Technical Annex¶
This section contains technical details of the model.
Naming convention¶
code |
name |
|---|---|
AGR |
Agriculture |
BAR |
Barren land |
BIO |
Biomass |
BLT |
Built-up land |
COA |
Coal |
COM |
Commercial & services |
CP01 |
Barley |
CP02 |
Cereals |
CP03 |
Coffee |
CP04 |
Maize |
CP05 |
Oilseeds |
CP06 |
Other crops |
CP07 |
Pulses |
CP08 |
Sorghum |
CP09 |
Tea |
CP10 |
Teff |
CP11 |
Wheat |
CRU |
Crude oil |
DSL |
Diesel |
ELC |
Electricity |
ELC_EXP |
Electricity exports |
ELC_IMP |
Electricity imports |
ELC_NET |
Net electricity imports |
EVT |
Evapotranspiration |
FOR |
Forests |
GAS |
Gas |
GEO |
Geothermal |
GRC |
Groundwater recharge |
GRS |
Grassland & woodland |
GSL |
Gasoline |
GWT |
Groundwater |
HFO |
HFO |
HYD |
Hydro |
IND |
Industry |
IRR |
Irrigation |
JET |
Aviation fuels |
JFL |
Jet fuel |
KER |
Kerosene |
LND |
Land |
LNG |
LNG |
LPG |
LPG |
LPG |
LPG |
LVS |
Livestock |
LVSL01 |
Cattle |
LVSL02 |
Horses |
LVSL03 |
Donkeys/Mules |
LVSL04 |
Camels |
LVSL05 |
Sheep |
LVSL06 |
Pigs |
LVSL07 |
Chicken |
LVSPRDALL |
Livestock |
MIG |
Mining |
NAP |
Naptha |
NGS |
Natural gas |
OHC |
Oil products |
OIL |
Oil |
OTH |
Other uses |
PCK |
Petroleum coke |
PCK |
Petroleum coke |
PET |
Petroleum products |
PRC |
Precipitation |
PUB |
Public supply |
PWR |
Power sector |
PWR_WAT |
Power sector (consumptive use) |
PWR_WITH |
Power sector (withdrawal) |
REN |
Other renewables |
RES |
Residential |
SOL |
Solar |
SUR |
Surface water run-off |
TLU |
Livestock |
TRA |
Transport |
URN |
Nuclear |
WAS |
MSW |
WAT |
Water |
WND |
Wind |
Sets¶
Name |
Description |
Index |
|---|---|---|
YEAR |
It represents the time frame of the model, it contains all the years to be considered in the study. |
y |
TECHNOLOGY |
It includes any element of the energy system that changes a commodity from one form to another, uses it or supplies it. All system components are set up as a ‘technology’ in OSeMOSYS. As the model is an abstraction, the modeller is free to interpret the role of a technology at will, where relevant. It may for example represent a single real technology (such as a power plant) or can represent a heavily aggregated collection of technologies (such as the stock of several million light bulbs), or may even simply be a ‘dummy technology’, perhaps used for accounting purposes. |
t |
TIMESLICE |
It represents the time split of each modelled year, therefore the time resolution of the model. Common to several energy systems modelling tools (incl. MESSAGE / MARKAL / TIMES), the annual demand is ‘sliced’ into representative fractions of the year. It is necessary to assess times of the year when demand is high separately from times when demand is low, for fuels that are expensive to store. In order to reduce the computation time, these ‘slices’ are often grouped. Thus, the annual demand may be split into aggregate seasons where demand levels are similar (such as ‘summer, winter and intermediate’). Those seasons may be subdivided into aggregate ‘day types’ (such as workdays and weekends), and the day further sub divided (such as into day and night) depending on the level of demand. |
l |
FUEL |
It includes any energy vector, energy service or proxies entering or exiting technologies. These can be aggregate groups, individual flows or artificially separated, depending on the requirements of the analysis. |
f |
EMISSION |
It includes any kind of emission potentially deriving from the operation of the defined technologies. Typical examples would include atmospheric emissions of greenhouse gasses, such as CO2. |
e |
MODE_OF_OPERATION |
It defines the number of modes of operation that the technologies can have. If a technology can have various input or output fuels and it can choose the mix (i.e. any linear combination) of these input or output fuels, each mix can be accounted as a separate mode of operation. For example, a CHP plant may produce heat in one mode of operation and electricity in another. |
m |
REGION |
It sets the regions to be modelled, e.g. different countries. For each of them, the supply-demand balances for all the energy vectors are ensured, including trades with other regions. In some occasions it might be computationally more convenient to model different countries within the same region and differentiate them simply by creating ad hoc fuels and technologies for each of them. |
r |
SEASON |
It gives indication (by successive numerical values) of how many seasons (e.g. winter, intermediate, summer) are accounted for and in which order. |
ls |
This set is needed if storage facilities are included in the model. |
||
DAYTYPE |
It gives indication (by successive numerical values) of how many day types (e.g. workday, weekend) are accounted for and in which order. |
ld |
This set is needed if storage facilities are included in the model. |
||
DAILYTIMEBRACKET |
It gives indication (by successive numerical values) of how many parts the day is split into (e.g. night, morning, afternoon, evening) and in which order these parts are sorted. |
lh |
This set is needed if storage facilities are included in the model. |
||
STORAGE |
It includes storage facilities in the model. |
s |
Parameters¶
Global parameters |
|
|---|---|
YearSplit[l,y] |
Duration of a modelled time slice, expressed as a fraction of the year. The sum of each entry over one modelled year should equal 1. |
DiscountRate[r] |
Region specific value for the discount rate, expressed in decimals (e.g. 0.05) |
DaySplit[lh,y] |
Length of one DailyTimeBracket in one specific day as a fraction of the year (e.g., when distinguishing between days and night: 12h/(24h*365d)). |
Conversionls[l,ls] |
Binary parameter linking one TimeSlice to a certain Season. It has value 0 if the TimeSlice does not pertain to the specific season, 1 if it does. |
Conversionld[ld,l] |
Binary parameter linking one TimeSlice to a certain DayType. It has value 0 if the TimeSlice does not pertain to the specific DayType, 1 if it does. |
Conversionlh[lh,l] |
Binary parameter linking one TimeSlice to a certain DaylyTimeBracket. It has value 0 if the TimeSlice does not pertain to the specific DaylyTimeBracket, 1 if it does. |
DaysInDayType[ls,ld,y] |
Number of days for each day type, within one week (natural number, ranging from 1 to 7) |
TradeRoute[r,rr,f,y] |
Binary parameter defining the links between region r and region rr, to enable or disable trading of a specific commodity. It has value 1 when two regions are linked, 0 otherwise |
DepreciationMethod[r] |
Binary parameter defining the type of depreciation to be applied. It has value 1 for sinking fund depreciation, value 2 for straight-line depreciation. |
Demands |
|
SpecifiedAnnualDemand[r,f,y] |
Total specified demand for the year, linked to a specific ‘time of use’ during the year. |
SpecifiedDemandProfile[r,f,l,y] |
Annual fraction of energy-service or commodity demand that is required in each time slice. For each year, all the defined SpecifiedDemandProfile input values should sum up to 1. |
AccumulatedAnnualDemand[r,f,y] |
Accumulated Demand for a certain commodity in one specific year. It cannot be defined for a commodity if its SpecifiedAnnualDemand for the same year is already defined and vice versa. |
Performance |
|
CapacityToActivityUnit[r,t] |
Conversion factor relating the energy that would be produced when one unit of capacity is fully used in one year. |
AvailabilityFactor[r,t,y] |
Capacity available eachTimeSlice expressed as a fraction of the total installed capacity, with values ranging from 0 to 1. It gives the possibility to account for forced outages. |
OperationalLife[r,t] |
Maximum time a technology can run in the whole year, as a fraction of the year ranging from 0 to 1. It gives the possibility to account for planned outages. |
ResidualCapacity[r,t,y] |
Useful lifetime of a technology, expressed in years. |
InputActivityRatio[r,t,f,m,y] |
Capacity available from before the modelling period. |
OutputActivityRatio[r,t,f,m,y] |
Rate of use of a commodity by a technology, as a ratio of the rate of activity. |
Technology costs |
|
CapitalCost[r,t,y] |
Capital investment cost of a technology, per unit of capacity. |
VariableCost[r,t,m,y] |
Cost of a technology for a given mode of operation (Variable O&M cost), per unit of activity. |
FixedCost[r,t,y] |
Fixed O&M cost of a technology, per unit of capacity. |
Storage |
|
TechnologyToStorage[r,t,s,m] |
Binary parameter linking a technology to the storage facility it charges. It has value 1 if the technology and the storage facility are linked, 0 otherwise. |
TechnologyFromStorage[r,t,s,m] |
Binary parameter linking a storage facility to the technology it feeds. It has value 1 if the technology and the storage facility are linked, 0 otherwise. |
StorageLevelStart[r,s] |
Level of storage at the beginning of first modelled year, in units of activity. |
StorageMaxChargeRate[r,s] |
Maximum charging rate for the storage, in units of activity per year. |
StorageMaxDischargeRate[r,s] |
Maximum discharging rate for the storage, in units of activity per year. |
MinStorageCharge[r,s,y] |
It sets a lower bound to the amount of energy stored, as a fraction of the maximum, with a number reanging between 0 and 1. The storage facility cannot be emptied below this level. |
OperationalLifeStorage[r,s] |
Useful lifetime of the storage facility. |
CapitalCostStorage[r,s,y] |
Binary parameter linking a technology to the storage facility it charges. It has value 0 if the technology and the storage facility are not linked, 1 if they are. |
ResidualStorageCapacity[r,s,y] |
Binary parameter linking a storage facility to the technology it feeds. It has value 0 if the technology and the storage facility are not linked, 1 if they are. |
Capacity constraints |
|
CapacityOfOneTechnologyUnit[r,t,y] |
Capacity of one new unit of a technology. In case the user sets this parameter, the related technology will be installed only in batches of the specified capacity and the problem will turn into a Mixed Integer Linear Problem. |
TotalAnnualMaxCapacity[r,t,y] |
Total maximum existing (residual plus cumulatively installed) capacity allowed for a technology in a specified year. |
TotalAnnualMinCapacity[r,t,y] |
Total minimum existing (residual plus cumulatively installed) capacity allowed for a technology in a specified year. |
Investment constraints |
|
TotalAnnualMaxCapacityInvestment[r,t,y] |
Maximum capacity of a technology, expressed in power units. |
TotalAnnualMinCapacityInvestment[r,t,y] |
Minimum capacity of a technology, expressed in power units. |
Activity constraints |
|
TotalTechnologyAnnualActivityUpperLimit[r,t,y] |
Total maximum level of activity allowed for a technology in one year. |
TotalTechnologyAnnualActivityLowerLimit[r,t,y] |
Total minimum level of activity allowed for a technology in one year. |
TotalTechnologyModelPeriodActivityUpperLimit[r,t] |
Total maximum level of activity allowed for a technology in the entire modelled period. |
TotalTechnologyModelPeriodActivityLowerLimit[r,t] |
Total minimum level of activity allowed for a technology in the entire modelled period. |
Reserve margin |
|
ReserveMarginTagTechnology[r,t,y] |
Binary parameter tagging the technologies that are allowed to contribute to the reserve margin. It has value 1 for the technologies allowed, 0 otherwise. |
ReserveMarginTagFuel[r,f,y] |
Binary parameter tagging the fuels to which the reserve margin applies. It has value 1 if the reserve margin applies to the fuel, 0 otherwise. |
ReserveMargin[r,y] |
Minimum level of the reserve margin required to be provided for all the tagged commodities, by the tagged technologies. If no reserve margin is required, the parameter will have value 1; if, for instance, 20% reserve margin is required, the parameter will have value 1.2. |
RE Generation target |
|
RETagTechnology[r,t,y] |
Binary parameter tagging the renewable technologies that must contribute to reaching the indicated minimum renewable production target. It has value 1 for thetechnologies contributing, 0 otherwise. |
RETagFuel[r,f,y] |
Binary parameter tagging the fuels to which the renewable target applies to. It has value 1 if the target applies, 0 otherwise. |
REMinProductionTarget[r,y] |
Minimum ratio of all renewable commodities tagged in the RETagCommodity parameter, to be produced by the technologies tagged with the RETechnology parameter. |
Emissions |
|
EmissionActivityRatio[r,t,e,m,y] |
Emission factor of a technology per unit of activity, per mode of operation. |
EmissionsPenalty[r,e,y] |
Penalty per unit of emission. |
AnnualExogenousEmission[r,e,y] |
It allows the user to account for additional annual emissions, on top of those computed endogenously by the model (e.g. emissions generated outside the region). |
AnnualEmissionLimit[r,e,y] |
Annual upper limit for a specific emission generated in the whole modelled region. |
ModelPeriodExogenousEmission[r,e] |
It allows the user to account for additional emissions over the entire modelled period, on top of those computed endogenously by the model (e.g. generated outside the region). |
ModelPeriodEmissionLimit[r,e] |
Annual upper limit for a specific emission generated in the whole modelled region, over the entire modelled period. |
Variables¶
Demands |
Unit |
|
RateOfDemand[r,l,f,y]>=0 |
Intermediate variable. It represents the energy that would be demanded in one time slice l if the latter lasted the whole year. It is a function of the parameters SpecifiedAnnualDemand and SpecifiedDemandProfile. |
Energy |
(per year) |
||
Demand[r,l,f,y]>=0 |
Demand for one fuel in one time slice. |
Energy |
Storage |
||
RateOfStorageCharge[r,s,ls,ld,lh,y] |
Intermediate variable. It represents the commodity that would be charged to the storage facility s in one time slice if the latter lasted the whole year. It is a function of the RateOfActivity and the parameter TechnologyToStorage. |
Energy |
(per year) |
||
RateOfStorageDischarge[r,s,ls,ld,lh,y] |
Intermediate variable. It represents the commodity that would be discharged from storage facility s in one time slice if the latter lasted the whole year. It is a function of the RateOfActivity and the parameter TechnologyFromStorage. |
Energy |
(per year) |
||
NetChargeWithinYear[r,s,ls,ld,lh,y] |
Net quantity of commodity charged to storage facility s in year y. It is a function of the RateOfStorageCharge and the RateOfStorageDischarge and it can be negative. |
Energy |
NetChargeWithinDay[r,s,ls,ld,lh,y] |
Net quantity of commodity charged to storage facility s in daytype ld. It is a function of the RateOfStorageCharge and the RateOfStorageDischarge and can be negative. |
Energy |
StorageLevelYearStart[r,s,y]>=0 |
Level of stored commodity in storage facility s in the first time step of year y. |
Energy |
StorageLevelYearFinish[r,s,y]>=0 |
Level of stored commodity in storage facility s in the last time step of year y. |
Energy |
StorageLevelSeasonStart[r,s,ls,y]>=0 |
Level of stored commodity in storage facility s in the first time step of season ls. |
Energy |
StorageLevelDayTypeStart[r,s,ls,ld,y]>=0 |
Level of stored commodity in storage facility s in the first time step of daytype ld. |
Energy |
StorageLevelDayTypeFinish[r,s,ls,ld,y]>=0 |
Level of stored commodity in storage facility s in the last time step of daytype ld. |
Energy |
StorageLowerLimit[r,s,y]>=0 |
Minimum allowed level of stored commodity in storage facility s, as a function of the storage capacity and the user-defined MinStorageCharge ratio. |
Energy |
StorageUpperLimit[r,s,y]>=0 |
Maximum allowed level of stored commodity in storage facility s. It corresponds to the total existing capacity of storage facility s (summing newly installed and pre-existing capacities). |
Energy |
AccumulatedNewStorageCapacity[r,s,y]>=0 |
Cumulative capacity of newly installed storage from the beginning of the time domain to year y. |
Energy |
NewStorageCapacity[r,s,y]>=0 |
Capacity of newly installed storage in year y. |
Energy |
CapitalInvestmentStorage[r,s,y]>=0 |
Undiscounted investment in new capacity for storage facility s. Derived from the NewStorageCapacity and the parameter CapitalCostStorage. |
Monetary units |
DiscountedCapitalInvestmentStorage[r,s,y]>=0 |
Investment in new capacity for storage facility s, discounted through the parameter DiscountRate. |
Monetary units |
SalvageValueStorage[r,s,y]>=0 |
Salvage value of storage facility s in year y, as a function of the parameters OperationalLifeStorage and DepreciationMethod. |
Monetary units |
DiscountedSalvageValueStorage[r,s,y]>=0 |
Salvage value of storage facility s, discounted through the parameter DiscountRate. |
Monetary units |
TotalDiscountedStorageCost[r,s,y]>=0 |
Difference between the discounted capital investment in new storage facilities and the salvage value in year y. |
Monetary units |
Capacity variables |
||
NumberOfNewTechnologyUnits[r,t,y]>=0, integer |
Number of newly installed units of technology t in year y, as a function of the parameter CapacityOfOneTechnologyUnit. |
No unit |
NewCapacity[r,t,y]>=0 |
Newly installed capacity of technology t in year y. |
Power |
AccumulatedNewCapacity[r,t,y]>=0 |
Cumulative newly installed capacity of technology t from the beginning of the time domain to year y. |
Power |
TotalCapacityAnnual[r,t,y]>=0 |
Total existing capacity of technology t in year y (sum of cumulative newly installed and pre-existing capacity). |
Power |
Activity variables |
||
RateOfActivity[r,l,t,m,y] >=0 |
Intermediate variable. It represents the activity of technology t in one mode of operation and in time slice l, if the latter lasted the whole year. |
Energy |
(per year) |
||
RateOfTotalActivity[r,t,l,y] >=0 |
Sum of the RateOfActivity of a technology over the modes of operation. |
Energy |
(per year) |
||
TotalTechnologyAnnualActivity[r,t,y] >=0 |
Total annual activity of technology t. |
Energy |
TotalAnnualTechnologyActivityByMode[r,t,m,y] >=0 |
Annual activity of technology t in mode of operation m. |
Energy |
TotalTechnologyModelPeriodActivity[r,t] |
Sum of the TotalTechnologyAnnualActivity over the years of the modelled period. |
Energy |
RateOfProductionByTechnologyByMode[r,l,t,m,f,y] >=0 |
Intermediate variable. It represents the quantity of fuel f that technology t would produce in one mode of operation and in time slice l, if the latter lasted the whole year. It is a function of the variable RateOfActivity and the parameter OutputActivityRatio. |
Energy |
(per year) |
||
RateOfProductionByTechnology[r,l,t,f,y] >=0 |
Sum of the RateOfProductionByTechnologyByMode over the modes of operation. |
Energy |
(per year) |
||
ProductionByTechnology[r,l,t,f,y] >=0 |
Production of fuel f by technology t in time slice l. |
Energy |
ProductionByTechnologyAnnual[r,t,f,y] >=0 |
Annual production of fuel f by technology t. |
Energy |
RateOfProduction[r,l,f,y] >=0 |
Sum of the RateOfProductionByTechnology over all the technologies. |
Energy |
(per year) |
||
Production[r,l,f,y] >=0 |
Total production of fuel f in time slice l. It is the sum of the ProductionByTechnology over all technologies. |
Energy |
RateOfUseByTechnologyByMode[r,l,t,m,f,y] >=0 |
Intermediate variable. It represents the quantity of fuel f that technology t would use in one mode of operation and in time slice l, if the latter lasted the whole year. It is the function of the variable RateOfActivity and the parameter InputActivityRatio. |
Energy |
(per year) |
||
RateOfUseByTechnology[r,l,t,f,y] >=0 |
Sum of the RateOfUseByTechnologyByMode over the modes of operation. |
Energy |
(per year) |
||
UseByTechnologyAnnual[r,t,f,y] >=0 |
Annual use of fuel f by technology t. |
Energy |
RateOfUse[r,l,f,y] >=0 |
Sum of the RateOfUseByTechnology over all the technologies. |
Energy |
(per year) |
||
UseByTechnology[r,l,t,f,y] >=0 |
Use of fuel f by technology t in time slice l. |
Energy |
Use[r,l,f,y] >=0 |
Total use of fuel f in time slice l. It is the sum of the UseByTechnology over all technologies. |
Energy |
Trade[r,rr,l,f,y] |
Quantity of fuel f traded between region r and rr in time slice l. |
Energy |
TradeAnnual[r,rr,f,y] |
Annual quantity of fuel f traded between region r and rr. It is the sum of the variable Trade over all the time slices. |
Energy |
ProductionAnnual[r,f,y] >=0 |
Total annual production of fuel f. It is the sum of the variable Production over all technologies. |
Energy |
UseAnnual[r,f,y] >=0 |
Total annual use of fuel f. It is the sum of the variable Use over all technologies. |
Energy |
Costing variables |
||
CapitalInvestment[r,t,y] >=0 |
Undiscounted investment in new capacity of technology t. It is a function of the NewCapacity and the parameter CapitalCost. |
Monetary units |
DiscountedCapitalInvestment[r,t,y] >=0 |
Investment in new capacity of technology t, discounted through the parameter DiscountRate. |
Monetary units |
SalvageValue[r,t,y] >=0 |
Salvage value of technology t in year y, as a function of the parameters OperationalLife and DepreciationMethod. |
Monetary units |
DiscountedSalvageValue[r,t,y] >=0 |
Salvage value of technology t, discounted through the parameter DiscountRate. |
Monetary units |
OperatingCost[r,t,y] >=0 |
Undiscounted sum of the annual variable and fixed operating costs of technology t. |
Monetary units |
DiscountedOperatingCost[r,t,y] >=0 |
Annual OperatingCost of technology t, discounted through the parameter DiscountRate. |
Monetary units |
AnnualVariableOperatingCost[r,t,y] >=0 |
Annual variable operating cost of technology t. Derived from the TotalAnnualTechnologyActivityByMode and the parameter VariableCost. |
Monetary units |
AnnualFixedOperatingCost[r,t,y] >=0 |
Annual fixed operating cost of technology t. Derived from the TotalCapacityAnnual and the parameter FixedCost. |
Monetary units |
TotalDiscountedCostByTechnology[r,t,y] >=0 |
Difference between the sum of discounted operating cost / capital cost / emission penalties and the salvage value. |
Monetary units |
TotalDiscountedCost[r,y] >=0 |
Sum of the TotalDiscountedCostByTechnology over all the technologies. |
Monetary units |
ModelPeriodCostByRegion[r] >=0 |
Sum of the TotalDiscountedCost over all modelled years. |
Monetary units |
Reserve margin |
||
TotalCapacityInReserveMargin[r,y] >=0 |
Total available capacity of the technologies required to provide reserve margin. It is derived from the TotalCapacityAnnual and the parameter ReserveMarginTagTechnology. |
Energy |
DemandNeedingReserveMargin[r,l,y] >=0 |
Quantity of fuel produced that is assigned to a target of reserve margin. Derived from the RateOfProduction and the parameter ReserveMarginTagFuel. |
Energy |
RE Generation target |
||
TotalREProductionAnnual[r,y] |
Annual production by all technologies tagged as renewable in the model. Derived from the ProductionByTechnologyAnnual and the parameter RETagTechnology. |
Energy |
RETotalProductionOfTargetFuelAnnual[r,y] |
Annual production of fuels tagged as renewable in the model. Derived from the RateOfProduction and the parameter RETagFuel. |
Energy |
Emissions |
||
AnnualTechnologyEmissionByMode[r,t,e,m,y] >=0 |
Annual emission of agent e by technology t in mode of operation m. Derived from the RateOfActivity and the parameter EmissionActivityRatio. |
Quantity of emission |
AnnualTechnologyEmission[r,t,e,y] >=0 |
Sum of the AnnualTechnologyEmissionByMode over the modes of operation. |
Quantity of emission |
AnnualTechnologyEmissionPenaltyByEmission[r,t,e,y] >=0 |
Undiscounted annual cost of emission e by technology t. It is a function of the AnnualTechnologyEmission and the parameter EmissionPenalty. |
Monetary units |
AnnualTechnologyEmissionsPenalty[r,t,y] >=0 |
Total undiscounted annual cost of all emissions generatedby technology t. Sum of the AnnualTechnologyEmissionPenaltyByEmission over all the emitted agents. |
Monetary units |
DiscountedTechnologyEmissionsPenalty[r,t,y] >=0 |
Annual cost of emissions by technology t, discounted through the DiscountRate. |
Monetary units |
AnnualEmissions[r,e,y] >=0 |
Sum of the AnnualTechnologyEmission over all technologies. |
Quantity of emission |
ModelPeriodEmissions[r,e] >=0 |
Total system emissions of agent e in the model period, accounting for both the emissions by technologies and the user defined ModelPeriodExogenousEmission. |
Quantity of emission |


How fast does my internet need to be for Google Stadia?
by Jade KingInternet speed is definitely one of the biggest obstacles players will face when picking up Google Stadia , with the platform being entirely reliant on whether or not your device is picking up a solid connection.
You’ll need a certain internet speed to accommodate a specific resolution, visual quality and performance – with the crescendo of 4K/60fps being a particularly difficult benchmark to reach. However, it’s more than possible in the right conditions.
Related: Google Stadia Review
What internet speed do I need for Google Stadia?
Google has made the process of determining your Stadia capabilities much easier by releasing an official speed test for the platform. You can access it here, although we’ve included specific resolutions and performance alongside the connection required to reach them below.
- 10Mbps or above: A minimum of 720p resolution with stereo sound targeting 60fps performance
- 20Mbps or above: 1080p video with HDR with 5.1 surround sound targeting 60fps
- 35Mbps or above: 4K video at 60fps with HDR and full surround sound
Obviously, all of the aforementioned targets will fluctuate depending on your internet connection, with image quality, sound and performance changing on a person-by-person basis. If your bandwidth remains steady you should have a fairly consistent experience.
Google Stadia will launch with a total of 22 games including the likes of Final Fantasy 15, Assassin’s Creed Odyssey and Rage 2. Destiny 2: The Collection and Samurai Shodown will be available as part of your Google Stadia Pro subcription – although everything else must be purchased separately. This isn’t the Netflix of games, it’s essentially a new platform with its own digital ecosystem.
Stadia is set to launch on November 19 with its Founder’s and Premiere Edition, a tier which requires a Pro subscription to gain access to Stadia. A free tier will debut in 2020, although Google is yet to share any specifics. Chances are we’ll find out more in the coming months. Our full review will be coming your way soon.
Unlike other sites, we thoroughly review everything we recommend, using industry standard tests to evaluate products. We’ll always tell you what we find. We may get a commission if you buy via our price links. Tell us what you think – email the Editor


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Software developer
I'm a fresh graduate
with a Bachelor's
degree of Information
Technology
Haneen Hashlamoun
Software developer
My name is Haneen Hashlamoun
I have Computer Technology
Diploma
digree , two years
experienced as developer.
Suhaib Okour
Software developer
I'm Suhaib Okour. I studied
English Language at Jordan
University.
I am currently having a course in
software development at LTUC
Odai Sadaqa
Software developer
I'm Odai Sadaqa, I have a
Master degree in computer
science and Machine
Learning
Aseel Mohammad
Software developer
Skilled in managing inventory,
payroll, and human resources.
Holder of a
Bachelor’s degree
in public Administration and
volunteered with the
department of statistics.
Aseel Al Asaad
Software developer
Aseel Alasaad I fresh
graduate , I have Bacholor's
degree Of computer science


Whether you want to snuggle or adventure across an Oregon ridgeline, Roswell will be ready!
Roswell has the spirit of a 2yr old yellow lab but the skills of a national champion field lab. He's the greatest dog in Eugene!
Roswell is a lover of the great outdoors. He will lead you fearlessly through the wilderness outback while also spending time reflecting on feelings and personal growth!
"Roswell has spent a lot of time supervising on construction projects. He's not always easy to work with, but he gets the job done right the first time. Highly recommended!"
"When I first saw Roswell at the national champion field try outs, I was like, oh that pretty face will never do well. Boy was I wrong. Roswell has proven time and time again that he is in fact the best dog in Eugene. Please contact me directly at Nutrition by Hand for more information."
"Roswell has unbounded enthusiasm for life, but he also knows how to rest and recover. He times is efforts perfectly down to the last second so he can be ready for the next dogventure at any moment. This guy is highly recommended for any of your adventuring needs!"


Baby in hospital following Ashbourne incident
by Ann CaseyA baby is being treated in Temple Street Children's Hospital for serious injuries following an incident at a house in Ashbourne yesterday morning.
A man in his 20s was arrested and is currently being detained, following the incident in which a young woman also received minor injuries.
He is expected to be charged with an assault on the woman and to appear at Trim Court later today.


Elon Musk's SpaceX Starship rocket explodes prematurely during testing
When you’re starting out on a new venture, a few premature detonations can be easily forgiven.
So we’re hoping Elon Musk isn’t feeling too embarrassed about the explosion of his Starship – a rocket that’s designed to carry humans to Mars and beyond.
The Mk1 prototype was undergoing a ‘cryogenic pressure test’ at SpaceX’s facilities near the South Texas village of Boca Chica when the spacecraft went kaboom.
Witnesses said the top of the craft shot almost 500 feet into the air and videos show plumes of smoke pouring from the top of the gigantic spaceship.
‘The purpose of today’s test was to pressurise systems to the max, so the outcome was not completely unexpected,’ a SpaceX spokesperson said in a statement.
‘There were no injuries, nor is this a serious setback.’
Musk took to Twitter to tell fans that his firm will now focus its attention on a new Mk3 Starship prototype.
Advertisement Advertisement
‘This had some value as a manufacturing pathfinder, but flight design is quite different,’ he wrote.
Starship was previously known as BFR (Big Falcon Rocket or the Big F***ing Rocket).
The 50 metre-tall Starship will one day fly atop a booster called the Super Heavy and take astronauts to the moon as well as the Red Planet.
Musk hopes to send a Mk3 version of Starship into space next year, propelled by the mega-powerful Super Heavy.
He now believes humanity will be able to found a permanent settlement on the Red Planet in just over three decades.


General Data Placement Process Background
Every object that is written to NooBaa goes through a digestion process which includes the following steps:
- Fragmentation
- Deduplication
- Compression
- Encryption
The output of this process is a set of encrypted chunks.
Each chunk is stored in a manner deemed safe by the system. Chunks will therefore be stored in one of the following ways:
A minimum of three copies of the same chunk on storage nodes, where data redundancy is achieved by storing the same chunk in multiple locations.
A single copy of the chunk in a cloud pool, where data redundancy is achieved based on the cloud vendor's internal redundancy.
Spread Data Placement
When to use it?
- When the system contains multiple pools.
- For buckets that contain data not requiring full site failure redundancy.
Under the hood
- The system will create three copies across the underlying pools for each of the chunks.
- There is no guaranteed chunk distribution between the pools.
- The client will receive an acknowledge once redundant copies have been created.
[Spread Policy Diagram]
Mirror Data Placement
When to use it?
- When the system contains multiple pools.
- For bucket, that retains data requiring full site failure redundancy.
Under the hood
- The system will create three copies on each of the underlying pools for each of the chunks.
- Data will be asynchronously replicated between pools.
- It is guaranteed that each of the chunks will be stored redundantly on each of the underlying pools.
[Mirror Policy Diagram]


This is a small web game for smartphones only.
We used hmtl5, css3 and dart. It´s our first project
in this web sector and we appreciate every constructive
criticism by playing this game. Have Fun!
Fachhochschule Luebeck
University of Applied Sciences
Web technology Project 2017,
by Manuela, Tram & Sören


Domain Name System (DNS) is a system that links the website names to specific IP addresses. The DNS has the responsibility of converting a host name into an IP address and finding it on the network. It simply works as a phonebook for users. However, there are many DNS servers interconnected, considering a single server cannot handle all the requests. These different servers handle requests for different domains such as .org, .com, .net, etc. DNS was created for open public communications. However, due to being open, the system is also vulnerable to cyberattacks.


Please fill out your favorite things!
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Number of staff and roles within the project
| Role within project | Gross employee cost (£) | Rate (£/day) | Days to be spent by all staff at this grade | Total costs |
|---|---|---|---|---|
| £ |
||||
| Total labour costs |
£ |
| Role within project | Gross employee cost | Rate (£/day) | Days to be spent by all staff at this grade | Total costs | Actions |
|---|
| 20% of labour costs | total overhead costs |
|---|---|
| Total overhead costs |
£ |
| Custom administration support costs |
Rate calculated: |
|---|---|
| Total administration support cost |
£ |
| Calculated overheads | Total calculated overheads |
|---|---|
Download the overhead calculation spreadsheet
|
|
| Total cost of overheads as calculated in the spreadsheet |
£ |
You are not currently applying for overhead costs
There is no need to provide any further supporting documentation or calculations. Actual costs can be claimed up to a maximum of this calculated figure.
If you feel your overhead costs are higher than 20% you may calculate a value using an Innovate UK model. The model shows you which types of indirect costs associated with your project you may claim. For support with this option, please contact our Customer Support Service on 0300 321 4357. They will be happy to provide you with the calculation model sheet (together with guidance documentation). Any value claimed under this model will be subject to a review. This will assess the appropriateness of your claim if your grant application is successful.
If you feel your overheads are higher than 20% you may calculate a value using the Innovate UK model in the spreadsheet available below. The model shows you which types of indirect costs associated with your project you may claim. For support with this option, please contact our Customer Support Service on 0300 321 4357. Any value claimed under this model will be subject to a review. This will assess the appropriateness of your claim if your grant application is successful.
The applicant has not uploaded a file.
The applicant has uploaded a completed overhead costs spreadsheet.
description
| Item | Quantity | Cost per item (£) | Total |
|---|---|---|---|
| £ |
|||
| Total materials costs |
£ |
| Item | Quantity | Cost per item (£) | Total | Actions |
|---|
| Item description | New or Existing | Depreciation period (months) | Net present value (£) | Residual value (£) | Utilisation (%) | Net cost |
|---|---|---|---|---|---|---|
|
£ |
||||||
| Total capital usage |
£ |
| Subcontractor name | Country | Role | Cost |
|---|---|---|---|
| Mr Francis Bois | France | Specialist consultant |
£ |
| Total subcontracting costs |
£ |
| Purpose of journey or description of subsistence cost | Number of times | Cost each (£) | Total |
|---|---|---|---|
|
£ |
|||
| Total travel and subsistence |
£ |
| Purpose of journey or description of subsistence cost | Number of times | Cost each (£) | Total | Actions |
|---|
| Description and justification of the cost | Estimated cost (£) |
|---|---|
|
|
|
| Total other costs |
£ |
| Description and justification of the cost | Estimated cost (£) | Actions |
|---|
No other funding
| Source of funding | Date secured | Funding amount |
|---|---|---|
| National rail | 17 April 2016 | |
| Total other funding |
| Source of funding | Date secured | Funding amount (£) | Actions |
|---|


Established in Camberley in 1946, UniBond was originally known for PVA. But we've come along way since those early days. The UniBond range now includes a broad range of products for both trade and DIY use including Grab Adhesives, Sealants, Tiling products, Tapes, Epoxies and Wood Glues. It is this long experience which ensures that UniBond continues to offer quality products and solutions today.


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Meet our FitPal Development Team! The group was created as a part of the coursework Using R for Analystics MGMT590-URA


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Turtles
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Using Binding and Template Directives Supporting tagline
Using Binding and Template Directives
Directives are the most powerful AngularJS feature; they allow you to extend HTML to create the foundation for rich and complex web applications in a way that is naturally expressive.
Why and When to Use Directives
Directives are the signature feature of AngularJS, setting the overall style of AngularJS development and the shape of an AngularJS application.
AngularJS comes with more than 50 built-in directives that provide access to core features that are useful in almost every web application including data binding, form validation, template generation, event handling, and manipulating HTML elements.
Why
Directives expose core AngularJS functionality such as event handling, form validation, and templates. You use custom directives to apply your application features to views.
When
Directives are used throughout an AngularJS application.
Using the Data Binding Directives
The first category of built-in directives is responsible for performing data binding, which is one of the features that elevates AngularJS from a template package into a full-fledged application development framework.
Data binding uses values from the model and inserts them into the HTML document.
The Data Binding Directives
| Directive | Applied As | Description |
| ng-bind | Attribute, class | Binds the innerText property of an HTML element. |
| ng-bind-html | Attribute, class | Creates data bindings using the innerHTML property of an HTML element. This is potentially dangerous because it means that the browser will interpret the content as HTML, rather than content. |
| ng-bind-template | Attribute, class | Similar to the ng-bind directive but allows for multiple template expressions to be specified in the attribute value. |
| ng-model | Attribute, class | Creates a two-way data binding. |
| ng-non-bindable | Attribute, class | Declares a region of content for which data binding will not be performed. |
APPLYING DIRECTIVES
apply directives as attributes:
There are  items
configure directives using the standard class attribute
There are  items
Performing One-Way Bindings (and Preventing Them)
AngularJS supports two kinds of data binding.
The first, one-way binding, means a value is taken from the data model and inserted into an HTML element.
AngularJS bindings are live, which means that when the value associated with the binding is changed in the data model, the HTML element will be updated to display the new value.
The ng-bind directive is responsible for creating one-way data bindings, but it is rarely used directly because AngularJS will also create this kind of binding whenever it encounters the characters in the HTML document.

There are {{todos.length}} items

This is the most natural and expressive way of creating data bindings: The bindings are easy to read and fit naturally into the content of HTML elements.
The second data binding uses the ng-bind directive, which has the same effect but requires an additional element
There are  items
The ng-bind directive replaces the content of the element that it is applied to, which means I have to add a span element to create the effect I want.
I don’t use the ng-bind directive in my own projects; I prefer the inline bindings.
Aside from being a little awkward to use, the ng-bind directive is limited to being able to process a single data binding expression.
If you need to create multiple data bindings, then you should use the ng-bind-template directive

Preventing Inline Data Binding
The drawback of the inline bindings is that AngularJS will find and process every set of characters in your content.
The solution is to use the ng-non-bindable directive, which prevents AngularJS from processing inline bindings.

AngularJS uses {{ and }} characters for templates

Creating Two-Way Data Bindings
Two-way data bindings track changes in both directions, allowing elements that gather data from the user to modify the state of the application.
Two-way bindings are created with the ng-model directive, a single data model property can be used for both one- and two-way bindings.
Two-way bindings can be applied only to elements that allow the user to provide a data value, which means the input, textarea, and select elements.
Changes to data model properties are disseminated to all of the relevant bindings, ensuring that the application is kept in sync.
Using the Template Directives
Data bindings are the core feature of AngularJS views, but on their own they are pretty limited.
Web applications—or any kind of application for that matter—tend to operate on collections of similar data objects and vary the view they present to the user based on different data values.
Fortunately, AngularJS includes a set of directives that can be used to generate HTML elements using templates, making it easy to work with data collections and to add basic logic to a template that responds to the state of the data.
The Template Directives
| Directive | Applied As | Description |
| ng-cloak | Attribute,class | Applies a CSS style that hides inline binding expressions, which can be briefly visible when the document first loads |
| ng-include | Element,Attribute,class | Loads, processes, and inserts a fragment of HTML into the Document Object Model |
| ng-repeat | Attribute,class | Generates new copies of a single element and its contents for each object in an array or property on an object |
| ng-repeat-start | Attribute,class | Denotes the start of a repeating section with multiple top-level elements |
| ng-repeat-end | Attribute,class | Denotes the end of a repeating section with multiple top-level elements |
| ng-switch | Element,Attribute,class | Changes the elements in the Document Object Model based on the value of data bindings |
Generating Elements Repeatedly
One of the most common tasks in any view is to generate the same content for each item in a collection of data.
In AngularJS this is done with the ng-repeat directive, which is applied to the element that should be duplicated.
There are two parts to using the ng-repeat directive.
The first is to specify the source of the data objects and the name by which you want to refer to the object that is being processed from within the template

The basic format of the value for the ng-repeat directive attribute is <variable>
in <source>
, where source is an object or array defined by the controller $scope, in this example the todos array.
The directive iterates through the objects in the array, creates a new instance of the element and its content, and then processes the templates it contains.
The <variable>
name assigned in the directive attribute value can be used to refer to the current data object.

{{item.action}}
{{item.complete}}

In my example, I generate a tr element that contains td elements that, in turn, contain inline data bindings that refer to the action and complete properties of the current object.
If you navigate to the directives.html file in the browser, AngularJS will process the directive and generate the following HTML elements:

Get groceries
false

Call plumber
false

Buy running shoes
true

Buy flowers
false

Call family
false

Repeating for Object Properties
The previous example used the ng-repeat directive to enumerate the objects in an array, but you can also enumerate the properties of an object.

{{prop}}

Working with Data Object Keys
There is an alternative syntax for the ng-repeat directive configuration that allows you to receive a key with each property or data object that is processed.

{{key}}={{value}}

…

action=Get groceries

complete=false

…
Working with the Built-in Variables
The ng-repeat directive assigns the current object or property to the variable you specify, but there is also a set of built-in variables that provide context for the data being processed.
| Variable | Description |
| $index | Returns the position of the current object or property |
| $first | Returns true if the current object is the first in the collection |
| $middle | Returns true if the current object is neither the first nor last in the collection |
| $last | Returns true if the current object is the last in the collection |
| $even | Returns true for the even-numbered objects in a collection |
| $odd | Returns true for the odd-numbered objects in a collection |
Repeating Multiple Top-Level Elements
The ng-repeat directive repeats a single top-level element and its contents for each object or property that it processes.
There are times, however, when you need to repeat multiple top-level elements for each data object.
I encounter this problem most often when I need to generate multiple table rows for each data item that I am processing—something that is difficult to achieve with ng-repeat because no intermediate elements are allowed between tr elements and their parents.

This is item {{$index}}

The action is: {{item.action}}

Item {{$index}} is {{$item.complete? '' : "not "}} complete

The ng-repeat-start directive is configured just like ng-repeat, but it repeats all of the top-level elements (and their contents) until (but including) the element to which the ng-repeat-end attribute has been applied.
Working with Partial Views
The ng-include directive retrieves a fragment of HTML content from the server, compiles it to process any directives that it might contain, and adds it to the Document Object Model.
These fragments are known as partial views.

To Do List

<ng-include src="'table.html'"></ng-include>

The Configuration Parameters of the ng-include Directive
| Name | Description |
| src | Specifies the URL of the content to load |
| onload | Specifies an expression to be evaluated when the content is loaded |
| autoscroll | Specifies whether AngularJS should scroll the viewport when the content is loaded |
Selecting Partial Views Dynamically
angular.module("exampleApp", [])
.controller("defaultCtrl", function ($scope) {
...
$scope.viewFile = function () {
return $scope.showList ? "list.html" : "table.html";
};
});
….
<ng-include src="viewFile()"></ng-include>
Using the ng-include Directive as an Attribute
…

…
The ng-include attribute can be applied to any HTML element, and the value of the src parameter is taken from the attribute value, which is viewFile() in this case.
The other directive configuration parameters are expressed as separate attributes, which you can see with the onload attribute.
Conditionally Swapping Elements
The ng-include directive is excellent for managing more significant fragments of content in partial, but often you need to switch between smaller chucks of content that are already within the document—and for this, AngularJS provides the ng-switch directive.

<input type="radio" ng-model="data.mode"
value="{{button}}" ng-checked="$first" />
{{button}}

#	Action	Done

{{$index + 1}}
{{prop}}

{{item.action}} (Done)

Select another option to display a layout

Hiding Unprocessed Inline Template Binding Expressions
When working with complex content on slow devices, there can be a moment when the browser displays the HTML in the document while AngularJS is still parsing the HTML, processing the directives, and generally getting ready.
A better alternative is to use the ng-cloak directive, which has the effect of hiding content until AngularJS has finished processing it.
The ng-cloak directive uses CSS to hide the elements to which it is applied, and the AngularJS library removes the CSS class when the content has been processed, ensuring that the user never sees the {{ and }} characters of a template expression.
A common approach is to apply the directive to the body element, but that just means that the user sees an empty browser window while AngularJS processes the content.
I prefer to be more selective and *apply the directive only to the parts of the document where there are inline expressions.

<input type="radio" ng-model="data.mode"
value="{{button}}" ng-checked="$first">
{{button}}

#	Action	Done

{{$index + 1}}
{{prop}}

….


Alloy supports the standard boolean operators:
!F // negations: not F
F && G // conjunction: F and G
F || G // disjunction: F or G
It's useful to have a variety of implication operators:
F => G // implication: same as !F || G
G <=> G // bi-implication: same as (F => G) && (G => F)
F => G else H // conditional: if F then G else H; same as (F => G) && (!F=> H)
If you do not write sufficient parentheses, Alloy assumes that
certain operations bind tighter than others. In order from tightest to loosest:
not (!)
and (&&)
implication/conditional (=>)
bi-implication (<=>)
or (||)
Thus, the following pairs of expressions are equivalent
!A && B // (!A) && B
A && B || C // (A && B) || C
A || B && C // A || (B && C)
A => B => C // (A => B) => C
You are permitted to add excess parentheses at your judgement. Thus, A, (A), and ((A)) are all equivalent.
If you write several statements in the curly braces of the body of
a fact,
assert, or quantifier, those statements are implicityly conjoined
(&&). That is,
fact Fibbles {
(no X) && (all y:Y | y in Foo)
}
could be written as
fact Fibbles {
no X
all y:Y | y in Foo
}
and
all x: e | F && G && H
could be written as
all x: e {F G H}


I currently work at Beth Israel Deaconess Medical Center as a ITS Applicaiton Developer for the Emergency Department. I also am a managing partner at Meiday Health. Above is a photo of my cat watching me code.
For Recruiters:
blog (coming soon!)
allison dot alder at gmail dot com


This page demonstrates how the JS API handles executing nested commands and catching expected errors.


Guapler's TorrTorrGo (Onion)
Search Torrminatorr with any words by default in Tor Browser!
- Click the green + in the Search Box
- Add TorrTorrGo (Onion) to your search options
- Set it as Default in Preferences / Search
TorrTorrGo: For Any Words and any Browser
TorrTorrGo (Native): Torrminatorr directly


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| Name | Age | Gender | Breed | Type | Service | Price | Owner Name | Contact Phone |
|---|
| Name | Age | Gender | Breed | Type | Service | Price | Owner Name | Contact Phone |
|---|
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| Tue | 10:00 am - 7:00 pm |
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| Fri | 9:00 am - 5:00 pm |
| Sat | 9:00 am - 5:00 pm |
| Sun | Closed |


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1 12.6.2013 Sl.15 as/mb W.P.4882 (W) of 2007 Bimalendu Ghosh & Ors. -vs- State of West Bengal & Ors. Shri Bikash Mukherjee. ...For the State. Shri N. C. Bihani. ...For the Board. The grievances raised in the petition that the pond in question is being filled up by flying ash. Prayer has been made to restore back the pond situated at Ward No.2 under Budge Budge Municipality, R. S. Dag No.881, Khatian Nos.1599, 1610, 1572 and 1615, Mouza Gashibhukta Nandanpur, J.L.No.8, P.S. Budge Budge in the District of South 24-Paraganas. Prayer has also been made to comply the assurance recorded in the order passed by this Court in W.P.No.7579 (W) of 2001. Further prayer has been made to prohibit the respondents to change the nature and
character of the said pond. Other ancillary prayers have also been made.
It is submitted in the petition that the respondent authorities have violated the provisions of law as embodied under Water Bodies Act, the West Bengal Inland Fisheries Act, 1984, the West Bengal Town and Country (Planning and Development) Act, 1979 and the Water (Prevention and Control of Pollution) Act, 1974. Due to filling of the pond, people of the locality are facing water logging of the roads and appurtenant to the said plot. Within one kilometer of the said pond, there is no other water body available in case of any fire emergency. Hence, the petition has been preferred.
In the order dated 17.1.2003 passed in W.P.No.7579 (W) of 2001, it was stated by the learned Counsel appearing for the State that the Government is in seisin over the matter and 2 F.I.R. has been lodged against the respondent Nos.6 to 9 about the so called illegal filling up of the pond. Since the State Government is already in seisin over the matter, action should be taken in accordance with law.
It appears that the petitioners have preferred representation with the B. L. & L. R. O. for restoration of the pond. In case, the pond has not been restored so far, let it be restored to its original position by the B.L. & L.R.O. within a period of three months from date of receipt of the certified copy of this order.
With the aforesaid direction, the writ petition is disposed of. Urgent photostat certified copy of this order, if applied for, be given to the appearing parties upon compliance of necessary formalities.
(Joymalya Bagchi,J) (Arun Mishra, Chief Justice)


Setting up new version of our software on my box over lunch. Here's hoping it finishes faster than a can of soup.
5/27/2009, 11:45:24 AM
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link--- layout: tweet ---
Setting up new version of our software on my box over lunch. Here's hoping it finishes faster than a can of soup.
5/27/2009, 11:45:24 AM
Favs: 0
Retweets: 0
link


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Hello there , awesome person !


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Blizzard, NetEase win $569k in copyright suit against Chinese mobile publisher
4399 Network found to have violated copyrights of Overwatch with Gunplay Battlefront, Heroes of Warfare
by Rebekah ValentineBlizzard and NetEase have recently won a copyright lawsuit against Xiamen-based publisher 4399 Network, which was found to have copied Overwatch with at least two of its titles.
Shanghai publication Shine reports that a Pudong court ruled in favor of Blizzard and NetEase to the tune of nearly four million yuan ($569,000) in the suit. The majority of that compensation was for a mobile game that Shine refers to as Clash of Fighters, but is very likely the same as mobile MOBA Heroes of Warfare, which Blizzard and NetEase originally sued 4399 over in 2017.
500,000 yuan in compensation was received for the online game Gunplay Battlefront, with a lesser amount agreed upon since the game has been offline since 2017.
Blizzard and NetEase argued that the two games included character designs, gameplay, maps, and other elements that were extremely similar to Overwatch, which NetEase has published in China.


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Smallville
The end of the beginning starts here. Taking flight in its tenth and final season, this modern retelling of a hero's legendary origins continues to blend realism, action, heart and humor as Clark Kent soars toward claiming his birthright. Clark has emerged from his darkest hour, only to find the path to his destiny blocked by ghosts from the past -- shadows in the present tempting Clark toward the darkness at every turn. Despite insurmountable odds, is Clark strong enough to step into the light and claim his rightful place as Earth's mightiest protector?


#
# 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.
#
"""TFRecord sources and sinks."""
from __future__ import absolute_import
import logging
import struct
from apache_beam import coders
from apache_beam.io import filebasedsource
from apache_beam.io import fileio
from apache_beam.io.iobase import Read
from apache_beam.io.iobase import Write
from apache_beam.transforms import PTransform
import crcmod
__all__ = ['ReadFromTFRecord', 'WriteToTFRecord']
def _default_crc32c_fn(value):
"""Calculates crc32c by either snappy or crcmod based on installation."""
if not _default_crc32c_fn.fn:
try:
import snappy # pylint: disable=import-error
_default_crc32c_fn.fn = snappy._crc32c # pylint: disable=protected-access
except ImportError:
logging.warning('Couldn\'t find python-snappy so the implementation of '
'_TFRecordUtil._masked_crc32c is not as fast as it could '
'be.')
_default_crc32c_fn.fn = crcmod.predefined.mkPredefinedCrcFun('crc-32c')
return _default_crc32c_fn.fn(value)
_default_crc32c_fn.fn = None
class _TFRecordUtil(object):
"""Provides basic TFRecord encoding/decoding with consistency checks.
For detailed TFRecord format description see:
https://www.tensorflow.org/versions/master/api_docs/python/python_io.html#tfrecords-format-details
Note that masks and length are represented in LittleEndian order.
"""
@classmethod
def _masked_crc32c(cls, value, crc32c_fn=_default_crc32c_fn):
"""Compute a masked crc32c checksum for a value.
Args:
value: A string for which we compute the crc.
crc32c_fn: A function that can compute a crc32c.
This is a performance hook that also helps with testing. Callers are
not expected to make use of it directly.
Returns:
Masked crc32c checksum.
"""
crc = crc32c_fn(value)
return (((crc >> 15) | (crc << 17)) + 0xa282ead8) & 0xffffffff
@staticmethod
def encoded_num_bytes(record):
"""Return the number of bytes consumed by a record in its encoded form."""
# 16 = 8 (Length) + 4 (crc of length) + 4 (crc of data)
return len(record) + 16
@classmethod
def write_record(cls, file_handle, value):
"""Encode a value as a TFRecord.
Args:
file_handle: The file to write to.
value: A string content of the record.
"""
encoded_length = struct.pack('<Q', len(value))
file_handle.write('{}{}{}{}'.format(
encoded_length,
struct.pack('<I', cls._masked_crc32c(encoded_length)), #
value,
struct.pack('<I', cls._masked_crc32c(value))))
@classmethod
def read_record(cls, file_handle):
"""Read a record from a TFRecords file.
Args:
file_handle: The file to read from.
Returns:
None if EOF is reached; the paylod of the record otherwise.
Raises:
ValueError: If file appears to not be a valid TFRecords file.
"""
buf_length_expected = 12
buf = file_handle.read(buf_length_expected)
if not buf:
return None # EOF Reached.
# Validate all length related payloads.
if len(buf) != buf_length_expected:
raise ValueError('Not a valid TFRecord. Fewer than %d bytes: %s' %
(buf_length_expected, buf.encode('hex')))
length, length_mask_expected = struct.unpack('<QI', buf)
length_mask_actual = cls._masked_crc32c(buf[:8])
if length_mask_actual != length_mask_expected:
raise ValueError('Not a valid TFRecord. Mismatch of length mask: %s' %
buf.encode('hex'))
# Validate all data related payloads.
buf_length_expected = length + 4
buf = file_handle.read(buf_length_expected)
if len(buf) != buf_length_expected:
raise ValueError('Not a valid TFRecord. Fewer than %d bytes: %s' %
(buf_length_expected, buf.encode('hex')))
data, data_mask_expected = struct.unpack('<%dsI' % length, buf)
data_mask_actual = cls._masked_crc32c(data)
if data_mask_actual != data_mask_expected:
raise ValueError('Not a valid TFRecord. Mismatch of data mask: %s' %
buf.encode('hex'))
# All validation checks passed.
return data
class _TFRecordSource(filebasedsource.FileBasedSource):
"""A File source for reading files of TFRecords.
For detailed TFRecords format description see:
https://www.tensorflow.org/versions/master/api_docs/python/python_io.html#tfrecords-format-details
"""
def __init__(self,
file_pattern,
coder,
compression_type):
"""Initialize a TFRecordSource. See ReadFromTFRecord for details."""
super(_TFRecordSource, self).__init__(
file_pattern=file_pattern,
compression_type=compression_type,
splittable=False)
self._coder = coder
def read_records(self, file_name, offset_range_tracker):
if offset_range_tracker.start_position():
raise ValueError('Start position not 0:%s' %
offset_range_tracker.start_position())
current_offset = offset_range_tracker.start_position()
with self.open_file(file_name) as file_handle:
while True:
if not offset_range_tracker.try_claim(current_offset):
raise RuntimeError('Unable to claim position: %s' % current_offset)
record = _TFRecordUtil.read_record(file_handle)
if record is None:
return # Reached EOF
else:
current_offset += _TFRecordUtil.encoded_num_bytes(record)
yield self._coder.decode(record)
[docs]class ReadFromTFRecord(PTransform):
"""Transform for reading TFRecord sources."""
def __init__(self,
file_pattern,
coder=coders.BytesCoder(),
compression_type=fileio.CompressionTypes.AUTO,
**kwargs):
"""Initialize a ReadFromTFRecord transform.
Args:
file_pattern: A file glob pattern to read TFRecords from.
coder: Coder used to decode each record.
compression_type: Used to handle compressed input files. Default value
is CompressionTypes.AUTO, in which case the file_path's extension will
be used to detect the compression.
**kwargs: optional args dictionary. These are passed through to parent
constructor.
Returns:
A ReadFromTFRecord transform object.
"""
super(ReadFromTFRecord, self).__init__(**kwargs)
self._args = (file_pattern, coder, compression_type)
[docs] def expand(self, pvalue):
return pvalue.pipeline | Read(_TFRecordSource(*self._args))
class _TFRecordSink(fileio.FileSink):
"""Sink for writing TFRecords files.
For detailed TFRecord format description see:
https://www.tensorflow.org/versions/master/api_docs/python/python_io.html#tfrecords-format-details
"""
def __init__(self, file_path_prefix, coder, file_name_suffix, num_shards,
shard_name_template, compression_type):
"""Initialize a TFRecordSink. See WriteToTFRecord for details."""
super(_TFRecordSink, self).__init__(
file_path_prefix=file_path_prefix,
coder=coder,
file_name_suffix=file_name_suffix,
num_shards=num_shards,
shard_name_template=shard_name_template,
mime_type='application/octet-stream',
compression_type=compression_type)
def write_encoded_record(self, file_handle, value):
_TFRecordUtil.write_record(file_handle, value)
[docs]class WriteToTFRecord(PTransform):
"""Transform for writing to TFRecord sinks."""
def __init__(self,
file_path_prefix,
coder=coders.BytesCoder(),
file_name_suffix='',
num_shards=0,
shard_name_template=fileio.DEFAULT_SHARD_NAME_TEMPLATE,
compression_type=fileio.CompressionTypes.AUTO,
**kwargs):
"""Initialize WriteToTFRecord transform.
Args:
file_path_prefix: The file path to write to. The files written will begin
with this prefix, followed by a shard identifier (see num_shards), and
end in a common extension, if given by file_name_suffix.
coder: Coder used to encode each record.
file_name_suffix: Suffix for the files written.
num_shards: The number of files (shards) used for output. If not set, the
default value will be used.
shard_name_template: A template string containing placeholders for
the shard number and shard count. Currently only '' and
'-SSSSS-of-NNNNN' are patterns allowed.
When constructing a filename for a particular shard number, the
upper-case letters 'S' and 'N' are replaced with the 0-padded shard
number and shard count respectively. This argument can be '' in which
case it behaves as if num_shards was set to 1 and only one file will be
generated. The default pattern is '-SSSSS-of-NNNNN'.
compression_type: Used to handle compressed output files. Typical value
is CompressionTypes.AUTO, in which case the file_path's extension will
be used to detect the compression.
**kwargs: Optional args dictionary. These are passed through to parent
constructor.
Returns:
A WriteToTFRecord transform object.
"""
super(WriteToTFRecord, self).__init__(**kwargs)
self._args = (file_path_prefix, coder, file_name_suffix, num_shards,
shard_name_template, compression_type)
[docs] def expand(self, pcoll):
return pcoll | Write(_TFRecordSink(*self._args))


--- layout: post title: Building your First App with Xamarin.iOS excerpt: tags: comments: true ---
Over the weekend I was learning how to use Camtasia 2 for Mac and decided I’d produce and share a quick video. The video shows you how to get started using Xamarin.iOS. It is a very simple app but shows Labels Text Fields and Buttons and how to wire them up to work together to produce a working app. After I learn more about Camtasia 2 I will can create additional videos.
Thanks for watching and let me know what types of videos that you would like to see in the future.


| DOSE: T+ 0:00 |
0.5 g |
smoked |
Cannabis - Hash |
(liquid) |
| T+ 0:15 |
8 g |
smoked |
Cytisus scoparius |
(extract) |
I was 15, and I was in a mild depressed state. I had recently overcome obsessive-compulsive disorder, and everyday I looked for something to fill the void. After binging on alcohol and marijuana for basically months, just taking a few days off at a time, I began to notice auditory hallucinations that occured all the time. I would always hear footsteps, people calling my name, people walking around in my yard, my yard's gate being open, and other sounds at times where my parents were at work all day, and no one being in the house at all. A pretty bad mental state I suppose.
Me and my friend had collected scotch broom flowers (active ingredient: cytisus) because we had heard it was a hallucinogen. After trying all methods of smoking it, we felt usually nothing more than a headrush, or a few visual disturbances out of the corner out of our eyes. It made marijuana look like LSD. At this time I was in a better mindset. I had not quite shut off my obsessive-compulsiveness, but I had been progressing with dealing with it, and raising my self-esteem and losing my intense paranoia. Bad things started to happen in my life, and I intensively attacked my mind for having OCD. It eventually just stopped.
 I found some extract that we had made from the broom flowers, and put it in my pipe. All of a sudden, I noticed after every toke I felt less stoned, like a huge sobriety surge. I felt completely sober (which is strange after consuming 1/2 gram of hash oil 15 minutes before). I felt intensely calm, and then the hallucinations started.
I lost depth perception rapidly, everything looked two-dimensional. I looked at things on my floor and stared in wonder. Things began to show emotion and feeling. It was as if I saw behind every object, as if they were as personifed as people. It was rather beautiful, and so was everything in my room. Intense vision trails followed, and everything was just plain weird. No more golden age, just a meaningless mass of stuff. Trash. That sums up the middle part of the experience. I was unable to feel fear for some reason, until a gun floated up to my window and fired. Thats when I began to regain depth perception and intense fear. Meaninglessness turned to meaning. Things started to pop up out of my vision. Jim Morrison was no longer holding a dog. I regained emotion. I became scared. The depth perception was back. I stared at the computer screen until it was all over. I ignored everything. All I felt was fear.
For about half an hour after that I heard random voices. Then I burnt out, and felt nothing but fear. The experience was somber, and intense. A song I can relate it to is Brain Damage by Pink Floyd.
This was a horrible, confusing experience, and it left me no new realisations as I thought hallucinations would. This felt like a complete mental decomposition, and all of it sloppily thrown together again.
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.


Prepare a signed transaction for broadcast.
signed transaction
broadcast mode
a transaction broadcast
Derive a Bech32 address from a public key.
public key bytes
Bech32 human readable part, defaulting to COSMOS_PREFIX
Bech32-encoded address
Derive a keypair from a BIP32 master key.
BIP32 master key
BIP32 derivation path, defaulting to COSMOS_PATH
derived public and private key pair
Derive a BIP32 master key from a mnemonic.
BIP39 mnemonic seed
BIP32 master key
Create a transaction with metadata for signing.
Create a signature from a StdSignMsg
.
transaction with metadata for signing
public and private key pair (or Wallet
)
a signature and corresponding public key
Create signature bytes from a StdSignMsg
.
transaction with metadata for signing
private key bytes
signature bytes
Create a Wallet
from a random mnemonic.
Bech32 human readable part, defaulting to COSMOS_PREFIX
BIP32 derivation path, defaulting to COSMOS_PATH
a keypair and address derived from a random mnemonic
Create a Wallet
from a BIP32 master key.
BIP32 master key
Bech32 human readable part, defaulting to COSMOS_PREFIX
BIP32 derivation path, defaulting to COSMOS_PATH
a keypair and address derived from the provided master key
Create a Wallet
from a known mnemonic.
BIP39 mnemonic seed
Bech32 human readable part, defaulting to COSMOS_PREFIX
BIP32 derivation path, defaulting to COSMOS_PATH
a keypair and address derived from the provided mnemonic
Sign a transaction.
This combines the Tx
and TxMeta
into a StdSignMsg
, signs it,
and attaches the signature to the transaction. If the transaction is already signed, the signature will be
added to the existing signatures.
transaction (signed or unsigned)
metadata for signing
public and private key pair (or Wallet
)
a signed transaction
Verify a StdSignMsg
against a StdSignature
.
transaction with metadata for signing
signature
true
if the signature is valid and matches, false
otherwise
Verify a signature against a StdSignMsg
.
transaction with metadata for signing
signature bytes
public key bytes
true
if the signature is valid and matches, false
otherwise
Verify a StdSignMsg
against multiple StdSignature
s.
transaction with metadata for signing
signatures
true
if all signatures are valid and match, false
otherwise or if no signatures were provided
Generated using TypeDoc
The core API.
Must be asynchronously instantiated.


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Formula Student is the world's biggest competition for engineers. Every year competitions are held all across the globe. Founded by the «Society of Automotive Engineers» in 1981. At present there are more than 600 teams from universities all over the world competing with their self-constructed race cars. The winner is not necessarily the team with the fastest car, but the one with the best package regarding construction, performance, financial planning and sales arguments.
The Competition
Students are given free hand in creating concepts for their respective race cars. The regulations, published every year, primarily set safety standards for the competing vehicles.
Opposed to other racing series, having the fastest car does not guarantee victory in the Formula Student. Teams are evaluated in eight events; five of which are dynamic and three static. Racing performance, deep understanding of engineering theory and cost-related presentations are the focus of the Formula Student. Presentations are evaluated by a judge panel consisting of experts from motorsport, automotive and logistical industries.
Events
A Formula Student competition consists of so-called static and dynamic disciplines. In the static events the engineers have to present their car and their development process to well-known judges from the economy, the automotive industry and prestigious racing series like Formula One. Disciplines being judged are:
ENGINEERING DESIGN:Judgement of technical aspects, the construction and key attributes of the car.
COST:Financial planning of the whole car, including manufacturing.
BUSINESS PLAN:Presentation in order to convince a potential investor of a profitable business idea using the self-developed race car.
The dynamic events reveal the driving performance of the prototypes. Every discipline puts different abilities of the cars to the test:
ACCELERATION:An acceleration race over 75m distance with a standing start.
SKID PAD:Cornering on a track shaped like an eight to test the lateral acceleration of the car.
AUTOCROSS:Qualifying for the Endurance, the fastest lap time wins.
ENDURANCE & EFFICIENCY:An endurance race over 22km distance, including one driver change. The Efficiency scoring rates the consumed amount of energy in relation to the total time.
Further Information
www.formulabharat.comwww.formulastudent.de


Xiaomi introduces the Redmi Note 8 Pro in the new Ocean Blue color
by AbdullahXiaomi introduces the Redmi Note 8 Pro in the new Ocean Blue color
4.3 (85%) 8 votes
Today, almost 3 months after the start of sales of the controversial bestseller Redmi Note 8 Pro, Xiaomi showed off the device in a new Ocean Blue color, confirming recent leaks.
Just a week ago, Xiaomi introduced a new version of Redmi Note 8 Pro for the Chinese market with an increased storage capacity of 8/256 GB (previously two versions were available: 6/64 GB and 8/128 GB). However, it remains unknown whether the blue version of Redmi Note 8 Pro will receive an enlarged storage capacity.
Also, live photos of the smartphone in the new color have appeared on the network, which will complement the three existing colors since the device was released: Shadow Black (black), Gamma Green (green) and Halo White (white).
Redmi Note 8 Pro specifications
- 6.53-inch (2340 × 1080 pixels) Full HD+ display with Corning Gorilla Glass 5 protection
- Octa Core MediaTek Helio G90T 12nm processor (Dual 2.05GHz A76 + Hexa 2GHz A55 CPUs) with 800MHz Mali-G76 3EEMC4 GPU
- 6GB LPPDDR4x RAM with 64GB (UFS 2.1) storage, 6GB / 8GB (LPPDDR4x) RAM with 128GB (UFS 2.1) storage, expandable memory up to 256GB with microSD
- Dual SIM (nano + nano + microSD)
- Android 9.0 (Pie) with MIUI 10, upgradable to MIUI 11
- 64MP rear camera with 1/1.72″ Samsung GW1 sensor, 0.8μm pixel size, f/1.8 aperture, LED flash, EIS, 8MP 120° ultra-wide angle lens with 1.12μm pixel size, 2MP depth sensor and 2MP camera for 2cm macro with 1.75μm pixel size, f/2.4 aperture
- 20MP front camera with f/2.0 aperture
- Fingerprint sensor, IR sensor
- 3.5mm audio jack, FM Radio
- Built-in Alexa with dual wake word
- Splash resistant (IP52)
- Dimensions: 161.37×76.4×8.79mm; Weight: 199.8g
- Dual 4G VoLTE, WiFi 802.11 ac (2.4GHz + 5GHz), Bluetooth 5, GPS + GLONASS, USB Type-C
- 4500mAh battery with 18W fast charging


Beautiful Flicker: Documentation¶
Indices and tables¶
Waveform¶
Handles Flicker Waveform Data
The class and methods contained herein allow you to generate flicker waveforms and perform relevant calculations, including flicker index, frequency, and percent. Plots can also be generated directly from the waveform.
It is recommended to simply create instances of the Waveform class, as opposed to using methods outside the class, as this will automatically perform all necessary calculations on initialization. In addition, class methods are easily callable (e.g. Waveform.plot(), Waveform.get_frequency())
The class is:
- Waveform - A class used to represent a flicker waveform
The functions are:
- import_waveform_csv - Imports a waveform from a CSV file, typically produced by an oscilloscope
- denoise - Applies the Savitzky-Golay Filter to remove noise
- framerate - Gets the frame rate (samples per second) of the data
- find_nearest_idx - Finds the index of the nearest value in an array
- find_nearest_idx_rising - Finds the closest rising-edge value in a 1D array
- frequency - Calculates the dominant frequency of the waveform
- percent_flicker - Computes the flicker percentage of the waveform
- flicker_index - Gets the flicker index of the waveform
- n_periods - Truncates a waveform to n periods
-
class
src.waveform.
Waveform
(filename: str, name: str, remove_noise: bool = True)[source]¶ A class used to represent a flicker waveform
This class should be used in lieu of the other functions in this file, as all necessary computations are completed in initialization (waveform import from CSV) and class methods are easily callable (e.g. Waveform.plot(), Waveform.get_frequency())
- name : str
- The name of the waveform. Use this to keep track of multiple waveforms and for plotting
- data : ndarray
- The 2D array holding waveform data. Format is [time(seconds):float, voltage:float]
- denoised : bool
- Whether or not the waveform has been filtered to remove noise
- framerate : int
- The number of samples per second of the waveform
- v_max : float
- The maximum voltage
- v_min : float
- The minimum voltage
- v_avg : float
- The average voltage (mean of v_max and v_min)
- v_pp : float
- The peak-to-peak voltage (v_max - v_min)
- frequency : float
- The dominant flicker frequency, in Hertz
- period : float
- The period (1 / frequency), in seconds
- one_period : ndarray
- A 2D array containing just one period of the waveform. Format is [time(seconds):float, voltage:float]
- flicker_index : float
- The flicker index of the waveform
- percent_flicker : float
- The percent flicker of the waveform
- get_name()
- Gets the name of this waveform instance
- get_data()
- Gets the 2D array containing waveform data
- get_framerate()
- Gets the framerate (samples per second) of the data
- get_denoised()
- Whether this waveform has been filtered for noise
- get_v_max(rounded=True, digits=2)
- Gets the maximum voltage of this waveform instance
- get_v_min(rounded=True, digits=2)
- Gets the minimum voltage of this waveform instance
- get_v_pp(rounded=True, digits=2)
- Gets the peak-to-peak voltage of this waveform instance
- get_v_avg(rounded=True, digits=2)
- Gets the average voltage of this waveform instance
- get_frequency(rounded=True, digits=1)
- Gets the flicker frequency of this waveform instance
- get_period()
- Gets the period of one oscillation of this waveform instance
- get_one_period()
- Gets the 2D array containing one period of the waveform data
- get_n_periods(num_periods=1)
- Gets the 2D array containing the specified number of periods in the waveform data
- get_percent_flicker(rounded=True, digits=1)
- Gets the percent flicker of this instance of the waveform
- get_flicker_index(rounded=True, digits=1)
- Gets the flicker index of this instance of the waveform
- plot(num_periods=None, filename=None, showstats=True, fullheight=False, figsize=(8,4))
- Plots the time-series waveform graphic
- get_summary(verbose=False, format=’String’, rounded=True)
- Returns a summary of the parameters of this waveform instance
-
get_data
() → numpy.ndarray[source]¶ Gets the 2D array containing waveform data
- ndarray
- The 2D array holding waveform data. Format is [time(seconds):float, voltage:float]
-
get_denoised
() → bool[source]¶ Whether this waveform has been filtered for noise
- bool
- True if the waveform has been denoised, False if not
-
get_flicker_index
(rounded: bool = True, digits: int = 1) → float[source]¶ Gets the flicker index of this instance of the waveform
- rounded : bool
- If True (default), will round the output If False, will not round the output
- digits : int
- The number of decimal points to round to
- float
- The flicker index
-
get_framerate
() → int[source]¶ Gets the framerate (samples per second) of the data
- int
- The number of samples per second of the waveform
-
get_frequency
(rounded: bool = True, digits: int = 1) → float[source]¶ Gets the flicker frequency of this waveform instance
- rounded : bool
- If True (default), will round the output If False, will not round the output
- digits : int
- The number of decimal points to round to
- float
- The flicker frequency in Hertz
-
get_n_periods
(num_periods: int = 1) → numpy.ndarray[source]¶ Gets the 2D array containing the specified number of periods in the waveform data
- num_periods : int
- The number of periods to return. NOTE: Must be fewer than the number of periods in the input data
- ndarray
- The 2D array holding the specified number of periods of the waveform data. Format is [time(seconds):float, voltage:float]
-
get_name
() → str[source]¶ Gets the name of this waveform instance
- str
- The name of this waveform instance
-
get_one_period
() → numpy.ndarray[source]¶ Gets the 2D array containing one period of the waveform data
- ndarray
- The 2D array holding one period of the waveform. Format is [time(seconds):float, voltage:float]
-
get_percent_flicker
(rounded: bool = True, digits: int = 1) → float[source]¶ Gets the percent flicker of this instance of the waveform
- rounded : bool
- If True (default), will round the output If False, will not round the output
- digits : int
- The number of decimal points to round to
- float
- The percent flicker (will be in the format XX.XXXX, NOT 0.XXXXXX)
-
get_period
() → float[source]¶ Gets the period of one oscillation of this waveform instance
- float
- The period in seconds
-
get_summary
(verbose: bool = False, format: str = 'String', rounded: bool = True)[source]¶ Returns a summary of the parameters of this waveform instance
- verbose : bool
- If False, will display:
- Frequency, Percent Flicker, Flicker Index (and Name for dict format)
- If True, will display all of the above, plus:
- Period, Frame Rate, V_min, V_max, V_avg, V_pp
- format : str
- The format of the output, either ‘String’ or ‘Dict’
- rounded : bool
- If True (default), will round the output values If False, will not round the output values
Returns: str or dict
If format = ‘String’: A String summarizing the waveform If format = ‘Dict’: A dict summarizing the waveform
-
get_v_avg
(rounded: bool = True, digits: int = 2) → float[source]¶ Gets the average voltage of this waveform instance
Average voltage is the mean of v_max and v_min
- rounded : bool
- If True (default), will round the output If False, will not round the output
- digits : int
- The number of decimal points to round to
- float
- The average voltage
-
get_v_max
(rounded: bool = True, digits: int = 2) → float[source]¶ Gets the maximum voltage of this waveform instance
- rounded : bool
- If True (default), will round the output If False, will not round the output
- digits : int
- The number of decimal points to round to
- float
- The max voltage
-
get_v_min
(rounded: bool = True, digits: int = 2) → float[source]¶ Gets the minimum voltage of this waveform instance
- rounded : bool
- If True (default), will round the output If False, will not round the output
- digits : int
- The number of decimal points to round to
- float
- The min voltage
-
get_v_pp
(rounded: bool = True, digits: int = 2) → float[source]¶ Gets the peak-to-peak voltage of this waveform instance
Peak-to-peak voltage is v_max - v-min
- rounded : bool
- If True (default), will round the output If False, will not round the output
- digits : int
- The number of decimal points to round to
- float
- The peak-to-peak voltage
-
plot
(num_periods: int = None, filename: str = None, showstats: bool = True, fullheight: bool = False, figsize: tuple = (8, 4))[source]¶ Plots the time-series waveform graphic
- num_periods : int or None
- The number of periods to plot. If None, will show the whole waveform
- filename : str or None
- If specified, will save the file in the specified location. e.g.: filename=’../out/this_graph.png’ If None, the plot will not be saved
- showstats : bool
- If True, will show the flicker frequency, percent, and index on the plot
- fullheight : bool
- If True, will set the Y axis limits from 0 to 1. If False, will set the limits from v_min to 1
- figsize : tuple
- The (x,y) size of the figure
-
src.waveform.
denoise
(data: numpy.ndarray, window_length: int = 1001) → numpy.ndarray[source]¶ Applies the Savitzky-Golay Filter to remove noise
- data : ndarray
- The waveform data as a 2D array
- window_length : int
- The window length for the filter. Higher equals more smoothing
- ndarray
- The waveform with noise removed
-
src.waveform.
find_nearest_idx
(array: numpy.ndarray, value: float) → int[source]¶ Finds the index of the nearest value in an array
- array : ndarray
- A 1D array to search
- value : float
- The closest value to search for in the array
- int
- The first index of the array value that is closest to the search value
-
src.waveform.
find_nearest_idx_rising
(array: numpy.ndarray, value: float) → int[source]¶ Finds the closest rising-edge value in a 1D array
This function is primarily used to find the start of a period. It recursively calls find_nearest_index()
- array : ndarray
- A 1D array to search
- value : float
- The closest value to search for in the array
- int
- The first index of the array value that is both closest to the search value and on a rising edge
-
src.waveform.
flicker_index
(one_period: numpy.ndarray, v_avg: float) → float[source]¶ Gets the flicker index of the waveform
- one_period : ndarray
- One period of the waveform as a 2D array
- v_avg : float
- The average voltage
- float
- The flicker index
-
src.waveform.
framerate
(data: numpy.ndarray) → int[source]¶ Gets the frame rate (samples per second) of the data
- data : ndarray
- The waveform data as a 2D array
- int
- The number of samples per second in the data
-
src.waveform.
frequency
(data: numpy.ndarray, framerate: int, v_avg: float) → float[source]¶ Calculates the dominant frequency of the waveform
Uses the zero-crossing method for fast and accurate frequency calculation
- data : ndarray
- The waveform data as a 2D array
- framerate : int
- The frame rate (samples per second)
- v_avg : float
- The average voltage
- float
- The frequency in Hertz
-
src.waveform.
import_waveform_csv
(filename: str) → numpy.ndarray[source]¶ Imports a waveform from a CSV file, typically produced by an oscilloscope
NOTE: Format should be [time(seconds), volts] and header info should be removed
- filename : str
- The name of the CSV file
- ndarray
- A 2D numpy array in the format [time(seconds), volts]
-
src.waveform.
n_periods
(data: numpy.ndarray, v_avg: float, period: float, num_periods: int = 1) → numpy.ndarray[source]¶ Truncates a waveform to n periods
NOTE: The number of periods must be shorter than the input waveform
- data : ndarray
- The waveform data as a 2D array
- v_avg : float
- The average voltage
- period : float
- The period in seconds
- num_periods : int
- The number of periods to return
- ndarray
- The waveform truncated to the specified number of periods
Plot¶
Flicker Plotting Functions
These functions generate commonly-used flicker graphics.
The functions are:
- ieee_par_1789_graph - Plots the IEEE PAR 1789 logarithmic graph
- waveform_graph - Plots the time-domain flicker waveform
-
src.plot.
ieee_par_1789_graph
(data, figsize: tuple = (8, 4), filename: str = None, showred: bool = True, showyellow: bool = True, noriskcolor: bool = True, max_freq: int = 3000, min_pct: float = 0.1, suppress: bool = False)[source]¶ Plots the IEEE PAR 1789 logarithmic graph
- data : list
- The data points as a list of tuples as [(freq, mod, ‘Name’)], where mod is <= 1
- figsize : tuple
- The (width,height) of the plotted figure
- filename : str or None
- If specified, will save plot as the specified filename, e.g.: filename=’../out/this_graph.png’
- showred : bool
- Whether to show the unsafe region in red
- showyellow : bool
- Whether to show the low-risk region in yellow
- noriskcolor : bool
- If False, no risk region will show in gray. If True, will show in green
- max_freq : int
- The maximum frequency, in kHz
- min_pct : float
- The minimum percent to display
- suppress : bool
- If True, the plot will not be shown
-
src.plot.
waveform_graph
(waveform, figsize: tuple = (8, 4), suppress: bool = False, filename: str = None, showstats: bool = True, num_periods: int = None, fullheight: bool = False)[source]¶ Plots the time-domain flicker waveform
- waveform : Waveform
- The waveform object
- figsize : tuple
- The (horizontal, vertical) figure size
- suppress : bool
- If True, will not run plt.show()
- filename : str or None
- If specified, will save the file named as such, e.g.: filename=’../out/this_graph.png’
- showstats : bool
- If True, will show the flicker frequency, percent, and index on the bottom left of the graph
- num_periods : int or None
- If not None, will truncate the graph to the specified number of periods. NOTE: Must be less than the number of periods present in the data
- fullheight : bool
- If True, will set the bottom y limit to zero. If False, will display the non-scaled waveform starting from v_min
Utils¶
Miscellaneous Utilities
Helper functions.
Currently, the only function is:
- round_output - Function to round floats on return
-
src.utils.
round_output
(value: float, toround: bool = True, digits: int = 2)[source]¶ Function to round floats on return
Wrap return values with this function to easily round output. e.g. To round to one decimal place: return round_output(return_value, toround=True, digits=1)
- value : float
- The value to round
- toround : bool
- Whether or not to round
- digits : int or None
- The number of digits to round to. To round to a whole number (int), set digits to None or 0
- float or int
- If toround=True: The input value, rounded to an int or float If toround=False: The input value, not rounded


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Welcome to hexagonal’s documentation!¶
hexagonal is an LMS (library management system) build as a first year bachelors’ project at Innopolis University.
Class diagram can be found at hexagonal.docs.hexagonal-classes.uml. Also a
rendered version
.
It should also be noted that as a dynamic by nature language, Python doesn’t have native support for class attribute documentation. This is also a bad practice, because it hurts readability of code.