awspricingfull module¶
AWS Instances (EC2, ElastiCache, RDS, Redshift, DynamoDB) pricing retrieval project.
Project contains one module which is designed to retrieve the AWS prices for five major AWS services that have reserved capacity involved: EC2, ElastiCache, RDS, Redshift and DynamoDB. The prices either On-Demand or Reserved (specified by user) can be retrieved to Command Line in JSON, Table (Prettytable) or CSV formats. CSV format option also saves the csv file to the folder specified by user, which is the main use case.
The undocumented AWS pricing APIs are used as the sources. The same APIs serve the data to the AWS pricing pages. Both current and previous generation instance prices are retrieved.
Update 2.0: New pricing scheme (noUpfront, allUpfront, PartialUpfront) compatibility for RDS and Redshift is added. Minor bugs fixed.
Update 3.0: DynamoDB throughput capacity pricing is added. MariaDB and Aurora are added to RDS. New schema for AllPrices table introduced (DB and OS columns merged).
Created: 25 March, 2015
Updated: 3 May, 2016
@author: Ilia Semenov
@version: 3.0
-
class
awspricingfull.
AWSPrices
[source]¶ Bases:
object
Abstract class - base for the pricing retrieval classes for different services.
-
DEFAULT_CURRENCY
¶ str
USD Currency used in every AWS pricing by default.
-
REGIONS
¶ list of str
List of AWS regions.
-
JSON_NAME_TO_REGIONS_API
¶ dict of str: str
Mapping of some internal AWS region names used in few sources to the conventional ones.
-
get_ondemand_instances_prices
()[source]¶ - Abstract method for getting On-Demand pricing.
- Implemented in child classes.
Raises: NotImplementedError
– Abstract method is not implemented.
-
get_reserved_instances_prices
()[source]¶ - Abstract method for getting Reserved pricing.
- Implemented in child classes.
Raises: NotImplementedError
– Abstract method is not implemented.
-
load_data
(url)[source]¶ Method for retrieving the pricing data in a clean dictionary format.
Parameters: url – The pricing source url. Returns: data – Pricing data in a dictionary format Return type: dict of dict: dict
-
-
class
awspricingfull.
AllAWSPrices
[source]¶ Bases:
awspricingfull.AWSPrices
- Class for retrieving the instance pricing for 5 AWS Services:
- EC2, RDS, ElastiCache, Redshift and DynamoDB. Child of
awspricingfull.AWSPrices
class.
-
ec2
¶ EC2Prices
instance of
awspricingfull.EC2Prices
class containing methods for EC2 pricing retrieval.
-
elc
¶ ELCPrices
instance of
awspricingfull.ELCPrices
class containing methods for ElastiCache pricing retrieval.
-
rds
¶ RDSPrices
instance of
awspricingfull.RDSPrices
class containing methods for RDS pricing retrieval.
-
rs
¶ RSPrices
instance of
awspricingfull.RSPrices
class containing methods for Redshift pricing retrieval.
-
ddb
¶ DDBPrices
instance of
awspricingfull.DDBPrices
class containing methods for DynamoDB pricing retrieval.
-
return_json
(u)[source]¶ Method printing the pricing data in JSON format to Console.
Parameters: u (str) – Parameter specifying On-Demand (“ondemand”), Reserved (“reserved”) or both (“all”) pricing option. Returns: Pricing data in JSON string format or an error message. Return type: str
-
save_csv
(u, path='C:\\Users\\isemenov\\workspace\\awspricingfull\\docs\\', name=None)[source]¶ - Method saving the full pricing data in CSV format to the
- cpecified location.
Parameters: - u (str) – Parameter specifying On-Demand (“ondemand”), Reserved (“reserved”) or both (“all”) pricing option.
- path (str) – System path for saving the data file. Current directory is the the defauilt value.
- name (str) – The desired name of the file. The default values are “FULL_reserved_pricing.csv” for Reserved, “FULL_ondemand_pricing.csv” for On-Demand and “FULL_all_pricing.csv” for both.
Returns: Prints Full pricing in the CSV format (console).
-
class
awspricingfull.
AllAWSPrices2
[source]¶ Bases:
awspricingfull.AWSPrices
- Class for retrieving the instance pricing for 5 AWS Services:
- EC2, RDS, ElastiCache, Redshift and DynamoDB. Child of
awspricingfull.AWSPrices
class.
-
ec2
¶ EC2Prices
instance of
awspricingfull.EC2Prices
class containing methods for EC2 pricing retrieval.
-
elc
¶ ELCPrices
instance of
awspricingfull.ELCPrices
class containing methods for ElastiCache pricing retrieval.
-
rds
¶ RDSPrices
instance of
awspricingfull.RDSPrices
class containing methods for RDS pricing retrieval.
-
rs
¶ RSPrices
instance of
awspricingfull.RSPrices
class containing methods for Redshift pricing retrieval.
-
ddb
¶ DDBPrices
instance of
awspricingfull.DDBPrices
class containing methods for DynamoDB pricing retrieval.
-
return_json
(u)[source]¶ Method printing the pricing data in JSON format to Console.
Parameters: u (str) – Parameter specifying On-Demand (“ondemand”), Reserved (“reserved”) or both (“all”) pricing option. Returns: Pricing data in JSON string format or an error message. Return type: str
-
save_csv
(u, path='C:\\Users\\isemenov\\workspace\\awspricingfull\\docs\\', name=None)[source]¶ - Method saving the full pricing data in CSV format to the
- cpecified location.
Parameters: - u (str) – Parameter specifying On-Demand (“ondemand”), Reserved (“reserved”) or both (“all”) pricing option.
- path (str) – System path for saving the data file. Current directory is the the defauilt value.
- name (str) – The desired name of the file. The default values are “FULL_reserved_pricing.csv” for Reserved, “FULL_ondemand_pricing.csv” for On-Demand and “FULL_all_pricing.csv” for both.
Returns: Prints Full pricing in the CSV format (console).
-
class
awspricingfull.
DDBPrices
[source]¶ Bases:
awspricingfull.AWSPrices
Class for retrieving the DynamoDB pricing. Child of
awspricingfull.AWSPrices
class.-
DDB_ON_DEMAND_URL
¶ str
Undocumented AWS Pricing API URL - On-Demand DynamoDB Nodes
-
DDB_RESERVED_URL
¶ str
Undocumented AWS Pricing API URL - Reserved DynamoDB Nodes
-
INSTANCE_TYPE_MAPPING
¶ dict of str: str
Mapping of DynamoDB throughput types (read/write) to instance-type-like names (ddb.read/ddb.write)
-
get_ondemand_instances_prices
()[source]¶ Implementation of method for getting DynamoDB On-Denand pricing.
Returns: result – DynamoDB On-Denand pricing in dictionary format. Return type: dict of dict: dict
-
get_reserved_instances_prices
()[source]¶ Implementation of method for getting DynamoDB Reserved pricing.
Returns: result – DynamoDB Reserved pricing in dictionary format. Return type: dict of dict: dict
-
print_table
(u)[source]¶ - Method printing the DynamoDB pricing data to the console
- in the Pretty Table format (requires Pretty Table import).
Parameters: u (str) – Parameter specifying On-Demand (“ondemand”) or Reserved (“reserved”) pricing option. Returns: Prints DynamoDB pricing in the Pretty Table format.
-
save_csv
(u, path='C:\\Users\\isemenov\\workspace\\awspricingfull\\docs\\', name=None)[source]¶ - Method saving the DynamoDB pricing data in CSV format to the
- cpecified location.
Parameters: - u (str) – Parameter specifying On-Demand (“ondemand”) or Reserved (“reserved”) pricing option.
- path (str) – System path for saving the data file. Current directory is the the defauilt value.
- name (str) – The desired name of the file. The default values are “DDB_reserved_pricing.csv” for Reserved and “DDB_ondemand_pricing.csv” for On-Demand.
Returns: Prints DynamoDB pricing in the CSV format (console).
-
-
class
awspricingfull.
EC2Prices
[source]¶ Bases:
awspricingfull.AWSPrices
Class for retrieving the EC2 pricing. Child of
awspricingfull.AWSPrices
class.-
INSTANCES_RESERVED_LINUX_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Linux Reserved, Current Generation.
-
INSTANCES_RESERVED_RHEL_URL
¶ str
Undocumented AWS Pricing API URL - EC2 RHEL Reserved, Current Generation.
-
INSTANCES_RESERVED_SLES_URL
¶ str
Undocumented AWS Pricing API URL - EC2 SLES Reserved, Current Generation.
-
INSTANCES_RESERVED_WINDOWS_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows Reserved, Current Generation.
-
INSTANCES_RESERVED_WINSQL_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows SQL Reserved, Current Generation.
-
INSTANCES_RESERVED_WINSQLWEB_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows SQL Web Reserved, Current Generation.
-
PG_INSTANCES_RESERVED_LINUX_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Linux Reserved, Previous Generation.
-
PG_INSTANCES_RESERVED_RHEL_URL
¶ str
Undocumented AWS Pricing API URL - EC2 RHEL Reserved, Previous Generation.
-
PG_INSTANCES_RESERVED_SLES_URL
¶ str
Undocumented AWS Pricing API URL - EC2 SLES Reserved, Previous Generation.
-
PG_INSTANCES_RESERVED_WINDOWS_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows Reserved, Previous Generation.
-
PG_INSTANCES_RESERVED_WINSQL_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows SQL Reserved, Previous Generation.
-
PG_INSTANCES_RESERVED_WINSQLWEB_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows SQL Web Reserved, Previous Generation.
-
INSTANCES_ONDEMAND_LINUX_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Linux On-Demand, Current Generation.
-
INSTANCES_ONDEMAND_RHEL_URL
¶ str
Undocumented AWS Pricing API URL - EC2 RHEL On-Demand, Current Generation.
-
INSTANCES_ONDEMAND_SLES_URL
¶ str
Undocumented AWS Pricing API URL - EC2 SLES On-Demand, Current Generation.
-
INSTANCES_ONDEMAND_WINDOWS_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows On-Demand, Current Generation.
-
INSTANCES_ONDEMAND_WINSQL_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows SQL On-Demand, Current Generation.
-
INSTANCES_ONDEMAND_WINSQLWEB_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows SQL Web On-Demand, Current Generation.
-
PG_INSTANCES_ONDEMAND_LINUX_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Linux On-Demand, Previous Generation.
-
PG_INSTANCES_ONDEMAND_RHEL_URL
¶ str
Undocumented AWS Pricing API URL - EC2 RHEL On-Demand, Previous Generation.
-
PG_INSTANCES_ONDEMAND_SLES_URL
¶ str
Undocumented AWS Pricing API URL - EC2 SLES On-Demand, Previous Generation.
-
PG_INSTANCES_ONDEMAND_WINDOWS_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows On-Demand, Previous Generation.
-
PG_INSTANCES_ONDEMAND_WINSQL_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows SQL On-Demand, Previous Generation.
-
PG_INSTANCES_ONDEMAND_WINSQLWEB_URL
¶ str
Undocumented AWS Pricing API URL - EC2 Windows SQL Web On-Demand, Previous Generation.
-
INSTANCES_ONDEMAND_OS_TYPE_BY_URL
¶ dict of str: str
Mapping of On-Dermand urls to OS types.
-
INSTANCES_RESERVED_OS_TYPE_BY_URL
¶ dict of str: str
Mapping of Reserved urls to OS types.
-
get_ondemand_instances_prices
()[source]¶ Implementation of method for getting EC2 On-Demand pricing.
Returns: result – EC2 On-Demand pricing in dictionary format. Return type: dict of dict: dict
-
get_reserved_instances_prices
()[source]¶ Implementation of method for getting EC2 Reserved pricing.
Returns: result – EC2 Reserved pricing in dictionary format. Return type: dict of dict: dict
-
print_table
(u)[source]¶ - Method printing the EC2 pricing data to the console
- in the Pretty Table format (requires Pretty Table import).
Parameters: u (str) – Parameter specifying On-Demand (“ondemand”) or Reserved (“reserved”) pricing option. Returns: Prints EC2 pricing in the Pretty Table format.
-
save_csv
(u, path='C:\\Users\\isemenov\\workspace\\awspricingfull\\docs\\', name=None)[source]¶ - Method saving the EC2 pricing data in CSV format to the
- cpecified location.
Parameters: - u (str) – Parameter specifying On-Demand (“ondemand”) or Reserved (“reserved”) pricing option.
- path (str) – System path for saving the data file. Current directory is the the defauilt value.
- name (str) – The desired name of the file. The default values are “EC2_reserved_pricing.csv” for Reserved and “EC2_ondemand_pricing.csv” for On-Demand.
Returns: Prints EC2 pricing in the CSV format (console).
-
-
class
awspricingfull.
ELCPrices
[source]¶ Bases:
awspricingfull.AWSPrices
Class for retrieving the ElastiCache pricing. Child of
awspricingfull.AWSPrices
class.-
INSTANCES_ON_DEMAND_URL
¶ str
Undocumented AWS Pricing API URL - On-Demand ELC Current Generation.
-
PG_INSTANCES_ON_DEMAND_URL
¶ str
Undocumented AWS Pricing API URL - On-Demand ELC Previous Generation.
-
INSTANCES_RESERVED_LIGHT_UTILIZATION_URL
¶ str
Undocumented AWS Pricing - Light Reserved ELC Current Generation.
-
INSTANCES_RESERVED_MEDIUM_UTILIZATION_URL
¶ str
Undocumented AWS Pricing- Medium Reserved ELC Current Generation.
-
INSTANCES_RESERVED_HEAVY_UTILIZATION_URL
¶ str
Undocumented AWS Pricing- Heavy Reserved ELC Current Generation.
-
PG_INSTANCES_RESERVED_LIGHT_UTILIZATION_URL
¶ str
Undocumented AWS Pricing- Light Reserved ELC Previous Generation.
-
PG_INSTANCES_RESERVED_MEDIUM_UTILIZATION_URL
¶ str
Undocumented AWS Pricing- Medium Reserved ELC Previous Generation.
-
PG_INSTANCES_RESERVED_HEAVY_UTILIZATION_URL
¶ str
Undocumented AWS Pricing- Heavy Reserved ELC Previous Generation.
-
INSTANCES_RESERVED_UTILIZATION_TYPE_BY_URL
¶ dict of str: str
Mapping of Reserved urls to Reservation types.
-
INSTANCE_TYPE_MAPPING
¶ dict of str: str
Mapping of internal AWS ELC Type names to the conventional analogs.
-
get_ondemand_instances_prices
()[source]¶ Implementation of method for getting ELC On-Denand pricing.
Returns: result – ELC On-Denand pricing in dictionary format. Return type: dict of dict: dict
-
get_reserved_instances_prices
()[source]¶ Implementation of method for getting ELC Reserved pricing.
Returns: result – ELC Reserved pricing in dictionary format. Return type: dict of dict: dict
-
print_table
(u)[source]¶ - Method printing the ELC pricing data to the console
- in the Pretty Table format (requires Pretty Table import).
Parameters: u (str) – Parameter specifying On-Demand (“ondemand”) or Reserved (“reserved”) pricing option. Returns: Prints ELC pricing in the Pretty Table format.
-
save_csv
(u, path='C:\\Users\\isemenov\\workspace\\awspricingfull\\docs\\', name=None)[source]¶ - Method saving the ELC pricing data in CSV format to the
- cpecified location.
Parameters: - u (str) – Parameter specifying On-Demand (“ondemand”) or Reserved (“reserved”) pricing option.
- path (str) – System path for saving the data file. Current directory is the the defauilt value.
- name (str) – The desired name of the file. The default values are “ELC_reserved_pricing.csv” for Reserved and “ELC_ondemand_pricing.csv” for On-Demand.
Returns: Prints ELC pricing in the CSV format (console).
-
-
class
awspricingfull.
RDSPrices
[source]¶ Bases:
awspricingfull.AWSPrices
Class for retrieving the RDS pricing. Child of
awspricingfull.AWSPrices
class.-
RDS_MYSQL_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MySQL GPL, On-Demand, Current Generation.
-
RDS_MYSQL_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ MySQL GPL, On-Demand, Current Generation.
-
RDS_ORACLE_LICENSED_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Oracle Licensed, On-Demand, Current Generation.
-
RDS_ORACLE_LICENSED_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Oracle Licensed, On-Demand, Current Generation.
-
RDS_ORACLE_BYOL_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Oracle BYOL, On-Demand, Current Generation.
-
RDS_ORACLE_BYOL_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Oracle BYOL, On-Demand, Current Generation.
-
RDS_MSSQL_LICENSED_EXPRESS_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MS SQL Server Express Licensed, On-Demand, Current Generation.
-
RDS_MSSQL_LICENSED_WEB_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MS SQL Server WEB Licensed, On-Demand, Current Generation.
-
RDS_MSSQL_LICENSED_STANDARD_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MS SQL Server SE Licensed, On-Demand, Current Generation.
-
RDS_MSSQL_LICENSED_STANDARD_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ MS SQL Server SE Licensed, On-Demand, Current Generation.
-
RDS_MSSQL_BYOL_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MS SQL Server BYOL, On-Demand, Current Generation.
-
RDS_MSSQL_BYOL_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ MS SQL Server BYOL, On-Demand, Current Generation.
-
RDS_POSTGRESQL_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Postgres On-Demand, Current Generation.
-
RDS_POSTGRESQL_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Postgres, On-Demand, Current Generation.
-
PG_RDS_MYSQL_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MySQL GPL, On-Demand, Previous Generation.
-
PG_RDS_MYSQL_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ MySQL GPL, On-Demand, Previous Generation.
-
PG_RDS_ORACLE_LICENSED_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Oracle Licensed, On-Demand, Previous Generation.
-
PG_RDS_ORACLE_LICENSED_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Oracle Licensed, On-Demand, Previous Generation.
-
PG_RDS_ORACLE_BYOL_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Oracle BYOL, On-Demand, Previous Generation.
-
PG_RDS_ORACLE_BYOL_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Oracle BYOL, On-Demand, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_EXPRESS_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MS SQL Server Express Licensed, On-Demand, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_WEB_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MS SQL Server WEB Licensed, On-Demand, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_STANDARD_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MS SQL Server SE Licensed, On-Demand, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_STANDARD_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ MS SQL Server SE Licensed, On-Demand, Previous Generation.
-
PG_RDS_MSSQL_BYOL_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MS SQL Server BYOL, On-Demand, Previous Generation.
-
PG_RDS_MSSQL_BYOL_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ MS SQL Server BYOL, On-Demand, Previous Generation.
-
PG_RDS_POSTGRESQL_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Postgres On-Demand, Previous Generation.
-
PG_RDS_POSTGRESQL_MULTIAZ_ON_DEMAND_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Postgres, On-Demand, Previous Generation.
-
RDS_MYSQL_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MySQL GPL, Reserved, Current Generation.
-
RDS_MYSQL_MULTIAZ_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ MySQL GPL, Reserved, Current Generation.
-
RDS_ORACLE_LICENSED_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Oracle Licensed, Reserved, Current Generation.
-
RDS_ORACLE_LICENSED_MULTIAZ_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Oracle Licensed, Reserved, Current Generation.
-
RDS_ORACLE_BYOL_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Oracle BYOL, Reserved, Current Generation.
-
RDS_ORACLE_BYOL_MULTIAZ_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Oracle BYOL, Reserved, Current Generation.
-
RDS_MSSQL_LICENSED_EX_RESERVED_LIGHT_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server Express Licensed, Reserved Light, Current Generation.
-
RDS_MSSQL_LICENSED_EX_RESERVED_MEDIUM_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server Express Licensed, Reserved Medium, Current Generation.
-
RDS_MSSQL_LICENSED_EX_RESERVED_HEAVY_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server Express Licensed, Reserved Heavy, Current Generation.
-
RDS_MSSQL_LICENSED_WEB_RESERVED_LIGHT_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server WEB Licensed, Reserved Light, Current Generation.
-
RDS_MSSQL_LICENSED_WEB_RESERVED_MEDIUM_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server WEB Licensed, Reserved Medium, Current Generation.
-
RDS_MSSQL_LICENSED_WEB_RESERVED_HEAVY_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server WEB Licensed, Reserved Heavy, Current Generation.
-
RDS_MSSQL_LICENSED_STANDARD_RESERVED_LIGHT_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server SE Licensed, Reserved Light, Current Generation.
-
RDS_MSSQL_LICENSED_STANDARD_RESERVED_MEDIUM_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server SE Licensed, Reserved Medium, Current Generation.
-
RDS_MSSQL_LICENSED_STANDARD_RESERVED_HEAVY_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server SE Licensed, Reserved Heavy, Current Generation.
-
RDS_MSSQL_BYOL_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MS SQL Server BYOL, Reserved, Current Generation.
-
RDS_MSSQL_BYOL_MULTIAZ_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ MS SQL Server BYOL, Reserved, Current Generation.
-
RDS_POSTGRESQL_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Postgres, Reserved, Current Generation.
-
RDS_POSTGRESQL_MULTIAZ_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Postgres, Reserved, Current Generation.
-
PG_RDS_MYSQL_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MySQL GPL, Reserved, Previous Generation.
-
PG_RDS_MYSQL_MULTIAZ_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ MySQL GPL, Reserved, Previous Generation.
-
PG_RDS_ORACLE_LICENSED_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Oracle Licensed, Reserved, Previous Generation.
-
PG_RDS_ORACLE_LICENSED_MULTIAZ_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Oracle Licensed, Reserved, Previous Generation.
-
PG_RDS_ORACLE_BYOL_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Oracle BYOL, Reserved, Previous Generation.
-
PG_RDS_ORACLE_BYOL_MULTIAZ_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Oracle BYOL, Reserved, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_EX_RESERVED_LIGHT_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server Express Licensed, Reserved Light, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_EX_RESERVED_MEDIUM_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server Express Licensed, Reserved Medium, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_EX_RESERVED_HEAVY_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server Express Licensed, Reserved Heavy, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_LIGHT_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server WEB Licensed, Reserved Light, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_MEDIUM_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server WEB Licensed, Reserved Medium, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_HEAVY_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server WEB Licensed, Reserved Heavy, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_LIGHT_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server SE Licensed, Reserved Light, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_MEDIUM_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server SE Licensed, Reserved Medium, Previous Generation.
-
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_HEAVY_URL
¶ str
Undocumented AWS API URL - RDS MS SQL Server SE Licensed, Reserved Heavy, Previous Generation.
-
PG_RDS_MSSQL_BYOL_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Single AZ MS SQL Server BYOL, Reserved, Previous Generation.
-
PG_RDS_MSSQL_BYOL_MULTIAZ_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ MS SQL Server BYOL, Reserved, Previous Generation.
-
PG_RDS_POSTGRESQL_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Single AZ Postgres, Reserved, Previous Generation.
-
PG_RDS_POSTGRESQL_MULTIAZ_RESERVED_URL
¶ str
Undocumented AWS API URL - RDS Multi AZ Postgres, Reserved, Previous Generation.
-
RDS_ENGINE_TYPES
¶ list of str
List of RDS engines.
-
RDS_MULTIAZ_TYPES
¶ list of str
List of RDS Multi-AZ options.
-
RDS_MULTIAZ_MAPPING
¶ dict of str: str
Mapping of internal AWS RDS Family names to the cvonventional Multi-AZ tag.
-
RDS_ONDEMAND_TYPE_BY_URL
¶ dict of str: list of str
Mapping of AWS RDS URLs (On-Demand, Single AZ) to corresponding engine and license type.
-
RDS_ONDEMAND_MULTIAZ_TYPE_BY_URL
¶ dict of str: list of str
Mapping of AWS RDS URLs (On-Demand, Multi AZ) to corresponding engine and license type.
-
RDS_RESERVED_TYPE_BY_URL_NEW
¶ dict of str: list of str
Mapping of AWS RDS URLs (Reserved, Single AZ, New Pricing Scheme) to corresponding engine and license type.
-
RDS_RESERVED_MULTIAZ_TYPE_BY_URL_NEW
¶ dict of str: list of str
Mapping of AWS RDS URLs (Reserved, Multi AZ, New Pricing Scheme) to corresponding engine and license type.
-
RDS_RESERVED_TYPE_BY_URL_OLD
¶ dict of str: list of str
Mapping of AWS RDS URLs (Reserved, Old Pricing Scheme) to corresponding engine and license type.
-
INSTANCE_TYPE_MAPPING
¶ dict of str: str
Mapping of internal AWS RDS Type names to the cvonventional analogs.
-
get_ondemand_instances_prices
()[source]¶ Implementation of method for getting RDS On-Denand pricing.
Returns: result – RDS On-Denand pricing in dictionary format. Return type: dict of dict: dict
-
get_reserved_instances_prices
()[source]¶ Implementation of method for getting RDS Reserved pricing.
Returns: result – RDS Reserved pricing in dictionary format. Return type: dict of dict: dict
-
print_table
(u)[source]¶ - Method printing the RDS pricing data to the console
- in the Pretty Table format (requires Pretty Table import).
Parameters: u (str) – Parameter specifying On-Demand (“ondemand”) or Reserved (“reserved”) pricing option. Returns: Prints RDS pricing in the Pretty Table format.
-
save_csv
(u, path='C:\\Users\\isemenov\\workspace\\awspricingfull\\docs\\', name=None)[source]¶ - Method saving the RDS pricing data in CSV format to the
- cpecified location.
Parameters: - u (str) – Parameter specifying On-Demand (“ondemand”) or Reserved (“reserved”) pricing option.
- path (str) – System path for saving the data file. Current directory is the the defauilt value.
- name (str) – The desired name of the file. The default values are “RDS_reserved_pricing.csv” for Reserved and “RDS_ondemand_pricing.csv” for On-Demand.
Returns: Prints RDS pricing in the CSV format (console).
-
-
class
awspricingfull.
RSPrices
[source]¶ Bases:
awspricingfull.AWSPrices
Class for retrieving the Redshift pricing. Child of
awspricingfull.AWSPrices
class.-
RS_ON_DEMAND_URL
¶ str
Undocumented AWS Pricing API URL - On-Demand Redshift Nodes
-
PG_RS_ON_DEMAND_URL
¶ str
Undocumented AWS Pricing API URL - Reserved Redshift Nodes, Previous Generation
-
RS_RESERVED_URL
¶ str
Undocumented AWS Pricing API URL - Reserved Redshift Nodes
-
PG_RS_RESERVED_URL
¶ str
Undocumented AWS Pricing API URL - Reserved Redshift Nodes, Previous Generation
-
get_ondemand_instances_prices
()[source]¶ Implementation of method for getting Redshift On-Denand pricing.
Returns: result – Redshift On-Denand pricing in dictionary format. Return type: dict of dict: dict
-
get_reserved_instances_prices
()[source]¶ Implementation of method for getting Redshift Reserved pricing.
Returns: result – Redshift Reserved pricing in dictionary format. Return type: dict of dict: dict
-
print_table
(u)[source]¶ - Method printing the Redshift pricing data to the console
- in the Pretty Table format (requires Pretty Table import).
Parameters: u (str) – Parameter specifying On-Demand (“ondemand”) or Reserved (“reserved”) pricing option. Returns: Prints Redshift pricing in the Pretty Table format.
-
save_csv
(u, path='C:\\Users\\isemenov\\workspace\\awspricingfull\\docs\\', name=None)[source]¶ - Method saving the Redshift pricing data in CSV format to the
- cpecified location.
Parameters: - u (str) – Parameter specifying On-Demand (“ondemand”) or Reserved (“reserved”) pricing option.
- path (str) – System path for saving the data file. Current directory is the the defauilt value.
- name (str) – The desired name of the file. The default values are “Redshift_reserved_pricing.csv” for Reserved and “Redshift_ondemand_pricing.csv” for On-Demand.
Returns: Prints Redshift pricing in the CSV format (console).
-


Alien: Resurrection
Chad'z rating: ** (out of 4 = fair/below average)
1997, R, 108 minutes [1 hour, 48 minutes]
[science fiction/thriller]
starring: Sigourney Weaver (Ripley), Winona Ryder (Call), Ron Perlman (Johner), Gary Dourdan (Christie); written by Joss Whedon; produced by Bill Badalato, Gordon Carroll, David Giler, Walter Hill; directed by Jean-Pierre Jeunet; based on characters created by Dan O'Bannon, Ronald Shusett.
Seen December 1, 1997 at 7:20 p.m. at The Glenwood Movieplex Cinemas (Oneida, NY), theater #2, by myself for free (free pass). [Theater rating: ***: good seats, sound, and picture]
If there's anything I learned about the "Alien" series, it's the fact it epitomizes the term "The more things change, the more they stay the same." Each film has a unique direction to it, yet the stories and individual elements are basically the same. But what's most annoying about "Alien: Resurrection," besides the fact it repeats absolutely everything we've seen before, is it doesn't even try to have any direction or originality.
Describing the plots and the way they are set up in these films is beginning to become an exercise in futility, and I wouldn't doubt the screenwriters feel the same way writing them. Much like the previous sequels, there's a lot of jargon and rhetoric here to explain how Ripley (Weaver) will end up facing the aliens even though everything was fine when we last saw her. This time it's 200 years after the death of her original self, and scientists have created a clone of her (why did they wait so long?). How they did this isn't clearly explained, but we are knocked over the head with the fact she has memories and was slightly crossbred with alien DNA (which explains her super-strength and psychic abilities).
We get more variations on the same plot such as the arrogant scientists and leaders who try to study, breed, and exploit the aliens but something goes wrong and chaos ensues. There's also a militia brat pack, although this time they're pirates instead of miners, marines, or prisoners. Much like Ripley, they're also re-incarnated characters. First there's Johner (Perlman), the wise-cracking tough guy; then Call (Ryder), the smart, rational, strong-willed woman; Christie (Dourdan), the special weapons guy; along with a cripple in a high-tech wheelchair, a frail woman, and the suave leader. Most of these characters will die just as we're beginning to like them, and one of them will turn out to be a robot (deja vu!).
The storytelling here is so choppy we don't really get establishment of plot, but a collection of remotely related scenes, such as the scientist driven mad with curiosity studying the aliens, or the meeting between the pirate leader and the corrupt military leader which somehow explains why the pirates are on board the huge military vessel the film takes place in.
Of course the aliens break free from their cages and run rampant around the ship, killing many of the humans and giving the pirates something to run from and shoot at. And if that wasn't generic enough, I counted at least six times in which the music built up with violins screeching and then an alien popping out the shadows and killing someone. There's also an interesting scene (the only one for that matter) that takes place underwater, but somehow the humans manage to hold their breath for five or ten minutes!
Most of the film recycles and even rips-off elements from the other films that didn't work that well to begin with.  The faux Gothic atmosphere with lots of shadows, strobe lights, and steam all over the place is also tiring. The aliens themselves are now such cartoony embodiments of evil it's hard not to laugh at them, much less be frightened by them.
The "Alien" films are meant to be intense, sci-fi thrillers and there's no doubt that's what "Alein: Resurrection" is too, and I don't object to that principle. What I do object to is how the film constantly tries harder to be intense, but the more it does so, the sillier it seems.
Please visit Chad'z Movie Page @ http://members.aol.com/ChadPolenz - over 180 new and old films reviewed in depth, not just blind ratings and quick capsules. Also, check out The FIRST Shay Astar Web Page @ http://members.aol.com/ChadPolenz/ShayAstar.html
To receive Chad'z movie reviews directly, send e-mail to<EMAIL_ADDRESS>with "subscribe" in the header.
(C) 1997 Chad Polenz
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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Posted on: 2013-10-09 22:50:06.751706
I'm Available: Graphic designer, 7 years experience
* Photoshop, Blender, CSS3, HTML5.
* BA Graphic Design from the Mpls College of Arts and Design.
* Self-starter, detail-oriented, proven time-management skills.


RegEM: Regularized Expectation Maximization
Purpose
What follows is a collection of Matlab modules for
- the estimation of mean values and covariance matrices from incomplete datasets, and
- the imputation of missing values in incomplete datasets.
The modules implement the regularized EM algorithm described in
T. Schneider, 2001: Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values. Journal of Climate, 14, 853-871.
The EM algorithm for Gaussian data is based on iterated linear regression analyses. In the regularized EM algorithm, a regularized estimation method replaces the conditional maximum likelihood estimation of regression parameters in the conventional EM algorithm for Gaussian data. The modules here provide truncated total least squares (with fixed truncation parameter) and ridge regression with generalized cross-validation as regularized estimation methods.
The implementation of the regularized EM algorithm is modular, so that the modules that perform he regularized estimation of regression parameters (e.g., ridge regression and generalized cross-validation) can be exchanged for other regularization methods and other methods of determiningca regularization parameter. Per-Christian Hansen's Regularization Tools contain Matlab modules implementing a collection of regularization methods that can be adapted to fit into the framework of the EM algorithm. The generalized cross-validation modules of the regularized EM algorithm are adapted from Hansen's generalized cross-validation modules.
In the Matlab implementation of the regularized EM algorithm, more emphasis was placed on the modularity of the program code than on computational efficiency. The regularized EM algorithm is currently being developed further under a project funded by the National Science Foundation's Paleo Perspectives on Climate Change program
Installation
The program package consists of several Matlab modules. To install the programs, copy the package (available as a tar.gz-file) into a directory that is accessible by Matlab. Unpack the package using
gunzip imputation.tar.gz
tar -xvf imputation.tar
Starting Matlab and invoking Matlab's online help function
help filename
displays information on the module filename.m
.
Module Descriptions
- CHANGES
- Recent significant changes of the programs.
- center.m
- Centers data by subtracting the mean.
- gcvfctn.m (auxiliary module to gcvridge.m)
- Evaluates generalized cross-validation function.
- gcvridge.m
- Finds minimum of generalized cross-validation function for ridge regression.
- iridge.m
- Computes regression parameters by individual ridge regressions.
- mridge.m
- Computes regression parameters by a multiple ridge regression.
- nancov.m
- Sample covariance matrix of available values in incomplete dataset.
- nanmean.m
- Sample mean of available values in incomplete dataset.
- nanstd.m
- Standard deviation of available values in incomplete dataset.
- nansum.m
- Sum over available values in incomplete dataset.
- pca_truncation_criteria.m
- Computes criteria for truncating principal component analyses
- peigs.m
- Computes positive eigenvalues and corresponding eigenvectors.
- pttls.m
- Computes regression parameters by truncated total least squares.
- regem.m
- Driver module for regularized EM algorithm.
- standardize.m
- Standardizes data by subtracting the mean and scaling with the standard deviation.


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Football fan stretchered out after being hit face by ball during Albion's match against Sheffield Wednesday
The man is set to receive Conor Townsend's shirt after he was hit in the face by the ball during Saturday's match (November 23)
by Charlotte Regen, https://www.birminghammail.co.uk/authors/charlotte-regen/A man had to be 'stretchered' out of a crowd at a football match after he was smacked in the face by the ball when it was smashed by defender Conor Townsend.
It happened on Saturday (November 23) at The Hawthorns, during West Bromwich Albion's game against Sheffield Wednesday.
Thankfully, the fan hadn't suffered any significant injuries and was discharged at the scene, according to the ambulance service.
West Brom latest
This is your round up of all things West Brom; the one-stop shop that will keep you updated on the latest goings on at the club and beyond.
Transfers, injuries, match-days and managers, we’ve got you covered.
We’ll be bringing the very latest on the Baggies throughout the week and around the clock.
Make sure you keep yourself updated with our handy daily catch up...
Must reads...
- Garry Monk can't believe Sheffield Wednesday lost to West Brom
- 'Not since February 23' - Albion roll on as Bilic faces dilemma
- 'I spoke to him' - Every word from Bilic after Baggies stay above Leeds
- Here's how Ahmed Hegazi played on his West Brom comeback
- Things you might have missed as West Brom marched on
- Player ratings as Baggies keep title push rolling
For all your latest WBA news, opinion, analysis and transfer gossip, click here
You can follow our West Brom reporter Joseph Chapman on Twitter, and his email address is<EMAIL_ADDRESS>An eyewitness told Black Country Live: "There was a fan who was stretchered away from the game after being hit in the face by the ball.
"He was in the Halfords Lane stand near the dug outs. A player belted the ball into the crowd during the second half and it hit him flush in the face. Physios from both teams attended and then paramedics arrived."
Now, West Bromwich Albion are organising for the man to receive the responsible player's shirt for his trouble.
A spokesman for West Bromwich Albion said: "He is fine and left the stadium as normal after being attended to. We are organising to get him Conor Townsend’s shirt for his trouble - it was Conor’s clearance that hit him in the face."
A Spokesman for West Midlands Ambulance Service said: "During Saturday's game between West Bromwich Albion and Sheffield Wednesday a member of the crowd was struck in the face the by the ball.
"He was assessed by ambulance staff who were already at the ground and thankfully hadn't suffered any significant injuries and was discharged at the scene."
Keep up-to-date with all things bostin' in the Black Country on our We Love The Black Country Facebook group - where you'll get the latest news, travel, weather and events in your area.
Keep up-to-date with all things Dudley, Walsall, Sandwell and Wolverhampton on our Black Country Live Facebook page - where you'll get the latest news, travel, weather and events in your area.


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Mehanata Bulgarian Bar
113 Ludlow St, New York, NY 10002, United States
Korinya's throwing a village party. Strap on your dancing boots, take a shot and join us at the Mehanata Bulgarian Bar for a night of folk fun!
Triad Theatre
158 W 72nd St, New York, NY 10023, USA
2-drink minimum, $10 cover at the door
Soyuzivka Heritage Center
216 Foordmore Rd, Kerhonkson, NY 12446, USA
Free entry
ZABAVA!
Ukrainian American Citizens' Association
847 N. Franklin Street Philadelphia, PA 19123
$10 entrance at the door, kids under 12 free
Live performance from 12pm-1:30pm. Homemade Ukrainian food and cool refreshments available. Free street and lot parking. Rain or shine!
American Serbian Club
2524 Sarah Street, Pittsburgh Pennsylvania 15203
Tickets $15, children under 12 free
Wilberts Food & Music
812 Huron Rd E, Cleveland, OH 44115, USA
Free entry, suggested donation
Ukrainian American Museum & Library
9630 Joseph Campau St, Hamtramck, MI 48212
$10 general admission, children under 12 free
To arrange ticket purchase from the Museum, call Olga Liss-Liskiwskyi at<PHONE_NUMBER>
Ukrainian Cultural Center
26601 Ryan Rd, Warren, MI 48091
$25 per standard ticket, students $15, and kids under 12 free
Tickets available for purchase in person at the Ukrainian Cultural Center Monday through Saturday, 9am-5pm.
ZABAVA!
Ukrainian Canadian Care Centre
60 Richview Rd, Etobicoke, ON
Charitable concert
DROM Taberna
458 Queen St. W. Toronto, ON
$10 cover at the door


There are many kinds of file systems, varying widely both in their superficial syntactic details, and in their underlying power and structure. The facilities provided by Common Lisp for referring to and manipulating files has been chosen to be compatible with many kinds of file systems, while at the same time minimizing the program-visible differences between kinds of file systems.
Since file systems vary in their conventions for naming files, there are two distinct ways to represent filenames: as namestrings and as pathnames.
19.1.1 Namestrings as Filenames
19.1.3 Parsing Namestrings Into Pathnames


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26 Feb 2014
Gameweek 27 - Home Truths
Not too long ago, an ailing conscience was all the motivation needed to churn out these pieces. Then, one didn’t have to journey out into hitherto unknown nooks and crannies in search of failure - it was right there, perched unashamedly, perpetually ensconced within our midst. Red arrows were not only the norm, they were expected outcomes. Successful gameweeks were met with gnawing fear and trepidation as they only served to lull gaffers into faux comfort. Fast forward to the present and my unprecedented success of the past few gameweeks (engineered no less by my eschewal of erstwhile ways) has me mired in flux. It’s the perfect catch-22 – do I leave the comfort of my present situation and dip back into the old ways? Or do I carry on with the carefree nonchalance of the casual player and allow fate to continue delivering green arrows straight to my doorsteo? It’s a tetchy conundrum.
IMPLICATIONS
Switching gears to the LoF, the big story from the weekend has to be Still Thinking’s Andrew Orina’s ascension to the rarefied airs of the top ten courtesy of a 58 point haul. Having been stuck to the lower reaches of the log for the most part, this has indeed marked a significant about-turn fortune-wise for the defending champion in what has been a stuttering campaign.
Up top, Victoria Concordia gaffer Reuben managed to secure himself a bit of breathing space. His 69 point haul saw him open up a small gap between him and arch-nemesis Onesmus in 2nd place. Vamperoh’s 62 points saw him rise into 4th place at the expense of Excelsia FC, who trickled down into 5th. There was no love lost between Bang Average and Football Love who fought it out all weekend for 7th place; a duel ultimately won by Bang Average under the aegis of Daniel Sturridge. Further afield, Bazinga FC and Superwoman took advantage of the wilting form of other managers to ascend into 10th and 11th repsectively. Superwoman doing so with a barnstorming 91 points, easily the best result of the weekend. In 15th, Die Mannschaft continues to elicit concern over his sudden turn of form. With the business end of the season fast approaching, it remains to be seen whether our resident Teuton can arrest this poor fettle in time to muster a meaningful challenge higher up the log.
A few (VERY) important bits:
- There are confirmed blanks for West Brom, Sunderland, Man City and Man Utd for GW 28.
- Arsenal and Man City’s cup commitments will see them miss out on GW 29. The same applies to the teams they were scheduled to face (Swansea City and Aston Villa).
- Double GW 31 has been confirmed for Liverpool (car, SUN), Sunderland (nor, liv), Hull (WBA, whm) and West Ham (MUN, HUL).
Get those wildcards ready, it’s squeaky bum-time.
- denniskig at 10:10


An old, wise friend of mine offered to share with me the byproduct of an independent lab project since I had never tried a phenethylamine of any sort. My experiences before had been primarily with salvia d., DMT, and psilocybin. I suppose I had high expectations of the 2c's from hearing other folks' tales.
Since I was working with a new compound, I tried it at home first, with water, a bed, a bathroom, good music, and good company close at hand--tools to help with any funk I could find myself in. I've yet to have a freakout and would like to keep it that way!
I'm hypoglycemic and have a hard time with fasting, so I took the 20mg in water as soon as I got up in the morning. I held it sublingually for five minutes before swallowing. In another five minutes, I was edging towards a +1, comparable to a 2-beer buzz.
(T-45min) Beginnings of a mild bodyload: muscle weakness and an empty threat of nausea as the effects intensified. No visuals and not really psychedelic, but my thoughts were slowed and dulled. I walked out to the front porch to sit with my roommate and watch a fog bank roll in just fifty feet over our heads. I ate some cherries, some cheese, and drank a glass of red wine after my stomach stopped worrying. I would've been happier if I hadn't.
(t-1h) My roomate began to talk about his relationship problems. I lent him a sympathetic ear as usual, feeling no special warmth or connection, but grateful not to have to speak. I was beginning to have to concentrate very hard to be able to process my thoughts into comprehensible phrases. Cognition was drunkenly stultified, though I could still push past it to functionality/ +2
(t-1.5h) A few bike-tire track visual patterns briefly appeared and subside. I generally don't experience visuals on anything other than smoked DMT, though.
(t-2h) My thinking suddenly cleared and all visuals have fled--feels like the peak.
(t-3h) Almost at baseline, though a little bit fuzzy and tired. No euphoric or creative afterglow. Feel as though I may have had a minor insight, but can't really remember many details of the past few hours.
Retrospective:
Not a particularly interesting substance in its own right. However, it might add novelty to an otherwise uninspiring activity. Though very mild, the body load simply wasn't worth the initiative it sapped out of me. I'd like either ideas or the freedom to romp about, and this compound didn't inspire either.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Tensō
Tensō is a REST API facade for node.js, designed to simplify the implementation of APIs.
Tensō will handle the serialization & creation of hypermedia links, all you have to do is give it Arrays
or Objects
.
Example
Creating an API with Tensō can be as simple as one statement.
require( "tenso" )( {routes: require( __dirname + "/routes.js" )} );
Creating Routes
Routes are loaded as a module, with each HTTP method as an export, affording a very customizable API server.
Route handlers have the context of the Tensō server, i.e. this
will allow you to send a response with this.respond(req, res, body[, status, headers])
. You can also use res
to res.respond(body[, status, headers])
, res.redirect(url)
, or res.error(status[, Error])
.
The following example will create GET routes that will return an empty Array
at /
, an Error
at /reports/tps
, & a version 4 UUID at /uuid
.
var uuid = require( "keigai" ).util.uuid;
module.exports.get = {
"/": ["/reports", "/uuid"],
"/reports": ["/reports/tps"],
"/reports/tps": function ( req, res ) {
res.error( 785, Error( "TPS Cover Sheet not attached" ) );
},
"/uuid": function ( req, res ) {
res.respond( uuid(), 200, {"cache-control": "no-cache"} );
}
}
Request Helpers
Tensō decorates req
with "helpers" such as req.ip
, & req.parsed
. PATCH
, PUT
, & POST
payloads are available as req.body
. Sessions are available as req.session
when using local
authentication.
Responses
Responses will have a standard shape, and will be utf-8 by default. The result, and hypermedia will be in data
. Hypermedia (pagination, links, etc.) will be in data.link:[ {"uri": "...", "rel": "..."}, ...]
, & pagination will also be present via the Link
HTTP header.
The result will be in data.result
.
{
"data": {{ `null` or `{link: [], result: ?}` }},
"error": {{ `null` or an `Error` stack trace / message }},
"status": {{ HTTP status code }}
}
REST / Hypermedia
Hypermedia is a prerequisite of REST, and is best described by the Richardson Maturity Model. Tensō will automagically paginate Arrays of results, or parse Entity representations for keys that imply
relationships, and create the appropriate Objects in the link
Array, as well as the Link
HTTP header. Object keys that match this pattern: /_(guid|uuid|id|uri|url)$/
will be considered
hypermedia links.
For example, if the key user_id
was found, it would be mapped to /users/:id
with a link rel
of related
.
Tensō will bend the rules of REST when using authentication strategies provided by passport.js, or CSRF if is enabled, because they rely on a session. Session storage is in memory, or Redis. You have the option of a stateless or stateful API.
Cache
Tensō has a robust multi-level cache strategy, starting at the response headers. If a response can be cached, an Etag
will be sent to the Client
, and registered in an Etag LRU cache
which Tensō
uses along with a 'cache compressed asset to disk' strategy, allowing Tensō to stream the last known version of a resource to the next Client
that supports the same compression (gzip or deflate).
Etags
will lazy expire from the cache, to minimize wasted cycles.
Caching can be disabled by setting the cache-control
header to a "private" or "no cache" directive (see the above /uuid
example).
Configuration
This is a sample configuration for Tensō, without authentication or SSL. This would be ideal for development, but not production! Enabling SSL is as easy as providing file paths for the two keys.
{
"auth": /* Optional, see Authentication section */
"cache": 1000, /* Optional, size of Etag LRU cache */
"compress": false, /* Optional, enabled by default, disabled with SSL */
"headers": { ... }, /* Optional, custom headers */
"hostname": "localhost", /* Optional, default is 'localhost' */
"json": 2, /* Optional, default indent for 'pretty' JSON */
"logs": { /* Optional */
"level": "debug",
"stdout": true,
"dtrace": true,
"syslog": true
},
"port": 8000, /* Optional, default is 8000 */
"routes": require( "./routes.js" ), /* Required! */
"session": { /* Optional */
"secret": null,
"store": "memory", /* "memory" or "redis" */
"redis": /* See connect-redis for options */
},
"ssl": { /* Optional */
"cert": null,
"key": null
},
"uid": N /* Optional, system account uid to drop to after starting with elevated privileges to run on a low port */
}
Authentication
The protect
Array is the endpoints that will require authentication. The redirect
String is the end point users will be redirected to upon successfully authenticating, the default is /
.
Sessions are used for non Basic
or Bearer Token
authentication, and will have /login
, /logout
, & custom routes. Redis is supported for session storage.
Multiple authentication strategies can be enabled at once.
Basic Auth
{
"auth": {
"basic": {
"enabled": true,
"list": ["username:password", ...],
},
"protect": ["/"]
}
}
Facebook authentication will create /auth
, /auth/facebook
, & /auth/facebook/callback
routes. auth(accessToken, refreshToken, profile, callback)
must execute callback(err, user)
.
{
"auth": {
"facebook": {
"enabled": true,
"auth": function ( ... ) { ... }, /* Authentication handler, to 'find' or 'create' a User */
"client_id": "", /* Get this from Facebook */
"client_secret": "" /* Get this from Facebook */
},
"protect": ["/private"]
}
}
Google authentication (OpenID) will create /auth
, /auth/google
, & /auth/google/callback
routes. auth(identifier, profile, callback)
must execute callback(err, user)
.
{
"auth": {
"google": {
"enabled": true,
"auth": function ( ... ) { ... }, /* Authentication handler, to 'find' or 'create' a User */
},
"protect": ["/private"]
}
}
LinkedIn authentication will create /auth
, /auth/linkedin
, & /auth/linkedin/callback
routes. auth(token, tokenSecret, profile, callback)
must execute callback(err, user)
.
{
"auth": {
"linkedin": {
"enabled": true,
"auth": function ( ... ) { ... }, /* Authentication handler, to 'find' or 'create' a User */
"client_id": "", /* Get this from LinkedIn */
"client_secret": "", /* Get this from LinkedIn */,
"scope": "" /* Optional, permission scope */
}
"protect": ["/private"]
}
}
Local
Local authentication will create /login
. auth(username, password)
must execute callback(err, user)
.
{
"auth": {
"local": {
"enabled": true,
"auth": function ( ... ) { ... }, /* Authentication handler, to 'find' or 'create' a User */
}
"protect": ["/private"]
}
}
OAuth2
OAuth2 authentication will create /auth
, /auth/oauth2
, & /auth/oauth2/callback
routes. auth(accessToken, refreshToken, profile, callback)
must execute callback(err, user)
.
{
"auth": {
"oauth2": {
"enabled": true,
"auth": function ( ... ) { ... }, /* Authentication handler, to 'find' or 'create' a User */
"auth_url": "", /* Authorization URL */
"token_url": "", /* Token URL */
"client_id": "", /* Get this from authorization server */
"client_secret": "" /* Get this from authorization server */
},
"protect": ["/private"]
}
}
Oauth2 Bearer Token
{
"auth": {
"bearer": {
"enabled": true,
"tokens": ["abc", ...]
},
"protect": ["/"]
}
}
SAML
SAML authentication will create /auth
, /auth/saml
, & /auth/saml/callback
routes. auth(profile, callback)
must execute callback(err, user)
.
Tensō uses passport-saml, for configuration options please visit it's homepage.
{
"auth": {
"saml": {
"enabled": true,
...
},
"protect": ["/private"]
}
}
Twitter authentication will create /auth
, /auth/twitter
, & /auth/twitter/callback
routes. auth(token, tokenSecret, profile, callback)
must execute callback(err, user)
.
{
"auth": {
"twitter": {
"enabled": true,
"auth": function ( ... ) { ... }, /* Authentication handler, to 'find' or 'create' a User */
"consumer_key": "", /* Get this from Twitter */
"consumer_secret": "" /* Get this from Twitter */
},
"protect": ["/private"]
}
}
Sessions
Sessions can use a memory (default) or redis store. Memory will limit your sessions to a single server instance, while redis will allow you to share sessions across a cluster of processes, or machines. To use redis, set the store
property to "redis".
If the session secret
is not provided, a version 4 UUID
will be used.
{
"session" : {
"secret": "my secret",
"store": "redis",
"redis": {
"host": "<IP_ADDRESS>",
"port": 6379
}
}
}
Security
Tensō uses lusca for security as a middleware. Please see it's documentation for how to configure it; each method & argument is a key:value pair for security
.
{
"security": { ... }
}
Compression
Compression is enabled by default, for Clients that support gzip
or deflate
. Compression will be disabled if SSL
is enabled.
Rate Limiting
Rate limiting is controlled by configuration, and is disabled by default. Rate limiting is based on token
, session
, or ip
, depending upon authentication method.
Rate limiting can be overridden by providing an override
function that takes req
& rate
, and must return (a modified) rate
.
{
"rate": {
"enabled": true,
"limit": 450, /* Maximum requests allowed before `reset` */
"reset": 900, /* TTL in seconds */
"status": 429, /* Optional HTTP status */
"message": "Too many requests", /* Optional error message */
"override": function ( req, rate ) { ... } /* Override the default rate limiting */
}
}
Limiting upload size
A 'max byte' limit can be enforced on all routes that handle PATCH
, POST
, & PUT
requests. The default limit is 1 MB (1048576 b).
{
"maxBytes": 5242880
}
Logging
Standard log levels are supported, and are emitted (by configuration) to stdout
& stderr
, & syslog
.
DTrace
DTrace probes can be enabled by configuration (disabled by default). A shell script is available at ./dtrace.sh
to observe the probes.
The last argument for each probe is the nanoseconds it took to execute.
"allowed", "char *", "char *", "char *", "int"
"allows", "char *", "char *", "int"
"compress", "char *", "char *", "int"
"compression", "char *", "int"
"error", "char *", "char *", "int", "char *", "int"
"headers", "int", "int"
"log", "char *", "int", "int", "int"
"proxy", "char *", "char *", "char *", "char *", "int"
"middleware", "char *", "char *", "int"
"request", "char *", "int"
"respond", "char *", "char *", "char *", "int", "int"
"status", "int", "int", "int", "int", "int"
"write", "char *", "char *", "char *", "char *", "int"
License
Copyright (c) 2014 Jason Mulligan
Licensed under the BSD-3 license.


Navigate slideshows on topics dealing with the health and wellness department.
Take quiz slides that reinforce topics learned.
Achieve knowledge by succesfully completing slideshows.


Appkite is responsive design. It works perfectly in all device
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Appkite support all mojor browsers.
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--- layout: blazor title: Blazor Sparklines description: Blazor Sparklines keyword: blazor sparklines,sparkline charts, webassembly blazor sparklines chart ---
Sparklines are used in data intense dashboard to display summary information or trends. They commonly appear in a table or as part of a text paragraph. This unique project uses True Type Sparkline Fonts to render many disfferent types of sparklines and does not require a SVG or Canvas element. The sparkline font is downloaded once and used to display the Sparkline charts in a table or text paragraph multiple times.
Using the dotnet CLI, create a new Blazor project
dotnet new blazorwasm -o BlazorProjectClone the Blazor Sparklines Library
git clone https://github.com/Misfits-Rebels-Outcasts/Blazor-Sparkline.gitAdd a reference from the BlazorProject to the specific Blazor Sparklines Library. Go to the BlazorProject folder, edit the BlazorProject.csproj and add in the following:
<Project...>
.
.
.
<ItemGroup>
<ProjectReference Include="../Blazor-Sparkline/Sparklines/Sparklines.csproj" />
</ItemGroup>
</Project>
If you are using other sparkline charts such as the Column Bars, change the above to "../Blazor-Sparkline/ColumnBars/ColumnBars.csproj".
Edit Index.razor in the Pages folder of the BlazorProject
@using WebAssemblyMan
@page "/"

Hello, world!

Welcome to your new app.
<Sparkline InputData="60,0,16,25,48,45,1,0,6,37,78,79,90,90,91,99,80,0,40,0,0,56"
GenerateText="true" SegmentWidth="30">
</Sparkline>
<SurveyPrompt Title="How is Blazor working for you?" />
@using enables us to use the Blazor Sparklines library within our project. <Sparkline> is the tag where we declare our use of the sparkline component. If you are using other charts such as the Column Bars, change the above to "<ColumnBars InputData="50,30,20" ...><ColumnBars>".
Edit index.html to include the Sparkline Stylesheet.
<!DOCTYPE html>

.
.
.
<link href="_content/Sparklines/styles.css" rel="stylesheet" />

.
.
.

If you are using other sparklines such as the Column Bars, change the above to "<link href="_content/ColumnBars/styles.css" rel="stylesheet" />".
Run the BlazorProject
dotnet runYou should see a web page with the Sparkline Chart.


(Morning Herald, October 22, 1894)
An audience which filled the Lyceum Theatre [Baltimore] from pit to dome assembled last night at the second of the series of meetings held by the Vrooman Brothers in the interest of "Dynamic Religion". Fully 3,000 persons were present. Addresses were made by the Rev. Hiram Vrooman, Rev. Walter Vrooman and Rev. Swarri Vivekananda, the Brahmin High Priest now visiting this city. The speakers of the evening were seated on the stage, the Rev. Vivekananda being an object of particular interest to all. He wore a yellow turban and a red robe tied in at the waste [sic] with a sash of the same color, which added to the Oriental cast of his features and invested him with a peculiar interest. His personality seemed to be the feature of the evening. His address was delivered in an easy, unembarrassed manner, his diction being perfect and his accent similar to that of a cultured member of the Latin race familiar with the English language. He said in part:
THE HIGH PRIEST SPEAKS
"Buddha began to found the religion of India 600 years before the birth of Christ He found the religion of India at that time mainly engaged in eternal discussions upon the nature of the human soul. There was no remedy according to the ideas then prevailing for the cure of religious ills but sacrifices of animals, sacrificial altars and similar methods.
"In the midst of this system a priest [?] was born who was a member of one of the leading families who was the founder of Buddhism. His was, in the first place, not the founding of a new religion, but a movement of reformation. He believed in the good of all. His religion, as formulated by him, consisted of the discovery of three things: First, 'There is an evil'; second, 'What is the cause of this evil?' This he ascribed to the desires of men to be superior to others, an evil that could be cured by unselfishness. Third, 'This evil is curable by becoming unselfish'. Force, he concluded, could not cure it; dirt cannot wash dirt; hate cannot cure hate.
"This was the basis of his religion. So long as society tries to cure human selfishness by laws and institutions whose aim is to force others to do good to their neighbors, nothing can be done. The remedy is not to place trick against trick and force against force. The only remedy is in making unselfish men and women. You may enact laws to cure present evils, but they will be of no avail.
"Buddha found in India too much talking about God and His essence and too little work. He always insisted upon this fundamental truth, that we are to be pure and holy, and that we are to help others to be holy also. He believed that man must go to work and help others; find his soul in others; find his life in others. He believed that in the conjunction of doing good to others is the only good we do ourselves. [sic] He believed that there was always in the world too much theory and too little practice. A dozen Buddhas in India at the present time would do good, and one Buddha in this country would also be beneficial.
"When there is too much doctrine, too much belief in my father's religion, too much rational superstition, a change is needed. Such doctrine produces evil, and a reformation is necessary."
At the conclusion of Mr. Vivekananda's address there was a hearty burst of applause.
(Baltimore American, October 22, 1894)
The Lyceum Theater was crowded to the doors last night at the second meeting of the series conducted by the Vrooman brothers on "Dynamic Religion". Swami Vivekananda, of India, made the principal address. He spoke on the Buddhist religion, and told of the evils which existed among the people of India, at the time of the birth of Buddha. The social inequalities in India, he said, were at that period a thousand times greater than anywhere else in the world. "Six hundred years before Christ," he continued, "the priesthood of India exercised great influence over the minds of the people, and between the upper and nether millstone of intellectuality and learning the people were ground. Buddhism, which is the religion of more than two-third of the human family, was not founded as an entirely new religion, but rather as a reformation which carried off the corruption of the times. Buddha seems to have been the only prophet who did everything for others and absolutely nothing for himself. He gave up his home and all the enjoyments of life to spend his days in search of the medicine for the terrible disease of human misery. In an age when men and priests were discussing the essence of the deity, he discovered what people had overlooked, that misery existed. The cause of evil is our desire to be superior to others and our selfishness. The moment that the world becomes unselfish all evil will vanish. So long as society tries to cure evil by laws and institutions, evil will not be cured. The world has tried this method ineffectually for thousands of years. Force against force never cures, and the only cure for evil is unselfishness. We need to teach people to obey the laws rather than to make more laws. Buddhism was the first missionary religion of the world but it was one of the teachings of Buddhism not to antagonize any other religion. Sects weaken their power for good by making war on each other."


I am Juan Sebastian Barragan Jeronimo, I am from Casanare Colombia I am an environmental Engineer and
currently a systems and computing engineering student at
the University of the Andes
This page is an introduction of myself.
One of my biggest interests on the environmental engineering field is the water quality modeling, One of the well-known model for this is call
Qual2K.
This is a One dimensional river and stream water quality model, that is able to model a horizontal and vertical well-mixed channel. I have use this tool in
several courses an I believe a have a pretty good understanding of it. Once you have a calibrated model of a particular river, stream or creek you will be able
to answer questions like what if a factory discharge certain flow with known characteristics (Oxygen dissolve, nutrients, temperature, DOB ... )
it is possible to estimate the impact downstream.
One thing that drives me crazy about it is that is major component is written in
Fortran that makes me wonder if there is room for improvements,
what definitely needs a improvement is the display of the results, it is not user friendly.
Another field inside environmental engineering that interests me is the Geographic information system (GIS). It is about collecting and displaying the spacial information of a given place. This has a huge kinds of applications in many fields,but in the environmental engineering applications are focus on prevention of disasters and hydrology. Recently I have been able to use drons as tools for data collecting the gif and the pictures I present are a sample of the work that I have done.
Since I first took one class of programing I discover this other passion of mine. I strongly believe that the combination of this two professions I will be able to contribute to the environmental engineering due to the fact that many of its computational tools need to improve the user experience. Therefore, I want to gain good skills programing in Web development, Machine Learning, mobil and desktop applications.


#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import array
from collections import namedtuple
from pyspark import SparkContext, since
from pyspark.rdd import RDD
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc
from pyspark.mllib.util import JavaLoader, JavaSaveable
from pyspark.sql import DataFrame
__all__ = ['MatrixFactorizationModel', 'ALS', 'Rating']
[docs]class Rating(namedtuple("Rating", ["user", "product", "rating"])):
"""
Represents a (user, product, rating) tuple.
>>> r = Rating(1, 2, 5.0)
>>> (r.user, r.product, r.rating)
(1, 2, 5.0)
>>> (r[0], r[1], r[2])
(1, 2, 5.0)
.. versionadded:: 1.2.0
"""
def __reduce__(self):
return Rating, (int(self.user), int(self.product), float(self.rating))
[docs]@inherit_doc
class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader):
"""A matrix factorisation model trained by regularized alternating
least-squares.
>>> r1 = (1, 1, 1.0)
>>> r2 = (1, 2, 2.0)
>>> r3 = (2, 1, 2.0)
>>> ratings = sc.parallelize([r1, r2, r3])
>>> model = ALS.trainImplicit(ratings, 1, seed=10)
>>> model.predict(2, 2)
0.4...
>>> testset = sc.parallelize([(1, 2), (1, 1)])
>>> model = ALS.train(ratings, 2, seed=0)
>>> model.predictAll(testset).collect()
[Rating(user=1, product=1, rating=1.0...), Rating(user=1, product=2, rating=1.9...)]
>>> model = ALS.train(ratings, 4, seed=10)
>>> model.userFeatures().collect()
[(1, array('d', [...])), (2, array('d', [...]))]
>>> model.recommendUsers(1, 2)
[Rating(user=2, product=1, rating=1.9...), Rating(user=1, product=1, rating=1.0...)]
>>> model.recommendProducts(1, 2)
[Rating(user=1, product=2, rating=1.9...), Rating(user=1, product=1, rating=1.0...)]
>>> model.rank
4
>>> first_user = model.userFeatures().take(1)[0]
>>> latents = first_user[1]
>>> len(latents)
4
>>> model.productFeatures().collect()
[(1, array('d', [...])), (2, array('d', [...]))]
>>> first_product = model.productFeatures().take(1)[0]
>>> latents = first_product[1]
>>> len(latents)
4
>>> products_for_users = model.recommendProductsForUsers(1).collect()
>>> len(products_for_users)
2
>>> products_for_users[0]
(1, (Rating(user=1, product=2, rating=...),))
>>> users_for_products = model.recommendUsersForProducts(1).collect()
>>> len(users_for_products)
2
>>> users_for_products[0]
(1, (Rating(user=2, product=1, rating=...),))
>>> model = ALS.train(ratings, 1, nonnegative=True, seed=10)
>>> model.predict(2, 2)
3.73...
>>> df = sqlContext.createDataFrame([Rating(1, 1, 1.0), Rating(1, 2, 2.0), Rating(2, 1, 2.0)])
>>> model = ALS.train(df, 1, nonnegative=True, seed=10)
>>> model.predict(2, 2)
3.73...
>>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10)
>>> model.predict(2, 2)
0.4...
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> model.save(sc, path)
>>> sameModel = MatrixFactorizationModel.load(sc, path)
>>> sameModel.predict(2, 2)
0.4...
>>> sameModel.predictAll(testset).collect()
[Rating(...
>>> from shutil import rmtree
>>> try:
... rmtree(path)
... except OSError:
... pass
.. versionadded:: 0.9.0
"""
[docs] @since("0.9.0")
def predict(self, user, product):
"""
Predicts rating for the given user and product.
"""
return self._java_model.predict(int(user), int(product))
[docs] @since("0.9.0")
def predictAll(self, user_product):
"""
Returns a list of predicted ratings for input user and product
pairs.
"""
assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)"
first = user_product.first()
assert len(first) == 2, "user_product should be RDD of (user, product)"
user_product = user_product.map(lambda u_p: (int(u_p[0]), int(u_p[1])))
return self.call("predict", user_product)
[docs] @since("1.2.0")
def userFeatures(self):
"""
Returns a paired RDD, where the first element is the user and the
second is an array of features corresponding to that user.
"""
return self.call("getUserFeatures").mapValues(lambda v: array.array('d', v))
[docs] @since("1.2.0")
def productFeatures(self):
"""
Returns a paired RDD, where the first element is the product and the
second is an array of features corresponding to that product.
"""
return self.call("getProductFeatures").mapValues(lambda v: array.array('d', v))
[docs] @since("1.4.0")
def recommendUsers(self, product, num):
"""
Recommends the top "num" number of users for a given product and
returns a list of Rating objects sorted by the predicted rating in
descending order.
"""
return list(self.call("recommendUsers", product, num))
[docs] @since("1.4.0")
def recommendProducts(self, user, num):
"""
Recommends the top "num" number of products for a given user and
returns a list of Rating objects sorted by the predicted rating in
descending order.
"""
return list(self.call("recommendProducts", user, num))
[docs] def recommendProductsForUsers(self, num):
"""
Recommends the top "num" number of products for all users. The
number of recommendations returned per user may be less than "num".
"""
return self.call("wrappedRecommendProductsForUsers", num)
[docs] def recommendUsersForProducts(self, num):
"""
Recommends the top "num" number of users for all products. The
number of recommendations returned per product may be less than
"num".
"""
return self.call("wrappedRecommendUsersForProducts", num)
@property
@since("1.4.0")
def rank(self):
"""Rank for the features in this model"""
return self.call("rank")
[docs] @classmethod
@since("1.3.1")
def load(cls, sc, path):
"""Load a model from the given path"""
model = cls._load_java(sc, path)
wrapper = sc._jvm.org.apache.spark.mllib.api.python.MatrixFactorizationModelWrapper(model)
return MatrixFactorizationModel(wrapper)
[docs]class ALS(object):
"""Alternating Least Squares matrix factorization
.. versionadded:: 0.9.0
"""
@classmethod
def _prepare(cls, ratings):
if isinstance(ratings, RDD):
pass
elif isinstance(ratings, DataFrame):
ratings = ratings.rdd
else:
raise TypeError("Ratings should be represented by either an RDD or a DataFrame, "
"but got %s." % type(ratings))
first = ratings.first()
if isinstance(first, Rating):
pass
elif isinstance(first, (tuple, list)):
ratings = ratings.map(lambda x: Rating(*x))
else:
raise TypeError("Expect a Rating or a tuple/list, but got %s." % type(first))
return ratings
[docs] @classmethod
@since("0.9.0")
def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, nonnegative=False,
seed=None):
"""
Train a matrix factorization model given an RDD of ratings by users
for a subset of products. The ratings matrix is approximated as the
product of two lower-rank matrices of a given rank (number of
features). To solve for these features, ALS is run iteratively with
a configurable level of parallelism.
:param ratings:
RDD of `Rating` or (userID, productID, rating) tuple.
:param rank:
Number of features to use (also referred to as the number of latent factors).
:param iterations:
Number of iterations of ALS.
(default: 5)
:param lambda_:
Regularization parameter.
(default: 0.01)
:param blocks:
Number of blocks used to parallelize the computation. A value
of -1 will use an auto-configured number of blocks.
(default: -1)
:param nonnegative:
A value of True will solve least-squares with nonnegativity
constraints.
(default: False)
:param seed:
Random seed for initial matrix factorization model. A value
of None will use system time as the seed.
(default: None)
"""
model = callMLlibFunc("trainALSModel", cls._prepare(ratings), rank, iterations,
lambda_, blocks, nonnegative, seed)
return MatrixFactorizationModel(model)
[docs] @classmethod
@since("0.9.0")
def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01,
nonnegative=False, seed=None):
"""
Train a matrix factorization model given an RDD of 'implicit
preferences' of users for a subset of products. The ratings matrix
is approximated as the product of two lower-rank matrices of a
given rank (number of features). To solve for these features, ALS
is run iteratively with a configurable level of parallelism.
:param ratings:
RDD of `Rating` or (userID, productID, rating) tuple.
:param rank:
Number of features to use (also referred to as the number of latent factors).
:param iterations:
Number of iterations of ALS.
(default: 5)
:param lambda_:
Regularization parameter.
(default: 0.01)
:param blocks:
Number of blocks used to parallelize the computation. A value
of -1 will use an auto-configured number of blocks.
(default: -1)
:param alpha:
A constant used in computing confidence.
(default: 0.01)
:param nonnegative:
A value of True will solve least-squares with nonnegativity
constraints.
(default: False)
:param seed:
Random seed for initial matrix factorization model. A value
of None will use system time as the seed.
(default: None)
"""
model = callMLlibFunc("trainImplicitALSModel", cls._prepare(ratings), rank,
iterations, lambda_, blocks, alpha, nonnegative, seed)
return MatrixFactorizationModel(model)
def _test():
import doctest
import pyspark.mllib.recommendation
from pyspark.sql import SQLContext
globs = pyspark.mllib.recommendation.__dict__.copy()
sc = SparkContext('local[4]', 'PythonTest')
globs['sc'] = sc
globs['sqlContext'] = SQLContext(sc)
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()


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How to install Multi Expression Programming XWindows 7, 8, 10, 11 (64-bit)Just download the program from here: mepx_win64.zip (2.45 MB) unzip the archive and run the mepx.exe application. There is no installation kit. Please remember where you saved it so that you can run it next time. Apple macOS / OSX (64-bit) (version 10.13 or newer; Intel and Apple Silicon)Download the program from here: mepx_macos.zip (4.4 MB) It is .zip archive. Double-click it in Finder. It should be decompressed in the same folder as the zip archive. Open it from there (you should right click the icon and choose Open command). Ubuntu (64-bit) (tested on Ubuntu 18)Download the program from here: mepx.deb (2.23 MB) Install the program with Ubuntu Software Center. If the icons are not shown on buttons, please open a Terminal and run the following command (this will display icons on buttons - which are disabled by default): gsettings set org.gnome.settings-daemon.plugins.xsettings overrides "{'Gtk/ButtonImages': <1>, 'Gtk/MenuImages': <1>}"Test dataTest projects (taken from PROBEN1 and other datasets) can be downloaded from: MEPX test projects on Github. Just download a .xml file and press the Load project button from MEPX to load it. User manual
1. Quick start
2. DataThe analysis is performed on tabular data. Data must be complete (no missing values). There are 3 different types of data: Training, Validation, and Test. The training stage of the algorithm is performed on Training and (optionally on Validation data). At the end of the search process, the best solution (on Validation data - if present, if not on Training data) is tested against Test data. The last value(s) (Target(s) column(s)) on each row is the target (expected output). Test data can be without an output (they may have fewer columns than training data). The symbolic regression and time-series problems may have one output or more than one output. (More than one output is only allowed starting with version 2023.02.16) Data for classification problems can have 1 output (1 column as output) only (but the generated programs for multi-class classification can have multiple outputs - see below). For classification problems, the last column may contain only integer values (2 distinct values for binary classification or more for multi-class classification). Starting with version 2022.7.19, any integer values are accepted as output for classification problems. Previously, only continuous, integer values, starting with 0 were accepted as output for classification problems. It is also possible that the output for classification problems to be loaded from files in one-of-m format. For instance, if the problem has 5 classes, the output consists in 5 binary values, one of them being set to 1 and all others being set to 0 (example: 0 1 0 0 0). Training data is compulsory. The others (validation and test) are optional. Alphanumeric values can be loaded into MEPX. Alphanumeric values must be converted to numerical values before running the analysis. You have several specialized buttons for that:
The user can also scale numerical values to a given interval. 2.1. Loading dataData are loaded from csv, txt or other text files. The user can load data by pressing the Load button from the Training, Validation or Test page. Import data window helps the user to specify which list separator, decimal separators and the number of classes (only for the one-of-m format of the output) Values must be separated by blank space, tab, semicolon (;) or comma (,). Values cannot be separated by multiple different separators (for example comma and space). Consecutive separators are treated as one. If the separators are correct, the data will appear in tabular format, as bellow: For classification data with the output in one-of-m format, the user must specify the correct number of classes. This setting has no effect in the Import data window, but, on the main page, the Target column is computed and displayed correctly: 2.2. Problem descriptionA description of the problem can also be given and it will be saved as part of the project. 3. Parameters3.1. Data typeCan be:
3.2. Problem typeCan be:
Data for time-series can have 1 column or multiple columns. All columns are considered targets for time-series. (Multiple columns are permited only starting with version 2023.02.16.) 3.3. Error (Fitness) functionThe algorithm will try to minimize the error. Symbolic RegressionFor symbolic regression problems, the fitness is:
ClassificationFor classification, problems the fitness is computed in multiple ways depending on the problem or strategy. However what we report, in the resulting tables, is the percentage of incorrectly classified data (the number of incorrectly classified examples divided by the number of examples and multiplied by 100). A classification problem with 2 classes can be solved by selecting either binary classification or multi-class classification. Binary classification uses a threshold for making the distinction between classes. Values less or equal to the threshold are classified as belonging to class 0 and the others are classified as belonging to class 1. In the case of binary classification, the threshold is computed automatically (because of that, binary classification can be slower sometimes). For multi-class classification there are 4 strategies:
Time SeriesFor time series problems, the fitness is computed differently on training, validation and testing. First of all, the training data is transformed to a matrix having (Window Size + 1)*Num Outputs columns and Num Training data - Window Size rows. This is done by considering each consecutive Window Size values as input and the next one as Target. During evolution, the newly obtained matrix is used as training data. The error is computed as a regular Symbolic Regression. At the end of each generation, the best function obtained on Training is applied on the Validation set. We actually want to validate the formula obtained on training. Thus, again we construct a matrix, but in a different way. Instead of considering each consecutive values from Validation as input, we build the matrix dynamically considering the previously predicted values on Validation. Of course, the first row is made from the last Window Size values from Training (as input) and the first value from Validation (as Target). The error between predicted and the Target is computed as in the case of Symbolic Regression. At the end of evolution, the best function from Validation is applied on the Test set, in the same manner as for the Validation set. The first row here is generated by taking the last Window Size values from Validation (or from Training if the Validation is empty). 3.4. Other parameters related to dataIf use validation set is checked then, at each generation, the best individual on Training is run against the Validation data, and the best such individual (from those tested against the validation set) is the output of the program (and will be applied on the Test data). Note that, when the Validation data is used, the output of the MEPX is the best solution on Validation, not the best on Training. During evolution, one may see improvements on Training error, but the final solution might not perform so well on Training because the best for Validation is the final solution. Still, the Validation data is useful to prevent overfitting on Training. It is possible to run the optimization on a smaller set of Training data. In such case, you have to set the Random subset size percent to a value smaller than 100%. The training set is changed after Num generations for which random subset is kept fixed. 3.5. Time series parametersThe formula is trained on the Training set and then it is used to predict values on the Validation set. It is highly recommended to have Validation set, otherwise, the results can be poor due to overfitting on Training. MEPX will internally transform columns of training data into a matrix having (Window size + 1)*Num outputs columns. (the last Num outputs columns are the Targets). After discovering the formula, it can be used either to predict values on the Test set or to predict future values, depending on the Time series mode setting. The user can also specify how many future values to predict. 3.6. Functions (or mathematical operators)Here is the list of operators that can appear in the result. These are classic arithmetic operators +, -, *, ... nothing new here. Only some mathematical functions are available if the data type is integer numbers. Modulus can be used for both real and integer data types. Note that trigonometric operators work with radians. Do not confuse them with genetic operators used by the MEP algorithm. 3.7. ConstantsTo enable constants, one must define a probability greater than 0 for constants. You cannot edit that probability directly, but constants_probability + operators_probability + variables_probability = 1. So if you define a value for probability for operators or variables such that their sum is less than 0, you will get a greater than 0 value for constants. Constants can be user-defined or generated by the program (over a given interval). Generated constants can be kept fixed for all the evolution or they can also evolve. Mutation of constants is done by adding a random value between [-max delta, +max_delta]. 3.8. The algorithmThe data analysis cannot be made instantaneously due to the complexity of the problem. This is why a special algorithm inspired by biology is used. The algorithm works with multiple potential solutions that are modified (hopefully improved) along with a number of generations. MEPX uses a steady state model with multiple subpopulations. Steady-state means that inside one subpopulation, the worst individuals are replaced with newer ones (if the newer are better). At the end of each generation, the best individual in that generation is tested against the Validation set. The best on Validation is retained and tested, at the end of the search process against Test set. Parameters for the algorithm are specified in the below image: The user may specify the number of subpopulations. Each subpopulation will run independently from the others and, after one generation, they will exchange few individuals. Genetic operators (crossover and mutation) are classic ... nothing new here. It is possible to specify how often the variables, operators, and constants should appear in a chromosome. This is done probabilistically. If you want more operators to appear, please increase the operators' probability. More operators mean more complex expressions. The sum of functions (operators) probability, variables probability and constants probability must be 1. The constants' probability is computed automatically as 1 - the sum of the other 2 probabilities. 3.9. RunsRandom seed - It is also possible to specify the initial seed of the first run (consecutive runs will start from the previous seed + 1). Num runs - Usually multiple runs must be performed for computing some statistics. Num threads - Specify the number of processor cores used for running the algorithm. Each subpopulation is run on a thread. This can increase the speed of analysis significantly. If you have a quad-core processor with hyper-threading, you may set the number of threads to 8. For best results make sure that the number of subpopulations is a multiple of the number of threads. 4. ResultsThe following results are displayed: 4.1. ErrorError for the entire training, validation, and test set. In the case of classification problems, the number of incorrectly classified data (in percent) is shown. The number of effective instructions of the best program (in the simplified form) and the running time required to obtained the solution are also displayed in this table.
4.2. ModelObtained value for each data in the training, validation, and test set (also called Model or Output). 4.3. Output and Target graphicallyThe target and the output are represented graphically too. Predictions of future values are also represented graphically if the setting of Time Series mode is Predict. 4.4. Source codeC/C++, Excel VBA, Python or Latex source code of the best solution. This code can be simplified to show only instructions that generate the output (remember that not all genes of a chromosome participate to the solution - these genes are called introns). Note that there is no simplification in the case of multi-class classification. Excel VBA code can be used to create a custom formula in Excel. One can analyze new data directly from Excel with the formula discovered by MEPX. A movie with this feature is here on YouTube. Another movie with univariate time-series predictions in Excel is here on YouTube. Another movie with multivariate time-series formulas loaded in Excel is here on YouTube. 4.4. EvolutionEvolution of the fitness for the best individual in the population, the average fitness of the population and the fitness on the validation set (only from version 2<IP_ADDRESS>). Note that, for classification problems, the fitness is not always computed as the number of incorrectly classified data. 5. Use casesParticular settings for each type of problem are described in this section. 5.1. Symbolic regressionData must be in tabular format. Last columns are the target. Target is compulsory for Training and Validation, but not for Test set. Problem type must be set to Symbolic Regression. 5.2. ClassificationData must be in tabular format. Last column is the target class. Target is compulsory for Training and Validation, but not for Test set. Classes must be represented as integer numbers. Starting with version 2022.7.19, any integer numbers are accepted as target. Previously, the target should have been integer values between 0 and Number of classes - 1. Data can be loaded from files where the target is represented in 1-of-m format (0 0 1 0), but will be transformed into 1 column during import. Problem type must be set to Binary classification or Multi-class classification. 5.3. Time seriesProblem type must be set to Time series. Window size must be set. This parameter could influence the quality of prediction. Predict mode must be set. First try to predict on Test then change to predict new data. If Predict mode is Predict new data then, the Number of predictions must be set. 6. The format of the project filesProjects can be saved/loaded to/from .xml files which is the default format of the MEPX. The xml file has a tree structure. The root node is named project. Inside project we have other nodes like mepx_version, problem_description, algorithm. Inside the algorithm node we have training, validation, test, parameters, operators, results, etc Main structure of the .xml files is the following: <?xml version="1.0"?> <project> <mepx_version> </mepx_version> <problem_description> </problem_description> <algorithm> <training> </training> <validation> </validation> <test> </test> <variables_utilization> </variables_utilization> <parameters> </parameters> <constants> </constants> <operators> </operators> <results> </results> </algorithm> </project> 7. Running from the command lineMEPX can be run in dual mode:
The command line has this form: mepx.exe input.xml output.xml , where input.xml is a valid MEPX project. If the files are not in the same directory, please make sure that you give the full path for them! If you want to stop it earlier, run this command: mepx.exe input.xml output.xml stop_file_name Note: Last parameter is optional and is used only if you want to stop the search process earlier. 8. Embedding the MEPX in your applicationIf you want to embed the program in your Windows application you can create a new process using the CreateProcess function from Windows API. When the process is over you should read the results from the output.xml file. You can check if the process is almost over by checking for the existence of output.xml. The existence of the file does not guarantee that the writing of data is over. You can check of the file writing is over by looking for the </project> tag in the xml file. Or you can simply check if the file can be renamed.
The search process can take a few milliseconds or minutes or hours depending on the data to be analyzed. If you want to stop the search process before it naturally ends, you should create a file (stop_file_name)) which you specified as the last parameter of the command line. The program will check for the existence of that file and will stop the search when that file is found. Make sure that you delete this file if you previously created it with another search! 9. Use MEPX formulas as Excel functions
A movie with this feature is here on YouTube. Version 2021.10.29 or newer is needed to export formulas in VBA for Excel. 10. Reporting problems, bugs, commentsIf you have problems with this program please save the project (by pressing the Save Project button from the main toolbar) and send it to<EMAIL_ADDRESS>|


Tone.Chorus is a stereo chorus effect with feedback composed of a left and right delay with a Tone.LFO applied to the delayTime of each channel. Inspiration from Tuna.js. Read more on the chorus effect on SoundOnSound.
The frequency of the LFO.
The delay of the chorus effect in ms.
The depth of the chorus.
var chorus = new Tone.Chorus(4, 2.5, 0.5);
var synth = new Tone.PolySynth(4, Tone.MonoSynth).connect(chorus);
synth.triggerAttackRelease(["C3","E3","G3"], "8n");
The depth of the effect. A depth of 1 makes the delayTime modulate between 0 and 2*delayTime (centered around the delayTime).
The delayTime in milliseconds of the chorus. A larger delayTime will give a more pronounced effect. Nominal range a delayTime is between 2 and 20ms.
The oscillator type of the LFO.
Amount of stereo spread. When set to 0, both LFO's will be panned centrally. When set to 180, LFO's will be panned hard left and right respectively.
The amount of feedback from the output back into the input of the effect (routed across left and right channels).
The wet control is how much of the effected will pass through to the output. 1 = 100% effected signal, 0 = 100% dry signal.


--- layout: page title: About header-img: "img/about-bg.png" ---
Ony my way to web development - will share what I learn along the way!


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