df_title = df_title
self.data_vax.sort_index(level=['nom','date'], inplace=True)
if plotting_dates[1] == 'last':
self.plotting_dates.append(self.data_vax.index.get_level_values('date').unique()[-2])
else:
self.plotting_dates.append(pandas.to_datetime(plotting_dates[0]))
def __init__(self, cycle, list_countries, plotting_dates, intv, fig_size):
self.world_df = df_fct.import_df(['World_JH'], ['processed'])[0]
self.cycle = cycle
self.list_countries = list_countries
self.plotting_dates = [pandas.to_datetime(plotting_dates[0])]
self.intv = intv
self.root = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
self.fig_size = fig_size
if plotting_dates[1] == 'last':
self.plotting_dates.append(
self.world_df.index.get_level_values('date').unique()[-1])
else:
self.plotting_dates.append(pandas.to_datetime(plotting_dates[0]))
def __init__(self, intv, fig_size, plotting_dates, style_cycle, df_title,
para_to_plot):
self.french_df = df_fct.import_df(['Fra_Nat'], ['processed'])[0]
self.intv = intv
self.style_cycle = style_cycle
self.root = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
self.df_title = df_title
self.para_to_plot = para_to_plot
self.plotting_dates = [pandas.to_datetime(plotting_dates[0])]
self.fig_size = fig_size
if plotting_dates[1] == 'last':
self.plotting_dates.append(
self.french_df.index.get_level_values('date').unique()[-1])
else:
self.plotting_dates.append(pandas.to_datetime(plotting_dates[0]))
def __init__(self, style_cycle, intv, fig_size, country, name_df,
plotting_dates, to_plot, df_title, plots_titles, data_source):
self.style_cycle = style_cycle
self.intv = intv
self.country = country
self.df = df_fct.import_df([name_df], ['processed'])[0]
self.to_plot = to_plot
self.df_title = df_title
self.plots_titles = plots_titles
self.data_source = data_source
self.fig_size = fig_size
self.plotting_dates = [pandas.to_datetime(plotting_dates[0])]
if plotting_dates[1] == 'last':
self.plotting_dates.append(
self.df.index.get_level_values('date').unique()[-2])
else:
self.plotting_dates.append(pandas.to_datetime(plotting_dates[0]))
def __init__(self, intv, fig_size, style_cycle, df, cut_off, idx_names,
sort_by, drop, per_graph, para_to_plot, plotting_dates,
name_plot, title_graph):
self.intv = intv
self.style_cycle = style_cycle
self.data_df = df_fct.import_df([df], ['processed'])[0]
self.cut_off = cut_off
self.idx_names = idx_names
self.sort_by = sort_by
self.drop = drop
self.per_graph = per_graph
self.para_to_plot = para_to_plot
self.plotting_dates = [pandas.to_datetime(plotting_dates[0])]
self.name_plot = name_plot
self.title_graph = title_graph
self.fig_size = fig_size
if plotting_dates[1] == 'last':
self.plotting_dates.append(
self.data_df.index.get_level_values('date').unique()[-1])
else:
self.plotting_dates.append(pandas.to_datetime(plotting_dates[0]))
def __init__(self, I_para, R_para, intv, fig_size, plotting_dates,
style_cycle, para_to_plot, legend_name):
self.french_df = df_fct.import_df(['Fra_Nat_v2'], ['processed'])[0]
self.I_para = I_para
self.R_para = R_para
self.intv = intv
self.plotting_dates = [pandas.to_datetime(plotting_dates[0])]
self.style_cycle = style_cycle
self.root = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
self.fig_size = fig_size
self.para_to_plot = para_to_plot
self.legend_name = [legend_name[key] for key in self.para_to_plot]
self.pop = 67114995
if plotting_dates[1] == 'last':
self.plotting_dates.append(
self.french_df.index.get_level_values('date').unique()[-1])
else:
self.plotting_dates.append(pandas.to_datetime(plotting_dates[0]))
def __init__(self):
self.us_testing = df_fct.import_df(['US_Testing'], ['raw'])[0]
def __init__(self):
self.fra_testing_1, self.fra_testing_2 = df_fct.import_df(
['Fra_Testing1', 'Fra_Testing2'], ['raw', 'raw'])
self.fra_testing = pandas.DataFrame()
def __init__(self):
self.data_vax = df_fct.import_df(['Fra_Vax'], ['raw'])[0]
def __init__(self):
self.df_indic_nat = df_fct.import_df(['Fra_Indic_Nat'], ['raw'])[0]
self.df_indic_dpt = df_fct.import_df(['Fra_Indic_Dpt'], ['raw'])[0]
self.df_dpt_shp = df_fct.import_df(['Fra_Departements_shapefile'],
['raw'])[0]
def import_db(self):
self.df_cases, self.df_death = df_fct.import_df(
['World_JH_cases', 'World_JH_death'], ['raw' for x in range(2)])
self.df_fra = df_fct.import_df(['Fra_Nat'], ['processed'])[0]
