Code
Hub
Workspaces
Following
Trending
Connect
MCP
copy
Create free account
hub
/
github.com/COVIDAnalytics/DELPHI
/ functions
Functions
675 in github.com/COVIDAnalytics/DELPHI
⨍
Functions
675
◇
Types & classes
63
Method
get_aggregation_per_country
(df: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_secondwave.py:496
Method
get_aggregation_per_country
(df: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_new.py:446
Method
get_aggregation_world
Aggregates policy predictions at the world level from the predictions dataframe :param df_policy_predictions: DELPHI policy predictio
DELPHI_utils_V4_static.py:1576
Method
get_aggregation_world
Aggregates policy predictions at the world level from the predictions dataframe :param df_policy_predictions: DELPHI policy predictio
Connecticut/DELPHI_utils_CT_static.py:1645
Method
get_aggregation_world
(df: pd.DataFrame)
KIT/DELPHI_utils_KIT.py:446
Method
get_aggregation_world
(df: pd.DataFrame)
CDC/DELPHI_utils_CDC.py:446
Method
get_aggregation_world
(df: pd.DataFrame)
archive/V1 - No Jump/DELPHI_utils.py:516
Method
get_aggregation_world
(df: pd.DataFrame)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:473
Method
get_aggregation_world
(df: pd.DataFrame)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:663
Method
get_aggregation_world
(df: pd.DataFrame)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:473
Method
get_aggregation_world
(df: pd.DataFrame)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:471
Method
get_aggregation_world
(df: pd.DataFrame)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:550
Method
get_aggregation_world
Aggregates policy predictions at the world level from the predictions dataframe :param df_policy_predictions: DELPHI policy predictio
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1547
Method
get_aggregation_world
Aggregates policy predictions at the world level from the predictions dataframe :param df_policy_predictions: DELPHI policy predictio
archive/V3 Model/DELPHI_utils_V3_static.py:1543
Method
get_aggregation_world
(df: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_secondwave.py:518
Method
get_aggregation_world
(df: pd.DataFrame)
archive/Other Analyses/DELPHI_utils_new.py:468
Method
get_backtest_metrics_area
Updates the backtest metrics dictionary with metrics values for that particular area tuple :param df_backtest: pre-processed datafram
Connecticut/DELPHI_utils_CT_static.py:1803
Method
get_backtest_metrics_area
Updates the backtest metrics dictionary with metrics values for that particular area tuple :param df_backtest: pre-processed datafram
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1705
Method
get_backtest_metrics_area
Updates the backtest metrics dictionary with metrics values for that particular area tuple :param df_backtest: pre-processed datafram
archive/V3 Model/DELPHI_utils_V3_static.py:1701
Method
get_feasibility_flag
Checks that there is enough historical and prediction data to perform the backtest based on the user input :param df_historical: a da
Connecticut/DELPHI_utils_CT_static.py:1751
Method
get_feasibility_flag
Checks that there is enough historical and prediction data to perform the backtest based on the user input :param df_historical: a da
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1653
Method
get_feasibility_flag
Checks that there is enough historical and prediction data to perform the backtest based on the user input :param df_historical: a da
archive/V3 Model/DELPHI_utils_V3_static.py:1649
Method
get_historical_data_df
Generates a concatenation of all historical data available in the danger_map folder, all areas starting from the prediction date give
Connecticut/DELPHI_utils_CT_static.py:1701
Method
get_historical_data_df
Generates a concatenation of all historical data available in the danger_map folder, all areas starting from the prediction date give
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1603
Method
get_historical_data_df
Generates a concatenation of all historical data available in the danger_map folder, all areas starting from the prediction date give
archive/V3 Model/DELPHI_utils_V3_static.py:1599
Function
get_initial_conditions
(params_fitted, global_params_fixed)
archive/Other Analyses/DELPHI_utils_secondwave.py:542
Function
get_initial_conditions_with_testing
Generates the initial conditions for the DELPHI model based on global fixed parameters (mostly populations and some constant rates) and fitte
DELPHI_utils_V4_static.py:1815
Function
get_initial_conditions_with_testing
Generates the initial conditions for the DELPHI model based on global fixed parameters (mostly populations and some constant rates) and fitte
Connecticut/DELPHI_utils_CT_static.py:1884
Function
get_initial_conditions_with_testing
(params_fitted, global_params_fixed)
KIT/DELPHI_utils_KIT.py:500
Function
get_initial_conditions_with_testing
(params_fitted, global_params_fixed)
CDC/DELPHI_utils_CDC.py:500
Function
get_initial_conditions_with_testing
(params_fitted, global_params_fixed)
archive/V1 - No Jump/DELPHI_utils.py:570
Function
get_initial_conditions_with_testing
(params_fitted, global_params_fixed)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:527
Function
get_initial_conditions_with_testing
(params_fitted, global_params_fixed)
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:717
Function
get_initial_conditions_with_testing
(params_fitted, global_params_fixed)
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:527
Function
get_initial_conditions_with_testing
(params_fitted, global_params_fixed)
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:525
Function
get_initial_conditions_with_testing
(params_fitted, global_params_fixed)
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:606
Function
get_initial_conditions_with_testing
Generates the initial conditions for the DELPHI model based on global fixed parameters (mostly populations and some constant rates) and fitte
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1783
Function
get_initial_conditions_with_testing
Generates the initial conditions for the DELPHI model based on global fixed parameters (mostly populations and some constant rates) and fitte
archive/V3 Model/DELPHI_utils_V3_static.py:1779
Function
get_initial_conditions_with_testing
(params_fitted, global_params_fixed)
archive/Other Analyses/DELPHI_utils_secondwave.py:572
Function
get_normalized_policy_shifts_and_current_policy
( policy_data_us_only: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:917
Function
get_normalized_policy_shifts_and_current_policy_all_countries
Computes the normalized policy shifts and the current policy in each area of the world except the US (done in a separate function) :param
Connecticut/DELPHI_utils_CT_dynamic.py:714
Function
get_normalized_policy_shifts_and_current_policy_all_countries
( policy_data_countries: pd.DataFrame, pastparameters: pd.DataFrame, )
KIT/DELPHI_utils_KIT.py:884
Function
get_normalized_policy_shifts_and_current_policy_all_countries
( policy_data_countries: pd.DataFrame, pastparameters: pd.DataFrame, )
CDC/DELPHI_utils_CDC.py:883
Function
get_normalized_policy_shifts_and_current_policy_all_countries
( policy_data_countries: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:911
Function
get_normalized_policy_shifts_and_current_policy_all_countries
( policy_data_countries: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:1132
Function
get_normalized_policy_shifts_and_current_policy_all_countries
( policy_data_countries: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:911
Function
get_normalized_policy_shifts_and_current_policy_all_countries
( policy_data_countries: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:909
Function
get_normalized_policy_shifts_and_current_policy_all_countries
( policy_data_countries: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:958
Function
get_normalized_policy_shifts_and_current_policy_all_countries
( policy_data_countries: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Other Analyses/DELPHI_utils_secondwave.py:956
Function
get_normalized_policy_shifts_and_current_policy_us_only
Computes the normalized policy shifts and the current policy in each state of the US :param policy_data_us_only: processed dataframe with the
Connecticut/DELPHI_utils_CT_dynamic.py:648
Function
get_normalized_policy_shifts_and_current_policy_us_only
( policy_data_us_only: pd.DataFrame, pastparameters: pd.DataFrame, )
KIT/DELPHI_utils_KIT.py:838
Function
get_normalized_policy_shifts_and_current_policy_us_only
( policy_data_us_only: pd.DataFrame, pastparameters: pd.DataFrame, )
CDC/DELPHI_utils_CDC.py:837
Function
get_normalized_policy_shifts_and_current_policy_us_only
( policy_data_us_only: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:865
Function
get_normalized_policy_shifts_and_current_policy_us_only
( policy_data_us_only: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:1086
Function
get_normalized_policy_shifts_and_current_policy_us_only
( policy_data_us_only: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:865
Function
get_normalized_policy_shifts_and_current_policy_us_only
( policy_data_us_only: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:863
Function
get_normalized_policy_shifts_and_current_policy_us_only
( policy_data_us_only: pd.DataFrame, pastparameters: pd.DataFrame, )
archive/Other Analyses/DELPHI_utils_secondwave.py:910
Method
get_prediction_data
Retrieve the predicted data on the prediction_date given as an input by the user running :param prediction_date: prediction date to b
Connecticut/DELPHI_utils_CT_static.py:1732
Method
get_prediction_data
Retrieve the predicted data on the prediction_date given as an input by the user running :param prediction_date: prediction date to b
archive/V3 Model/DELPHI_utils_V3_static_serology.py:1634
Method
get_prediction_data
Retrieve the predicted data on the prediction_date given as an input by the user running :param prediction_date: prediction date to b
archive/V3 Model/DELPHI_utils_V3_static.py:1630
Function
get_testing_data_us
Function that retrieves testing data in the US from the CovidTracking website :return: a DataFrame where the column of interest is 'testing_c
DELPHI_utils_V4_dynamic.py:797
Function
get_testing_data_us
Function that retrieves testing data in the US from the CovidTracking website :return: a DataFrame where the column of interest is 'testing_c
Connecticut/DELPHI_utils_CT_dynamic.py:806
Function
get_testing_data_us
:return: a DataFrame where the column of interest is 'testing_cnt_daily' which gives the numbers of new daily tests per state
KIT/DELPHI_utils_KIT.py:1005
Function
get_testing_data_us
:return: a DataFrame where the column of interest is 'testing_cnt_daily' which gives the numbers of new daily tests per state
CDC/DELPHI_utils_CDC.py:1004
Function
get_testing_data_us
:return: a DataFrame where the column of interest is 'testing_cnt_daily' which gives the numbers of new daily tests per state
archive/V1 - No Jump/DELPHI_utils.py:1075
Function
get_testing_data_us
:return: a DataFrame where the column of interest is 'testing_cnt_daily' which gives the numbers of new daily tests per state
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_trust.py:1032
Function
get_testing_data_us
:return: a DataFrame where the column of interest is 'testing_cnt_daily' which gives the numbers of new daily tests per state
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_utils_V3_annealing.py:1253
Function
get_testing_data_us
:return: a DataFrame where the column of interest is 'testing_cnt_daily' which gives the numbers of new daily tests per state
archive/Test Models for V3/ArcTan Jump/DELPHI_utils_V4.py:1032
Function
get_testing_data_us
:return: a DataFrame where the column of interest is 'testing_cnt_daily' which gives the numbers of new daily tests per state
archive/Test Models for V3/Discrete Jump/DELPHI_utils_V2.py:1030
Function
get_testing_data_us
:return: a DataFrame where the column of interest is 'testing_cnt_daily' which gives the numbers of new daily tests per state
archive/backtesting_archived/DELPHI_backtest_utils_ventilator.py:1067
Function
get_testing_data_us
Function that retrieves testing data in the US from the CovidTracking website :return: a DataFrame where the column of interest is 'testing_c
archive/V3 Model/DELPHI_utils_V3_dynamic.py:788
Function
get_testing_data_us
:return: a DataFrame where the column of interest is 'testing_cnt_daily' which gives the numbers of new daily tests per state
archive/Other Analyses/DELPHI_utils_secondwave.py:1077
Method
kl_divergence
Compute the KL divergence between two lists :param y_true: list of true historical values :param y_pred: list of predicted va
DELPHI_utils_V4_dynamic.py:843
Method
kl_divergence
Compute the KL divergence between two lists :param y_true: list of true historical values :param y_pred: list of predicted va
Connecticut/DELPHI_utils_CT_dynamic.py:852
Method
kl_divergence
Compute the KL divergence between two lists :param y_true: list of true historical values :param y_pred: list of predicted va
archive/V3 Model/DELPHI_utils_V3_dynamic.py:834
Function
mape
(y_true, y_pred)
archive/Other Analyses/DELPHI_utils_secondwave.py:642
Method
max_ape
Compute the Maximum Absolute Percentage Error between two lists :param y_true: list of true historical values :param y_pred:
DELPHI_utils_V4_dynamic.py:856
Method
max_ape
Compute the Maximum Absolute Percentage Error between two lists :param y_true: list of true historical values :param y_pred:
Connecticut/DELPHI_utils_CT_dynamic.py:864
Method
max_ape
Compute the Maximum Absolute Percentage Error between two lists :param y_true: list of true historical values :param y_pred:
archive/V3 Model/DELPHI_utils_V3_dynamic.py:846
Function
model_covid
SEIR based model with 16 distinct states, taking into account undetected, deaths, hospitalized and recovered, and usi
DELPHI_model_V4_predict.py:184
Function
model_covid
SEIR based model with 16 distinct states, taking into account undetected, deaths, hospitalized and recovered, and usi
DELPHI_model_V4.py:223
Function
model_covid
( t, x, alpha, days, r_s, r_dth, p_dth, k1, k2, jump, t_jump, std_normal, t_rh, p_d )
Connecticut/DELPHI_model_HHC.py:150
Function
model_covid
SEIR based model with 16 distinct states, taking into account undetected, deaths, hospitalized and recovered, and usi
Connecticut/DELPHI_model_CT.py:210
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate d
KIT/DELPHI_model_KIT.py:151
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate d
CDC/DELPHI_model_CDC.py:151
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate d
archive/V1 - No Jump/DELPHI_model.py:136
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate d
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_model_V3_trust.py:134
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate d
archive/Test Models for V3/Normal Jump + Trust Solver/DELPHI_model_V3_annealing.py:154
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate d
archive/Test Models for V3/ArcTan Jump/DELPHI_model_V4.py:145
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate d
archive/Test Models for V3/Discrete Jump/DELPHI_model_V2.py:137
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate d
archive/backtesting_archived/DELPHI_backtest_old.py:155
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate
archive/backtesting_archived/DELPHI_backtest_model_ventilator.py:151
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate
archive/backtesting_archived/DELPHI_backtest_scenarios.py:141
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate
archive/backtesting_archived/DELPHI_backtest_scenarios_reopening.py:127
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate
archive/Adaptive Policy Model - Continuous Retraining/DELPHI_model_cr_with_policies.py:231
Function
model_covid
SEIR based model with 16 distinct states, taking into account undetected, deaths, hospitalized and recovered, and usi
archive/V3 Model/DELPHI_model_V3_serology.py:222
Function
model_covid
SEIR based model with 16 distinct states, taking into account undetected, deaths, hospitalized and recovered, and usi
archive/V3 Model/DELPHI_model_V3.py:216
Function
model_covid
SEIR based model with 16 distinct states, taking into account undetected, deaths, hospitalized and recovered, and usi
archive/V3 Model/DELPHI_model_V3_predict.py:166
Function
model_covid
SEIR + Undetected, Deaths, Hospitalized, corrected with ArcTan response curve alpha: Infection rate d
archive/Other Analyses/DELPHI_model_with_testing_US_only.py:158
Function
model_covid_predictions
SEIR based model with 16 distinct states, taking into account undetected, deaths, hospitalized and re
DELPHI_model_V4_with_policies.py:181
← previous
next →
501–600 of 675, ranked by callers