Class CardDataBuilder
Namespace: Trainworks.Builders
Assembly: TrainworksModdingTools.dll
Syntax
public class CardDataBuilder : Object
Constructors
CardDataBuilder()
Declaration
public CardDataBuilder()
Fields
cardID
Don't set directly; use CardID instead. Unique string used to store and retrieve the card data.
Declaration
public string cardID
Field Value
| Type | Description |
|---|---|
| System.String |
Properties
AssetPath
Custom asset path to load from. Must be inside the BaseAssetPath.
Declaration
public string AssetPath { get; set; }
Property Value
| Type | Description |
|---|---|
| System.String |
BaseAssetPath
Set automatically in the constructor. Base asset path, usually the plugin directory.
Declaration
public string BaseAssetPath { get; set; }
Property Value
| Type | Description |
|---|---|
| System.String |
BundleLoadingInfo
Loading Info for loading a card's sprite from an asset bundle.
Declaration
public BundleAssetLoadingInfo BundleLoadingInfo { get; set; }
Property Value
| Type | Description |
|---|---|
| BundleAssetLoadingInfo |
CardArtPrefabVariantRef
Use an existing base game card's art by filling this in with the appropriate card's asset reference information.
Declaration
public AssetReferenceGameObject CardArtPrefabVariantRef { get; set; }
Property Value
| Type | Description |
|---|---|
| UnityEngine.AddressableAssets.AssetReferenceGameObject |
CardArtPrefabVariantRefBuilder
Set CardArtPrefabVariantRef without reflection. The Build() method recursively builds all nested builders.
Declaration
public AssetRefBuilder CardArtPrefabVariantRefBuilder { get; set; }
Property Value
| Type | Description |
|---|---|
| AssetRefBuilder |
CardID
Unique string used to store and retrieve the card data. Implicitly sets NameKey and OverrideDescriptionKey if null
Declaration
public string CardID { get; set; }
Property Value
| Type | Description |
|---|---|
| System.String |
CardLoreTooltipKeys
Use an existing base game card's lore tooltip by adding its key to this list.
Declaration
public List<string> CardLoreTooltipKeys { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<System.String> |
CardPoolIDs
The IDs of all card pools the card should be inserted into.
Declaration
public List<string> CardPoolIDs { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<System.String> |
CardType
The type of card: Spell, Monster, Blight, Scourge, or Invalid.
Declaration
public CardType CardType { get; set; }
Property Value
| Type | Description |
|---|---|
| CardType |
ClanID
ID of the clan the card is a part of. Leave null for clanless. Base game clan IDs should be retrieved via helper class "MTClanIDs".
Declaration
public string ClanID { get; set; }
Property Value
| Type | Description |
|---|---|
| System.String |
Cost
Ember cost of the card.
Declaration
public int Cost { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Int32 |
CostType
Determines whether the card has a normal ember cost or an X ember cost.
Declaration
public CardData.CostType CostType { get; set; }
Property Value
| Type | Description |
|---|---|
| CardData.CostType |
Description
Custom description text appended to the end of the card. Overridden by the OverrideDescriptionKey field.
Declaration
public string Description { get; set; }
Property Value
| Type | Description |
|---|---|
| System.String |
EffectBuilders
Append to this list to add new card effects. The Build() method recursively builds all nested builders.
Declaration
public List<CardEffectDataBuilder> EffectBuilders { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<CardEffectDataBuilder> |
Effects
List of pre-built card effects.
Declaration
public List<CardEffectData> Effects { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<CardEffectData> |
EffectTriggerBuilders
Append to this list to add new character triggers. The Build() method recursively builds all nested builders.
Declaration
public List<CharacterTriggerDataBuilder> EffectTriggerBuilders { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<CharacterTriggerDataBuilder> |
EffectTriggers
List of pre-built character triggers.
Declaration
public List<CharacterTriggerData> EffectTriggers { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<CharacterTriggerData> |
FallbackData
In the event that art assets cannot be found, the game will search this for backup assets.
Declaration
public FallbackData FallbackData { get; set; }
Property Value
| Type | Description |
|---|---|
| FallbackData |
FullAssetPath
The full, absolute path to the asset. Concatenates BaseAssetPath and AssetPath.
Declaration
public string FullAssetPath { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
IgnoreWhenCountingMastery
Whether or not this card is displayed in the logbook when counting all of the player's mastered cards.
Declaration
public bool IgnoreWhenCountingMastery { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
LinkedClass
The class associated with the card.
Declaration
public ClassData LinkedClass { get; set; }
Property Value
| Type | Description |
|---|---|
| ClassData |
LinkedMasteryCard
Declaration
public CardData LinkedMasteryCard { get; set; }
Property Value
| Type | Description |
|---|---|
| CardData |
Name
Name displayed on the card. Overridden by the NameKey field.
Declaration
public string Name { get; set; }
Property Value
| Type | Description |
|---|---|
| System.String |
NameKey
Localization key for the card's name. Overrides the Name field.
Declaration
public string NameKey { get; set; }
Property Value
| Type | Description |
|---|---|
| System.String |
OverrideDescriptionKey
Localization key for the card's description. Overrides the Description field.
Declaration
public string OverrideDescriptionKey { get; set; }
Property Value
| Type | Description |
|---|---|
| System.String |
Rarity
The card's rarity: Common, Uncommon, Rare, Champion, Starter
Declaration
public CollectableRarity Rarity { get; set; }
Property Value
| Type | Description |
|---|---|
| CollectableRarity |
SharedMasteryCards
Declaration
public List<CardData> SharedMasteryCards { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<CardData> |
SpriteCache
A cache for the card's sprite so it doesn't have to be reloaded repeatedly.
Declaration
public Sprite SpriteCache { get; set; }
Property Value
| Type | Description |
|---|---|
| UnityEngine.Sprite |
StartingUpgrades
These upgrades are applied to all new instances of this card by default.
Declaration
public List<CardUpgradeData> StartingUpgrades { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<CardUpgradeData> |
Targetless
Whether or not the card has a target.
Declaration
public bool Targetless { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
TargetsRoom
Whether or not the card targets a room.
Declaration
public bool TargetsRoom { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
TraitBuilders
Append to this list to add new card traits. The Build() method recursively builds all nested builders.
Declaration
public List<CardTraitDataBuilder> TraitBuilders { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<CardTraitDataBuilder> |
Traits
List of pre-built card traits.
Declaration
public List<CardTraitData> Traits { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<CardTraitData> |
TriggerBuilders
Append to this list to add new card triggers. The Build() method recursively builds all nested builders.
Declaration
public List<CardTriggerEffectDataBuilder> TriggerBuilders { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<CardTriggerEffectDataBuilder> |
Triggers
List of pre-built card triggers.
Declaration
public List<CardTriggerEffectData> Triggers { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<CardTriggerEffectData> |
UnlockLevel
Level at which the card is unlocked.
Declaration
public int UnlockLevel { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Int32 |
Methods
AddToCardPool(String)
Adds this card to the cardpool whose ID is passed in
Declaration
public void AddToCardPool(string cardPoolID)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | cardPoolID | ID of the card pool, most easily retrieved using the helper class "MTCardPoolIDs" |
Build()
Builds the CardData represented by this builder's parameters recursively; i.e. all CardEffectBuilders in EffectBuilders will also be built.
Declaration
public CardData Build()
Returns
| Type | Description |
|---|---|
| CardData | The newly created CardData |
BuildAndRegister()
Builds the CardData represented by this builder's parameters recursively and registers it and its components with the appropriate managers.
Declaration
public CardData BuildAndRegister()
Returns
| Type | Description |
|---|---|
| CardData | The newly registered CardData |
CreateAndSetCardArtPrefabVariantRef(String, String)
Creates an asset reference to an existing game file. Primarily useful for reusing base game art. Cards with custom art should not use this method.
Declaration
public void CreateAndSetCardArtPrefabVariantRef(string m_debugName, string m_AssetGUID)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | m_debugName | The asset's debug name (usually the path to it) |
| System.String | m_AssetGUID | The asset's GUID |
SetClan(String)
Sets this card's clan to the clan whose ID is passed in
Declaration
public void SetClan(string clanID)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | clanID | ID of the clan, most easily retrieved using the helper class "MTClanIDs" |


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Visual Analytics Lab(US Department of Homeland Security) - PURDUE
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Contact:
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I am a 2nd year Master student, advised by Prof. Dr. David Ebert. My research is in the areas of optimization and machine learning, with applications in Supply Chain, Finance and Agriculture. My current focus is on research topics like Stochastic Optimization, Bayesian Inference and Deep Learning. Currently I am working on developing and deploying Deep learning and Bayesian inference models for effective irrigation Management to growers in California.
I am pursuing Masters in Industrial Engineering with specialization in Operation Research at Purdue Univeristy
I graduated from Sardar Patel College Of Engineering (class 2015) with a Btech degree in Mechanical Engineering.
More information is available in my CV, LinkedIn profile and GitHub account.


GO-REST-WOL Your little Monster to Wakeup the lazy one
| Computer | MAC Adress | Broadcast IP Address | API URL | |
|---|---|---|---|---|
| {{.Name}} | {{.Mac}} | {{.BroadcastIPAddress}} | /api/wakeup/computer/{{.Name}} |
REST API Usage
/api/computer/<ComputerName> Returns a JSON Object
{ "success":true, "message":"Succesfully Wakeup Computer Computer1 with Mac 64-07-2D-BB-BB-BF on Broadcast IP <IP_ADDRESS>:9", "error":null }
- success
- True or False if the WakeOnLan Paket was send
- message
- Message as string what happen
- error
- Encoded Jsonobject from GOLANG Error Object
Project Page: https://github.com/dabondi/go-rest-wol
Build with by David Baumann, https://github.com/dabondi


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{% extends "settings/settings_base.html" %} {% block extrascript %} {% endblock %} {% block navigation %} Settings Graph Rules {% endblock %} {% block tabcontent %}
| Graph Rule | Packages | Locales | Release Branch | Created On |
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| Graph Rule | Packages | Locales | Release Branch | Created On |
| {{ rule.rule_name }} | {{ rule.rule_packages|join:", " }} | {{ rule.rule_langs|join:", " }} | {{ rule.rule_relbranch }} | {{ rule.created_on }} |


Fran Castaño not surprised by Sevilla's interest in Mohammed Salisu; reveals how he DISCOVERED the youngster
Director of the African Talent Football Academy Fran Castaño says he is not surprised by Sevilla interest in Mohammed Salisu, describing how he discovered the young defender.
And Castaño who scouted the youngster says he is not surprise by the meteoric rise of the player because he's destined to achieve great things in the beautiful game when asked about the Sevilla interest.
"What surprises me most about him is that he always needs challenges to grow and that, until now, he has always overcome. He is not afraid of the challenge, for that reason he will not stop making the leap."
Castaño continued by revealing how he discovered the defender: "My academy is in Accra and they told us that there was a group of players in Kumasi that could be interesting. We made a five-hour trip and ten minutes from see him we knew since he was a different player."
"We saw it almost by chance because he was late, when we were leaving. He appeared without boots, but we soon saw that he was different . He has an impressive physique, he is technically good. He plays as central and left-handed, which is something we value very much. Then we talked with him and his family and discovered that he was the type of person we want, "added astaño.
"Given these qualities, it is not surprising that Monchi already plans to sign him for Sevilla," he deduced.
He has scored one goal in 12 matches for Real Valladolid in the ongoing La Liga campaign.
Salisu was called up for Ghana's 2021 Africa Cup of Nations double-header qualifiers but withdrew from the squad due to injury.


"""
combinerefs path
A helper for analyzing PYTHONDUMPREFS output.
When the PYTHONDUMPREFS envar is set in a debug build, at Python shutdown
time Py_FinalizeEx() prints the list of all live objects twice: first it
prints the repr() of each object while the interpreter is still fully intact.
After cleaning up everything it can, it prints all remaining live objects
again, but the second time just prints their addresses, refcounts, and type
names (because the interpreter has been torn down, calling repr methods at
this point can get into infinite loops or blow up).
Save all this output into a file, then run this script passing the path to
that file. The script finds both output chunks, combines them, then prints
a line of output for each object still alive at the end:
address refcnt typename repr
address is the address of the object, in whatever format the platform C
produces for a %p format code.
refcnt is of the form
"[" ref "]"
when the object's refcount is the same in both PYTHONDUMPREFS output blocks,
or
"[" ref_before "->" ref_after "]"
if the refcount changed.
typename is Py_TYPE(object)->tp_name, extracted from the second PYTHONDUMPREFS
output block.
repr is repr(object), extracted from the first PYTHONDUMPREFS output block.
CAUTION: If object is a container type, it may not actually contain all the
objects shown in the repr: the repr was captured from the first output block,
and some of the containees may have been released since then. For example,
it's common for the line showing the dict of interned strings to display
strings that no longer exist at the end of Py_FinalizeEx; this can be recognized
(albeit painfully) because such containees don't have a line of their own.
The objects are listed in allocation order, with most-recently allocated
printed first, and the first object allocated printed last.
Simple examples:
00857060 [14] str '__len__'
The str object '__len__' is alive at shutdown time, and both PYTHONDUMPREFS
output blocks said there were 14 references to it. This is probably due to
C modules that intern the string "__len__" and keep a reference to it in a
file static.
00857038 [46->5] tuple ()
46-5 = 41 references to the empty tuple were removed by the cleanup actions
between the times PYTHONDUMPREFS produced output.
00858028 [1025->1456] str '<dummy key>'
The string '<dummy key>', which is used in dictobject.c to overwrite a real
key that gets deleted, grew several hundred references during cleanup. It
suggests that stuff did get removed from dicts by cleanup, but that the dicts
themselves are staying alive for some reason. """
import re
import sys
def read(fileiter, pat, whilematch):
for line in fileiter:
if bool(pat.match(line)) == whilematch:
yield line
else:
break
def combinefile(f):
fi = iter(f)
for line in read(fi, re.compile(r'^Remaining objects:$'), False):
pass
crack = re.compile(r'([a-zA-Z\d]+) \[(\d+)\] (.*)')
addr2rc = {}
addr2guts = {}
before = 0
for line in read(fi, re.compile(r'^Remaining object addresses:$'), False):
m = crack.match(line)
if m:
addr, addr2rc[addr], addr2guts[addr] = m.groups()
before += 1
else:
print('??? skipped:', line)
after = 0
for line in read(fi, crack, True):
after += 1
m = crack.match(line)
assert m
addr, rc, guts = m.groups()
if addr not in addr2rc:
print('??? new object created while tearing down:', line.rstrip())
continue
print(addr, end=' ')
if rc == addr2rc[addr]:
print('[%s]' % rc, end=' ')
else:
print('[%s->%s]' % (addr2rc[addr], rc), end=' ')
print(guts, addr2guts[addr])
print("%d objects before, %d after" % (before, after))
def combine(fname):
with open(fname) as f:
combinefile(f)
if __name__ == '__main__':
combine(sys.argv[1])


WITNESS LIST
Judiciary & Civil Jurisprudence Committee
April 29, 2013 - 2:00 PM or upon final adjourn./recess
For:
Beasley, Felissa (Self)
BEasley, Jeri (Self)
Beasley, Nicholas (Self)
Beasley, Noah (Self)
Beasley, Ryker (Self)
Burfeind, Amber (Self; Independent Adoption Center)
Goodfriend, Dawn (Self)
Mclaughlin, Kassandra (Self)
Paulig, Gretchen (Self)
Rubke, Trish (Self)
Smith, Chuck (Equality Texas)
Wynne, Anne (Self)
Against:
Saenz, Jonathan (Texas Values)
Registering, but not testifying:
For:
Ausley, Robbie (Self)
Bier, Marti (Self)
Bier-Faili, Kiyah (Self)
Bryant, Suzanne (Self)
Carpenter, Edward (Self)
Cole, Shelby (Self)
Colucci, Liza (Self)
Cornell, Victor (American Civil Liberties Union of Texas)
Cunningham, Joy (Self)
Cunningham paulig, Emi (Self)
Cunningham paulig, Julia kate (Self)
Desormes-Smith, Darla (Self)
Desormes-Smith, Shannon (Self)
Edwards, Jude (Self)
Edwards, Kirsten (Self)
Faili, Sanaz (Self)
Garcia, Danielle (Self)
Goodfriend, Sarah (Self)
Goodfriend, Ting (Self)
Harris, Ashley (Texans Care for Children)
Hudgens, Sarah (Self)
Hull, Mike (Self)
Hundemer, Melissa (Self)
Lackey, Carrie (Self)
1
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Lancaster, David (Self)
Langsley, Karen (Self)
Lee, Veronica (Self)
Lee-Mizcles, Avery (Self)
Mac Donald, John (Self)
Marchione Jones, Rosalinda (Self)
Maxwell, Megan (Self)
Maxwell, Melissa (Self)
Mccuistion, Vicki (Self)
Miller, Andy (Self)
Minkoff, Sadie (Self)
Mizcles, Celina (Self)
Naylor, Angelique (Self)
Roan, Kristin (Self)
Rubke-Hunt, Gavin (Self)
Salinas, Carlos (Self)
Schreiber, Michelle (Self)
Shell, Kayla (Self)
Simms (Self)
Spillar, Dave (Self)
Stephens, Brian (Self)
Wilcox, Jill (Self)
Wong, Corinne (Self)
Woods, Camille (Self)
Against:
Hettinger, Ann (Self; Concerned Women for America)
Mutranowski, Kia (Self; Concerned Women for America)
Smith, Michelle (Self; Concerned Women For America)
Wood, Cecilia (Self; Concerned Women for America of Texas)
On:
Brown, Cindy (DSHS)
For:
Barillas, Ph.D., Katherine (One voice texas)
Cormie, Duncan (Self; Texas Network of Youth Services)
Salinas, Carlos (Alliance for Texas Families)
Registering, but not testifying:
For:
Harris, Ashley (Texans Care for Children)
On:
Parker, Pamela (Department of Family and Protective Services)
2
WITNESS LIST
Registering, but not testifying:
For:
Allmon, Jennifer (The Texas Catholic Conference of Bishops)
Cormie, Duncan (Self; Texas Network of Youth Services)
Cornell, Victor (American Civil Liberties Union of Texas)
On:
Parker, Pamela (Department of Family and Protective Services)
For:
Crossley, Jay (Self; Houston Tomorrow & Houston Food Policy Workgroup)
McGeary, Judith (Self; Farm and Ranch Freedom Alliance)
Plezia, Rick (Texas trial lawyers association)
Registering, but not testifying:
For:
Casnovsky, Joy (Self; Sustainable food center)
Harris, Ashley (Texans Care for Children)
Hull, Mike (Texans for lawsuit reform)
For:
Siegel, Martin (Self)
Registering, but not testifying:
For:
Cornell, Victor (American Civil Liberties Union of Texas)
Moreno, Celina (MALDEF)
On:
Parker, Pamela (Department of Family and Protective Services)
Against:
Dahill, John (Texas Conference of Urban Counties)
On:
Terry, Michael (Department of Public Safety)
Registering, but not testifying:
For:
Allen, Ray (Texas Probation Association)
Davis, Melissa (The National Association of Social Workers - Texas Chapter)
Switzer, Gyl (Mental health america of texas)
3
WITNESS LIST
Against:
Baggett, Donnis (Self; Texas Press Association)
Lewis, Bill (Mothers Against Drunk Driving)
Mendez, Mark (Tarrant County)
Tays, Steven (Bexar County Criminal District Attorney's Office)
For:
Bowles, Cosette (Self)
Drenner, Julie (Texas Values)
Hiller, Michael (Self)
Saenz, Jonathan (Texas Values)
Wood, Cecilia (Self; Concerned Women for America of Texas)
Registering, but not testifying:
For:
Allmon, Jennifer (The Texas Catholic Conference of Bishops)
Hettinger, Ann (Self; Concerned Women for America)
Mahoney, Annie (Texas Conservative Coalition)
Mutranowski, Kia (Self; Concerned Women for America)
Smith, Michelle (Self; Concerned Women for America)
Against:
Bresnen, Steve (Texas Family Law Foundation)
On:
Garbutt, Liz (HHSC Resource Witness)
Against:
Dahill, John (Texas Conference of Urban Counties)
Registering, but not testifying:
Against:
Allison, Jim (County Judges and Commissioners Association of Texas)
Mendez, Mark (Tarrant County)
Mitchell, Seth (Bexar County Commissioners Court)
Registering, but not testifying:
Against:
Tays, Steven (Bexar County Criminal District Attorney's Office)
4
WITNESS LIST
Registering, but not testifying:
Against:
Tays, Steven (Bexar County Criminal District Attorney's Office)
For:
Reddy, Vikrant (Texas Public Policy Foundation)
Registering, but not testifying:
For:
Allmon, Jennifer (The Texas Catholic Conference of Bishops)
Harris, Ashley (Texans Care for Children)
Magnuson, Benet (Texas Criminal Justice Coalition)
Mitchell, Seth (Bexar County Commissioners Court)
Olson, Anne (TX Baptist Christian Life Commission)
Sabo, Jason (Children at Risk)
Tays, Steven (Bexar County Criminal District Attorney's Office)
Wood, Justin (Harris County District Attorney's Office)
Yanez Correa, Ana (Texas Criminal Justice Coalition)
On:
Parker, Pamela (Department of Family and Protective Services)
Sohrt, Kaci (Travis County District Clerk's Office)
For:
Plezia, Rick (Texas trial lawyers association)
Registering, but not testifying:
For:
Christian, George (Texas Association of Defense Counsel)
Hull, Mike (Texans for lawsuit reform)
Mahoney, Annie (Texas Conservative Coalition)
For:
Cummings, Patricia (Self)
Heatly, Staley (Self)
Morton, Michael (Self)
Reddy, Vikrant (Texas Public Policy Foundation)
Against:
Breen, Terry (24th da office)
Clopton, Robert (Fort Bend County District Attorney's Office)
Clopton, Robert (Fort Bend County District Attorney's Office)
5
WITNESS LIST
On:
Kepple, Robert (Texas district and county attorneys association)
Registering, but not testifying:
For:
Bernhardt, Rebecca (Texas Defender Service)
Cornell, Victor (American Civil Liberties Union of Texas)
Eppes, Brian (Tarrant County District Attorney's Office)
Gonzalez, David (Self; Texas Criminal Defense Lawyers Association)
Gosslee, Susybelle (League of Women Voters Tx)
Mahoney, Annie (Texas Conservative Coalition)
Salinas, Carlos (Alliance for Texas Families)
Vitris, Michael (Texas Appleseed)
Wood, Justin (Harris County District Attorney's Office)
Yanez Correa, Ana (Texas Criminal Justice Coalition)
6


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EVP_KEYEXCH_fetch, EVP_KEYEXCH_free, EVP_KEYEXCH_up_ref, EVP_KEYEXCH_get0_provider, EVP_KEYEXCH_is_a, EVP_KEYEXCH_do_all_provided, EVP_KEYEXCH_names_do_all, EVP_KEYEXCH_get0_name, EVP_KEYEXCH_get0_description, EVP_KEYEXCH_gettable_ctx_params, EVP_KEYEXCH_settable_ctx_params - Functions to manage EVP_KEYEXCH algorithm objects
#include <openssl/evp.h>
EVP_KEYEXCH *EVP_KEYEXCH_fetch(OSSL_LIB_CTX *ctx, const char *algorithm,
const char *properties);
void EVP_KEYEXCH_free(EVP_KEYEXCH *exchange);
int EVP_KEYEXCH_up_ref(EVP_KEYEXCH *exchange);
OSSL_PROVIDER *EVP_KEYEXCH_get0_provider(const EVP_KEYEXCH *exchange);
int EVP_KEYEXCH_is_a(const EVP_KEYEXCH *exchange, const char *name);
const char *EVP_KEYEXCH_get0_name(const EVP_KEYEXCH *exchange);
void EVP_KEYEXCH_do_all_provided(OSSL_LIB_CTX *libctx,
void (*fn)(EVP_KEYEXCH *exchange, void *arg),
void *arg);
int EVP_KEYEXCH_names_do_all(const EVP_KEYEXCH *exchange,
void (*fn)(const char *name, void *data),
void *data);
const char *EVP_KEYEXCH_get0_description(const EVP_KEYEXCH *keyexch);
const OSSL_PARAM *EVP_KEYEXCH_gettable_ctx_params(const EVP_KEYEXCH *keyexch);
const OSSL_PARAM *EVP_KEYEXCH_settable_ctx_params(const EVP_KEYEXCH *keyexch);
EVP_KEYEXCH_fetch() fetches the key exchange implementation for the given algorithm from any provider offering it, within the criteria given by the properties. See "ALGORITHM FETCHING" in crypto(7) for further information.
The returned value must eventually be freed with EVP_KEYEXCH_free().
EVP_KEYEXCH_free() decrements the reference count for the EVP_KEYEXCH structure. Typically this structure will have been obtained from an earlier call to EVP_KEYEXCH_fetch(). If the reference count drops to 0 then the structure is freed.
EVP_KEYEXCH_up_ref() increments the reference count for an EVP_KEYEXCH structure.
EVP_KEYEXCH_get0_provider() returns the provider that exchange was fetched from.
EVP_KEYEXCH_is_a() checks if exchange is an implementation of an algorithm that's identifiable with name.
EVP_KEYEXCH_get0_name() returns the algorithm name from the provided implementation for the given exchange. Note that the exchange may have multiple synonyms associated with it. In this case the first name from the algorithm definition is returned. Ownership of the returned string is retained by the exchange object and should not be freed by the caller.
EVP_KEYEXCH_names_do_all() traverses all names for the exchange, and calls fn with each name and data.
EVP_KEYEXCH_get0_description() returns a description of the keyexch, meant for display and human consumption. The description is at the discretion of the keyexch implementation.
EVP_KEYEXCH_do_all_provided() traverses all key exchange implementations by all activated providers in the library context libctx, and for each of the implementations, calls fn with the implementation method and data as arguments.
EVP_KEYEXCH_gettable_ctx_params() and EVP_KEYEXCH_settable_ctx_params() return a constant OSSL_PARAM(3) array that describes the names and types of key parameters that can be retrieved or set by a key exchange algorithm using EVP_PKEY_CTX_get_params(3) and EVP_PKEY_CTX_set_params(3).
EVP_KEYEXCH_fetch() returns a pointer to a EVP_KEYEXCH for success or NULL for failure.
EVP_KEYEXCH_up_ref() returns 1 for success or 0 otherwise.
EVP_KEYEXCH_names_do_all() returns 1 if the callback was called for all names. A return value of 0 means that the callback was not called for any names.
EVP_KEYEXCH_is_a() returns 1 of exchange was identifiable, otherwise 0.
EVP_KEYEXCH_gettable_ctx_params() and EVP_KEYEXCH_settable_ctx_params() return a constant OSSL_PARAM(3) array or NULL on error.
"ALGORITHM FETCHING" in crypto(7), OSSL_PROVIDER(3)
The functions described here were added in OpenSSL 3.0.
Copyright 2019-2021 The OpenSSL Project Authors. All Rights Reserved.
Licensed under the Apache License 2.0 (the "License"). You may not use this file except in compliance with the License. You can obtain a copy in the file LICENSE in the source distribution or at https://www.openssl.org/source/license.html.


Restaurant Encapulation Try our Menu!
| # | Drinks | Cost | |
|---|---|---|---|
| 1 | Brazil | $17 | |
| 2 | Columbia Gomez Melo | $18.50 | |
| 3 | Hero Group | $15 |
| # | Baked Goods | Cost | |
|---|---|---|---|
| 1 | pain au chocolate | $5 | |
| 2 | plain croissant | $4 | |
| 3 | chocolate chip cookie | $3 |
Oh yeah, it’s that good. See for yourself.
Charming iconic soft power, uniforms bespoke Washlet hub handsome the best cosy elegant. Remarkable perfect carefully curated liveable Scandinavian. Efficient boutique bespoke sleepy. Efficient artisanal business class Marylebone flat white Ettinger.


Hi there! This will be a series of blog posts exploring image processing technique with a browser and HTML5 Canvas. Before we start and dive into filters and effects, let's start by looking at how image data is handled on the canvas.
Loading an image to browser
As web engineers, we take it for granted
displays an image in a browser because it is a digital photo. But what does it look like when you are dealing with it in JavaScript?
Use Image contractor to create image element
The way to create HTMLImageElement
(without writing HTML tag) is to use the Image
constructor. You create new Image instance add src
attribute to it like you type
. At this point, img
object you created has same properties as 1HTMLImageElement`, in the sample you can see width and height properties are logged on console.
Draw on canvas
Let's try to get not only properties but also the actual data of this image. Canvas in HTML5 has nifty method called getImageData
which provides pixel by pixel information about an image.
Let's start by creating Canvas element. There is no constructor like we used for Image, so we'll have to use createElement()
much like you create other HTML element in JavaScript.
var canvas = document.createElement('canvas');
canvas.width = width;
canvas.height = height;
Let's draw an image into this newly created canvas. It may feel natural to call method on the element itself like canvas.drawImage()
...however, in canvas, rendering layer (a layer that handles drawing on browser) is provided as separate thing called context
.
what is "context" ?
Context is a layer that handles "what is drawn on where in this canvas". Much like there are different types of drawing method from oil painting to mixed media collage, you can specify how you want to send drawing instruction (called contextType
). For now, we are going to use '2d' context type - which let us draw on canvas by saying "Draw a red circle on x:8, y:10". Other context type is webgl
which use different ways to draw on canvas.
You can create context by calling getContext('contextType')
on canvas element.
var context = canvas.getContext('2d');
"drawImage" and "getImageData"
Now we are ready to draw our image into canvas using our 2d context. Following code will draw a red pixel (1pixel x 1pixel rectangle) at position x:5 y:10 from top left corner.
var context = canvas.getContext('2d');
context.fillStyle = 'red';
context.fillRect(5, 10, 1, 1);
Let's say you have a 100 pixel by 100 pixel image and a magnifying glass, you can repeat above code for 10000 times to copy an image into canvas...but that's a lot of work.
Since we already have Image object, we can use drawImage()
method on context to auto copy image into canvas. Once the image is copied, we can call getImageData()
on 2d context to see data underneath the canvas.
As you can see in the example, context returned ImageData
object which contains width, height, and data of the canvas which is an Array of red, green, blue, and alpha value. We'll start using this data to manipulate appearance of an image.


{% extends "rq_scheduler_dashboard/base.html" %} {% block content %}
This list below contains all the registered queues with the number of jobs currently scheduled for the queue. Select a queue to view all jobs currently scheduled for the queue.
| Queue | Jobs |
|---|---|
| Loading... |
This list below shows the current status of the scheduler.
This list below contains all the scheduled jobs for queue {{ queue.name }}, sorted by age (next due on top).
| Name | Created At | Scheduled For | Actions |
|---|---|---|---|
| Loading... |


RAM access time is normally measured in:
You certainly don't wait for seconds each time your computer accesses the RAM.
Compared to other things going on in the computer (such as access disk, which takes thousandths of a second), RAM access is very fast.


Can we actually quantify decentralization? Balaji Srinivasan, CEO & Co-founder of 21.co, a company that allows you to replace your public email with an inbox that pays you, makes an attempt to quantify the distribution of value in decentralized systems.
Quantifying Decentralization, Blockstack Summit 2017
Source: Quantifying Decentralization by Balaji Srinivasan, Blockstack Summit 2017


New PSA-Fiat Chrysler management to try to improve cost savings: PSA CEO
by ReutersPARIS (Nov 8): The value of the cost savings announced in the planned merger between Peugeot owner PSA and Fiat Chrysler is nominal, and its management will try to improve that over time, PSA’s chief executive told French BFM Business radio on Friday.
CEO Carlos Tavares also said he does not see the need to scrap any brands within the new PSA-Fiat Chrysler entity.


--- layout: page title: Service ---
Hedera services sit on top of the Core node software and provide the functionalities that are accessed by users. These currently include the File Service, Consensus Service, Token Service, and Smart Contract Service. HIPs in the "Service" category are used to add to or improve the service-layer of Hedera. These types of features are implemented on Hedera consensus nodes.
{% assign hips=site.pages|where:"category","Service" %} {% include hipstable.md hips=hips %}


Atlanta:
Oh really?
Very well, then I have a favor to ask of you.
I want you to find the child that once lived with me here.
I feel her presence nearby!
Perhaps she can help me remember...
Although she's probably forgotten, too!
Ask around about her, would you?


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Fresh baked and fried snacks with hot drinks for you, anytime, anywhere!
ENGAL UNAVAGAM UNGAL NAAKKAI NAATIYAM AADA SEIYUMAE
5 per plate.
PLEASE FILL OUT ALL REQUIRED* FIELDS. THANKS!


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@* * Copyright 2019 HM Revenue & Customs * * Licensed 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 config.AppConfig @import controllers.routes._ @import uk.gov.hmrc.play.views.html._ @import viewmodels.HtmlTable @import uk.gov.hmrc.wco.dec.TransportEquipment @(form: Form[_], transports: Seq[TransportEquipment])(implicit r: Request[_], messages: Messages, appConfig: AppConfig) @tableHeader = @{ if(transports.size == 1) { messages("containerDetails.table.heading") } else { messages("containerDetails.table.multiple.heading", transports.size) } } @main_template(title = messages("containerDetails.title"), bodyClasses = None) { @helpers.form(action = controllers.goodsitems.routes.TransportEquipmentController.onSubmit()) { @components.error_summary(form.errors)


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../ cast-vote/ create-poll/ delete-poll/ expire-poll/ mark-poll-as-draft/ mark-poll-as-pending/ publish-poll/ unpublish-poll/ update-poll/


The main goal of mlrMBO
is to optimize expensive black-box functions by model-based optimization (aka Bayesian optimization) and to provide a unified interface for different optimization tasks and algorithmic MBO variants. Supported are, among other things:
This vignette gives a brief overview of the features of mlrMBO
. A more detailed documentation can be found on: http://mlr-org.github.io/mlrMBO/.
Installing mlrMBO
will also install and load the dependencies mlr
, ParamHelpers
, and smoof
. For this tutorial, you also need the additional packages DiceKriging
and randomForest
.
library(mlrMBO)
smoof
.mbo()
.As a simple example we minimize a cosine-like function with an initial design of 5 points and 10 sequential MBO iterations. Thus, the optimizer is allowed 15 evaluations of the objective function in total to approximate the optimum.
Instead of manually defining the objective, we use the smoof package which offers many toy and benchmark functions for optimization.
obj.fun = makeCosineMixtureFunction(1)
obj.fun = convertToMinimization(obj.fun)
print(obj.fun)
## Single-objective function
## Name: Cosine Mixture Function
## Description: no description
## Tags: single-objective, discontinuous, non-differentiable, separable, scalable, multimodal
## Noisy: FALSE
## Minimize: TRUE
## Constraints: TRUE
## Number of parameters: 1
## Type len Def Constr Req Tunable Trafo
## x numericvector 1 - -1 to 1 - TRUE -
## Global optimum objective value of -0.1000 at
## x
## 1 0
ggplot2::autoplot(obj.fun)
You are not limited to these test functions but can define arbitrary objective functions with smoof.
makeSingleObjectiveFunction(
name = "my_sphere",
fn = function(x) {
sum(x*x) + 7
},
par.set = makeParamSet(
makeNumericVectorParam("x", len = 2L, lower = -5, upper = 5)
),
minimize = TRUE
)
## Single-objective function
## Name: my_sphere
## Description: no description
## Tags:
## Noisy: FALSE
## Minimize: TRUE
## Constraints: TRUE
## Number of parameters: 2
## Type len Def Constr Req Tunable Trafo
## x numericvector 2 - -5 to 5 - TRUE -
Check ?smoof::makeSingleObjectiveFunction
for further details.
Before MBO can really start, it needs a set of already evaluated points - the inital design, as we have to initially learn our first machine learning regression model to propose new points for evaluation. If no design is given (i.e. design = NULL
), mbo()
will use a Maximin Latin Hypercube lhs::maximinLHS()
design with n = 4 * getNumberOfParameters(obj.fun)
points. If the design does not include function outcomes mbo()
will evaluate the design first before starting with the MBO algorithm. In this example we generate our own design.
des = generateDesign(n = 5, par.set = getParamSet(obj.fun), fun = lhs::randomLHS)
We will also precalculate the results:
des$y = apply(des, 1, obj.fun)
Note: mlrMBO uses y
as a default name for the outcome of the objective function. This can be changed in the control object.
We decide to use Kriging as our surrogate model because it has proven to be quite effective for numerical domains. The surrogate must be specified as a mlr regression learner:
surr.km = makeLearner("regr.km", predict.type = "se", covtype = "matern3_2", control = list(trace = FALSE))
Note: If no surrogate learner is defined, mbo()
automatically uses Kriging for a numerical domain, otherwise random forest regression.
The MBOControl
object allows customization of the optimization run and algorithmic behavior of MBO. It is created with makeMBOControl()
, and can be modified with further setter-functions.
For further customization there are the following functions:
setMBOControlTermination()
: It is obligatory to define a termination criterion like the number of MBO iterations.setMBOControlInfill()
: It is recommended to set the infill criterion. For learners that support predict.type = "se"
the Confidence Bound "cb"
and the Expected Improvement "ei"
are a good choice.setMBOControlMultiPoint()
: Needed, in case you want to evaluate more then just one point per MBO-Iteration you can control this process here. This makes sense for parallelization.setMBOControlMultiObj()
: Needed, in case you want to optimize a multi-objective target function.control = makeMBOControl()
control = setMBOControlTermination(control, iters = 10)
control = setMBOControlInfill(control, crit = makeMBOInfillCritEI())
Finally, we start the optimization process and print the result object. It contains the best best found solution and its corresponding objective value.
run = mbo(obj.fun, design = des, learner = surr.km, control = control, show.info = TRUE)
## [mbo] 1: x=0.142 : y = 0.0821 : 0.0 secs : infill_ei
## [mbo] 2: x=-0.0588 : y = -0.0568 : 0.0 secs : infill_ei
## [mbo] 3: x=-0.00787 : y = -0.0992 : 0.0 secs : infill_ei
## [mbo] 4: x=0.000914 : y = -0.1 : 0.0 secs : infill_ei
## [mbo] 5: x=-0.00234 : y = -0.0999 : 0.0 secs : infill_ei
## [mbo] 6: x=0.00423 : y = -0.0998 : 0.0 secs : infill_ei
## [mbo] 7: x=-0.000479 : y = -0.1 : 0.0 secs : infill_ei
## [mbo] 8: x=-0.501 : y = 0.253 : 0.0 secs : infill_ei
## [mbo] 9: x=0.0328 : y = -0.086 : 0.0 secs : infill_ei
## [mbo] 10: x=0.000129 : y = -0.1 : 0.0 secs : infill_ei
print(run)
## Recommended parameters:
## x=0.000129
## Objective: y = -0.100
##
## Optimization path
## 5 + 10 entries in total, displaying last 10 (or less):
## x y dob eol error.message exec.time ei
## 6 0.1424200502 0.08209419 1 NA <NA> 0 -3.210524e-02
## 7 -0.0587906203 -0.05684837 2 NA <NA> 0 -2.895198e-02
## 8 -0.0078713135 -0.09917464 3 NA <NA> 0 -4.123167e-03
## 9 0.0009136003 -0.09998887 4 NA <NA> 0 -1.465564e-03
## 10 -0.0023368623 -0.09992718 5 NA <NA> 0 -1.602634e-04
## 11 0.0042282952 -0.09976164 6 NA <NA> 0 -7.825227e-05
## 12 -0.0004786400 -0.09999694 7 NA <NA> 0 -3.685421e-05
## 13 -0.5011804833 0.25303607 8 NA <NA> 0 -1.283909e-05
## 14 0.0327825630 -0.08595717 9 NA <NA> 0 -1.574032e-05
## 15 0.0001293507 -0.09999978 10 NA <NA> 0 -1.035293e-05
## error.model train.time prop.type propose.time se mean
## 6 <NA> 0.195 infill_ei 0.156 6.915038e-02 -0.10692782
## 7 <NA> 0.341 infill_ei 0.051 7.139280e-02 -0.09925429
## 8 <NA> 0.016 infill_ei 0.049 1.174245e-02 -0.09714923
## 9 <NA> 0.015 infill_ei 0.050 1.928035e-03 -0.10030872
## 10 <NA> 0.017 infill_ei 0.162 4.537499e-04 -0.09994572
## 11 <NA> 0.028 infill_ei 0.049 4.007375e-04 -0.09978520
## 12 <NA> 0.015 infill_ei 0.047 8.330914e-05 -0.09999587
## 13 <NA> 0.017 infill_ei 0.045 1.710874e-01 0.48800629
## 14 <NA> 0.019 infill_ei 0.046 6.460668e-03 -0.08425606
## 15 <NA> 0.018 infill_ei 0.048 2.210341e-05 -0.09999986
For more insights into the MBO process, we can also start the previous optimization with the function exampleRun()
instead of mbo()
. This augments the results of mbo()
with additional information for plotting. Here, we plot the optimization state at iterations 1, 2, and 10.
run = exampleRun(obj.fun, learner = surr.km, control = control, show.info = FALSE)
print(run)
## MBOExampleRun
## Number of parameters : 1
## Parameter names : x
## Parameter types : numericvector
## Global Opt (known) : -1.0000e-01
## Gap for best point : 2.1884e-05
## True points per dim. : 50
## Objectives : 1
## Points proposed per iter : 1
##
## Infill criterion : Expected improvement (ei)
## Direction of optimization : maximize
## Requirement : SE estimation
## Components : se, mean
## Parameters : se.threshold=1e-06
## Infill optimizer : focussearch
## Infill optimizer restarts : 1
## Final point by : best.true.y
## Learner : regr.km
## Learner settings:
## jitter=FALSE,covtype=matern3_2,control=trace=FALSE
## Recommended parameters:
## x=0.00128
## Objective: y = -9.998e-02
plotExampleRun(run, iters = c(1L, 2L, 10L), pause = FALSE)
## Loading required package: ggplot2


{% extends "main/baseloggedin.html" %} {% block content %}
Agent: {{agent.name}}
Dataset: {{dataset.name}}
Each cluster shown contains more than 1 entry. This means Dedupe assumes the corresponding entries are duplicates of one another. Note that only entries with more than one spelling variant are shown here. This is because direct duplicate entries of one another do not constitute a data smell. You can analyze the full cluster distribution by downloading the dataset above, which will label your input dataset with a cluster ID and a probability score for all assumed data smell related duplicate entries.
{% if clusters|length == 0 %}| Occurrences | {% for col in column_names %}{{ col }} | {% endfor %}
|---|---|
| {{occurences}} | {% for col in variant %}"{{col}}" | {% endfor %}


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Chapter 11 How data is stored
11.1 What this chapter is about
This chapter is about how data gets into the computer. Once we have collected our dataset how do we store it? Why should you care about how a computer counts and store data as an economist? Knowing how a computer counts allows you to estimate the size of datasets and how to optimally store data.
Economists often work with datasets with millions (or billions) of observations and thousands of variables. Such a dataset can take hours or even days to open. Moreover, understanding how data is saved is important when the data looks different to what you expected. For example when the sentence “Thomas Müller plays for Bayern München” looks like “Thomas MÃ¼ller plays for Bayern MÃ¼nchen”, what went wrong?
Intended learning outcomes
After reading this chapter you should be able to
- Use binary counting (base 2 counting)
- Explain how computers store information (What is a bit?)
- Explain what bits, bytes, kilobytes, megabytes and gigabytes are
- Explain what decoding and encoding are.
- Estimate the size of a dataset
- Use the tidy data principles
11.2 How computers work
1 or 0
More or less everything you do on a computer is based on binary numbers. Binary numbers are numbers that only can take two values. We often think of them as 0 or 1. But it could also be true or false, low or high and so on. When you a are reading these chapters, reading a news story, playing a computer game or watching a movie, you are essentially looking at a very long list of zeros and ones, just like the list below. Your computer or mobile device is just transforming these values to interpretable content.
A very simplified description on how computers work is that they receive an electronic signal or they do not receive a signal. These two states correspond to the binary states, which we often call zero and one. Students at the Massachusetts Institute of Technology (MIT) created the video below to illustrate how computers work, where the binary signal is set by manual switches. You can watch it here:
From 0 and 1 to interpretable content.
How do we get from zeros and ones to letters, colors and movies? To understand how this works, we have to understand binary counting. You probably learned binary counting at some point, but we are all more used to decimal (or “base ten”) counting, where we use ten different symbols to count. The numbers 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. Binary counting is not very complicated. In fact, the key difference is that we only use two symbols, 0 and 1, and it is thus a “base two” system. To understand how it works, let us briefly recap how the decimal counting you use every day works. Consider the number 123:
- The 1. number from the right denotes how many “\(10^0=1\)”s: 3
- The 2. number from the right denotes how many “\(10^1=10\)”s: 2
- The 3. number from the right denotes how many “\(10^2=100\)”s: 1
- The sum of the above is thus: \(3 \times 1+2\times 10+1\times 100\)
So when we say 123 we are essentially saying 1 times one hundred, two times ten, and three times one.
Note that every time we moved to the left we multiply the value of the number by ten. The rightmost number represents ones, moving one step to the left we get the tens, one more gives the hundreds. If we had an even larger number, we could continue to thousands, ten-thousands etc. This is because it is a base 10 system. If we instead of multiplying by ten multiply by two, we have the base 2 system. The rightmost number again denotes the number of ones, the second number from the left now denotes the number of twos (\(2\times 1\)), the third number the number of fours (\(2\times 2\)), the third number the number eights (\(2\times 4\)), and so on. Let us try to write the same number as above (123 in base 10) using base two:
- The 1. number from the right denotes how many “\(2^0=1\)”s=1
- The 2. number from the right denotes how many “\(2^1=2\)”s=1
- The 3. number from the right denotes how many “\(2^2=4\)”s=0
- The 4. number from the right denotes how many “\(2^3=8\)”s=1
- The 5. number from the right denotes how many “\(2^4=16\)”s=1
- The 6. number from the right denotes how many “\(2^5=32\)”s=1
- The 7. number from the right denotes how many “\(2^6=64\)”s=1
- The 8. number from the right denotes how many “\(2^7=128\)”s=0
- The sum of the above is thus: \(1 \times 1+1\times 2+0\times 4 +1\times 8 +1\times 16 +1\times 32 +1\times 64 +0\times 128.\)
Which is equivalent to the decimal above. So 123 in decimal numbers, expressed in terms of binary numbers is: 01111011. How did we figure out the combination that lead to 123? we started backwards and asked: How many 128s can be included? Zero. How many 64s can be included? And so on.
From signals to bits and bytes.
We can thus count to any number using just a binary signal, as long as we have enough signals. But why did I include eight signals above, when only seven were needed? Recall from above, that a computer counts by receiving signals. The signal is that either there is a signal or there is no signal. Such a signal is called a “bit”. We typically measure data sizes in terms of “bytes”. A byte simply corresponds to eight bits. The string of eight values above therefore easily converts into the language you are used to when talking about file sizes on a computer.
If you have a (very small) file that is “1kb”" big it means that it contains 1024 bytes or \(1024\times 8= 8192 bits\)". Such a file therefore contains 8192 zeros or ones and in principle we would be able to continue the list above to have 8192 rows and write a very large number:
- The 1. number from the right denotes how many “\(2^0=1\)” we have.
- The 2. number from the right denotes how many “\(2^1=2\)” we have.
- The 3. number from the right denotes how many “\(2^2=4\)” we have.
- \(\vdots\)
- The 8192. number from the right denotes how many “\(2^{8191}=..?\) (a very large number!) we have”
We wrote in “principle”, because this is not how the computer would save such a number. Take for example the number 128. Using just 1 byte, the computer could save this number as “01000000”. However, if you create a new empty file on your computer, you will discover, that this is not what happens. Instead it is likely to that the file content will contain three bytes and look like the following:
00110001 00110010 00111000
Let us try to translate this.
- The first byte is \(1 \times 1+0\times 2+0\times 4 +0\times 8 +1\times 16 +1\times 32 +0\times 64 +0\times 128=49\)
- The second byte is: \(0 \times 1+1\times 2+0\times 4 +0\times 8 +1\times 16 +1\times 32 +0\times 64 +0\times 128=50\) * The third and last byte is: \(0 \times 1+0\times 2+0\times 4 +1\times 8 +1\times 16 +1\times 32 +0\times 64 +0\times 128=56\)
So somehow 49, 50 and 56 translate into 128. Computers use a set of rules to translate the signals into letters, numbers or pictures. The set of rules are defined by the “encoding scheme”. The document created above was saved using the 128 encoding scheme. According to this scheme a 49 is translated to a 1, 50 to 2 and 56 to 8. We will return to to the encoding schemes below, but let us first try to shed some more light on bits and bytes.
A never ending confusion 1000 or 1024?
If you ever bought a 1GB memory stick and checked the capacity on a computer, you might have discovered that the computers says the capacity is less than 1 GB. Why is that? Basically it is a confusion between decimal and binary counting. \(2^{10}=1024\) is very close to the number 1000, which we call “kilo”, \(2^{20}=1,048,576\) is close to 1,000,000 which we call “mega” and \(2^{30}=1,073,741,824\) is close to 1,000,000,000 which we call “giga”. If you say 1GB (1 gigabyte) you might therefore refer to 1,073,741,824 bytes or 1,000,000,000 bytes. Not all systems use the same aggregation (Linux/Unix systems may for example differ from Windows systems).
How fast is your Internet connection?
Another source of confusion when talking about bytes and bits is the “B”" vs “b”. The former relates to bytes, the latter to bits. If we are talking about 1GB we mean 1 gigabyte. If we are talking 1Gb we are talking about 1“gigabit”. If you have an Internet connection with an advertised speed up to “50 Mbps” it means that it is able to receive up to 50 megabits per second. So remember that 1 byte is 8 bits, and 50Mbps therefore means that you should be able to download \(50/8=6.25\) megabytes per second.
To recap, after reading this section you should be able to: * Distinguish between decimal and binary counting. * Explain how a computer saves information in terms of sequences if binary signals.
11.3 Encoding
How encoding and decoding works
We now know that a computer stores information by means of binary signals. We can translate these signals into numbers, but it is not yet clear how to translate these signals into letters and symbols. To achieve this, computers use rules where a number is translated into a numerical value, a letter or a symbol. So the computer has a set of bytes: 00110001 00110010 00111000, in decimal terms this corresponds to 49, 50 and 56. The computer then looks up in a large table which says that: 49=1, 50=2, 56=126. So the the list of bytes represents the number 128. going from 49, 50 and 56 to 128 is called decoding, which is what the computer does when you open a document. When you save a document the computer encodes the content from letters and symbols to first a decimal number (using the big table) and then to sequence of bytes.
To get terms straight: We say that we encode something when we convert something into a coded form, so if you take something in readable form and convert it into code, you are encoding it. You are decoding a piece of code if you are converting the code into readable form. }
Where does the big table come from?
So where is the big table that the computer uses to look up numbers? One of the most famous tables, i.e. set of rules to translate the number derived from a sequence of bits is the “American Standard Code for Information Interchange” also known as ASCII. ASCII contains a list of 128 numbers and the corresponding symbol. The full list is available here, but a small excerpt of the full list is reported here:
| Decimal number | Symbol |
|---|---|
| 48 | 0 |
| 49 | 1 |
| 50 | 2 |
| 51 | 3 |
| … | … |
| 65 | A |
| 66 | B |
| 67 | C |
So a file with the binary code “01000001” would show an “A” if the ASCII encoding scheme was applied. This happens as follows:
- Step 1: The computer receives eight signals, corresponding to byte .
- Step 2: This translates into the decimal number \(1 \times 1+0\times 2+0\times 4 +0\times 8 +0\times 16 +0\times 32 +1\times 64 +0\times 128=65\).
- Step 3: The computer looks in the ASCII codebook (Table ) and realizes that if it sees a number binary number that corresponds to the decimal number 65 it should print the symbol A.
When you are writing a text document the reverse happens. You enter an A, the computer looks up the table, notes that this corresponds to the decimal number 65 and converts it into binary number and saves it as a string of signals.
The basic ASCII codebook contains 95 symbols that we can read and 28 instructions that we cannot directly read, such like whitespace, tab, backspace and so on. But because it only contains 128 symbols, which can be saved using seven bits, the first bit in a byte will always be zero using the ASCII encoding. This is basically a waste of resources.
There are many codebooks. What a mess!
This all sounds straightforward. Unfortunately 128 symbols aren’t sufficient to show all symbols of all languages in the world. This creates the issue where “Thomas Müller plays for Bayern München” looks like “Thomas MÃ¼ller plays for Bayern MÃ¼nchen”. There are many encoding schemes, and if a file on the computer is encoded using one scheme, but the computer decodes the binary codes using a different scheme, the symbols will look odd.
Encoding and decoding: What you should know and do?
Should you know the code tables? Certainly not. You should know that they exist and that there many different versions. It is also worth knowing that the most popular encoding schemes are ASCII and UTF-8. We we will return to that.
If things look fine: do not worry. Luckily, in most cases you do not need to worry about the encoding and decoding schemes. Most files contain some instructions to the computer on what scheme to use.
When you navigate on websites, they will typically report the coding scheme to the browser (i.e. Google chrome, Firefox, Safari, Internet Explorer), and the browser makes the correct interpretation. Even if websites do not include information about which scheme to use to translate information, browsers are typically able to guess the encoding scheme quite well.
What you need to know is that if something looks wrong, it is most likely do to the encoding and decoding gone wrong. You should also know that the encoding can affect the file size, we will return to that later.
Let us consider an example where things have gone wrong: The pictures below show screenshots from the online version of the New York Times. It is the same frontpage, but in the first version, some text is garbled. For example the headline “Trump Offers a â€˜Steel Barrier,â€™ but Democrats Are Unmoved” should be “Trump Offers a ‘Steel Barrier,’ but Democrats Are Unmoved”, as illustrated by the lower picture. What happened?
What went wrong? I instructed the browser that the document it was showing was encoded using the “Western (Windows1252)” encoding scheme scheme, but if you look at the source code of the website, you will find the element:
which instructs to the browser to use the UTF-8 encoding scheme, which is scheme used in the lower picture. So what went wrong is simply that we decoded using the wrong “coding scheme”.
Garbled websites are rarely a real issue, but here are some examples where encoding errors are problematic
Encoding issues in data
Imagine that you would like to investigate the average income across German cities. You have a large dataset, with a variable stating individual income and another variable stating “city”. You realize that the variable city has several values representing the same city. For the city München, you observe the values “München” and “Munich”, so to get the average for München you compute the average across all individuals with city being either “München” or “Munich”. This is all fine and good. But if there is an encoding error, some observations might have the value “MÃ¼nchen”, which you do not include in your average. Your average is therefore wrong.
Encoding issues in scripts and codes. In a recent update of the popular statistical software Stata, the encoding support was changed. Version 14 of Stata of the software was the first version to fully support unicode UTF-8 encoding. This means that if you open Stata files created in version 12 or 13 in version 14, they will look garbled and instructions might not work.
The unicode encoding scheme
While ASCII used to be the most popular scheme, the unicode schemes are by far the most popular schemes today. The full unicode scheme consists of a table of 1,114,112 code points (or symbols). We call them code points, because the table is somewhat more complex than for the ASCII. Instead of providing a mapping between a binary code and a symbol, there is a mapping between a binary code and a code point, and a mapping between the code point and the symbol. These details are not important, and in fact the unicode code point 65 corresponds to the symbol “A”. Does that sound familiar? It should, because there is a 1:1 mapping from ASCII to UTF-8 unicode encoding, and 65 is the decimal number for the binary sequence representing A. So if a document was saved using ASCII encoding, but you instruct the computer to use UTF-8 encoding, you should not worry. You can think of ASCII as a subset of UTF-8
<IP_ADDRESS> UTF-8, UTF-16, UTF-32
But how does UTF-8 relate to unicode? Unicode is the full set of code-points, while UTF-8 is a specific encoding scheme. There is also UTF-32 which uses 32 bits. 32 bits per symbol gives a lot of flexibility, but it will often take up a lot of space. UTF-8 is a flexible encoding scheme, because it uses the number bytes necessary to show the information. Another encoding format is UTF-16, which is also flexible in length, but in contrast to UTF-8 it uses two bytes as minimum (UTF-8 uses one byte). Documents saved using UTF-16 encoding will therefore typically be larger than documents saved with UTF-8 encoding. As the example shows, these differences can be substantial (almost twice the size), without being visible to the reader. So the encoding scheme is important because it determines whether the information is read correctly, and because affects the file size. In the next subsection, we will talk more about file sizes.
Encoding and decoding goes wrong, what can you do?
If you discover an encoding and decoding scheme, there are a number of options to sort out the issue.
- Instruct the software you are using to save the content to use a different encoding scheme.
- Instruct the software you are using to read the content to use a different decoding scheme.
- Manually correct the decoding errors in the software that you are using to read the data.
11.3.1 Encoding: What you shouldn’t do!
Let’s end this discussion on encoding with a small warning. If you open a document that is decoded using a different codebook than was used for encoding, there is usually no problem. You discover the error, you ignore it or you solve the problem as above.
However, what you should avoid doing is to overwrite the encoding scheme. Consider the following example:
- Your friend writes a document and saves it using encoding scheme X.
- You open the document and your software assume it is encoded using encoding scheme Y. You ignore that some symbols clearly are garbled, make some changes and save the document using encoding scheme Z.
- Another friend opens the dataset and everything looks a mess.
In the example above, it would require a lot of work to sort out the mess. To see why, let’s consider what happened.
- In encoding scheme X an “ü” is saved as the decimal number 5: Binary code 00000101.
- You use scheme Y to decode 00000101, which gives an “Ã¼”.
- You now decode the symbols using scheme Z, where the “Ã¼” has decimal number 7. So the binary code now becomes 00000111
- To get back to the original “ü” we would need to know that some symbols should first be decoded using scheme Z, then encoded using scheme X.
11.4 Estimating file size and memory use
Variable types
When we store data on the computer we often ignore how the computer stores the variable. However, when you store large datasets, this can become very important, as a dataset might take up more space than necessary, because the type of the format of the variable is not aligned to the content. The specific list of potential variable formats varies depending on how you save the data (i.e. what software you use to save the data), but as a minimum, you should know the following types:
Boolean A boolean variable can only contain two possible values. True or false (0 or 1). You could for example have save a variable with the content “female” as a boolean variable, where “true” would correspond to the case where the observation represents a female individual, and false otherwise. - Minimum size requirement: 1 bit.
Integer Integer variables contain only whole numbers in a pre-specified range. The range depends on the size of the variable, if the variable is a byte, it will typically allow you to save values in the range from -128 to +127. - Typical size requirement: 1 byte (8 bits) (for values -128 to 127).
Floating point numbers Floating point variables can take almost any numerical value. However, in practice the range and the precision is limited. The limit depends on the space allocated to the variable, but unless you work with very large or small values or very precise values, you do not need to worry about this. - Typical size requirement: 8 bytes (64 bits).
Characters Character values contain text, and can include all symbols that are defined in the applied encoding scheme. - Size requirement depends on encoding, 1 byte per character at least for UTF-8 encoded strings.
Note that I ranked the variable types above according to their approximate size rank. Boolean variables can be saved using only very little space, while characters and floating point variables require much more computer memory. The specific ranking depends on how the variables are saved. We will return to this issue in the next chapter.
What is important for now is that the variable format type is important. Imagine that you save the foot size as a boolean variable. In that case you will throw away a lot of information, because the foot size can then only take 2 values. On the other hand, if you save the variable “female” as a floating point variable you will waste a lot of resources, because it uses more space than actually required.
File size
We now know what we need to approximate the file size. Let us now try to estimate the size of a dataset. Our dataset contains 10 variables and 20 observations. If all these observations are stored as floating-point numbers, the approximate file size is:
\[\begin{align} 10\times20\times8&=1600\text{ bytes} \nonumber\\ &=1600/2^{10}=1.56 \text{KB}\nonumber \end{align}\]
In most programs the data will be stored the in the RAM of the computer you are working on. 1.56 KB is not an issue in terms of RAM capacity for any modern computer.
But let’s consider another dataset, a dataset covering 6 million individuals over 30 years and 250 variables:
\[\begin{align} 6,000,000\times 30 \times250 \times8&=360,000,000,000\text{ bytes} \nonumber\\ &=360,000,000,000/2^{20}= 343,323 \text{MB}\nonumber\\ &=360,000,000,000/2^{30}= 335 \text{GB}\nonumber \end{align}\]
Our normal computers will not be able to handle this dataset in the RAM and loading this data will cause problems. Such datasets are not uncommon. If you are working with administrative data on an individual level, you often have sample sizes like that.
So what could we do? First, we should try to split the dataset up in smaller elements: fewer years and/or fewer variables. Second, we should try to save variables more efficiently. Do all 250 variables need floating point precision? How big would this dataset be, if all variables were saved as integers? Recall that an integer usually takes up one byte. The floating point precision number is thus eight times larger, and the same observations and variables saved as integers would then take up \(335/8\approx 42\)GB.
What happens if we use the wrong variable format?
So what if we converted our large dataset from floating point variables to integers and some of the variables actually included values that were floating points? Well it depends on the software you use to convert the variables, but the most likely outcome is that the value will be saved as “missing” if it is not covered by the used format.
Estimates are estimates
You will probably discover, that the file size estimates using the approach presented here tends to underestimate the actual file size. The main reason for this is that files include some “overhead”. The overhead might include information on the encoding type, but also information on the file format, which the software that reads the file uses.
11.5 The tidy data principles
Ideally we would like all raw datasets to be alike. As the R scientist Hadley Wickham puts it: “Like families, tidy datasets are all alike but every messy dataset is messy in its own way”4 (Wickham 2014).
Why would we like all datasets to be alike? Having a standardized way of tidying data allows us to apply methods on various datasets, without bothering about the data structure. Imagine that you first created a very nice graph using one dataset. You would now like to create the same graph using a different dataset. Ideally, you would not have to change anything but the data source. This requires that the two datasets are organized in the same way.
To achieve a standardized way of storing datasets, a set of principles for “tidy data” have been set up:
- Each variable forms a column.
- Each observation forms a row.
- Each type of observational unit forms a table.
Figure 11.3 shows an example of a messy dataset. Why is it messy? Let’s first note the variables in this dataset:
- GDP
- Country
- GNI
- Year
This dataset has four variables, but the dataframe in Figure 11.3 has seven columns. That clearly violates the first criteria above: the number of columns should be the same as the number of variables.
Let’s try to make it tidy. We tidy it by first creating new variables corresponding to those we listed above. Country already existed, but GDP, GNI and Year are created by separating the existing columns. The tidy version of this dataset is shown in Figure 11.3.
Most statistical software (for example Excel and R) are designed for tidy datasets. You will see this in Excel when you create Pivot tables and in R when you create charts with ggplot.
(Wickham 2014) also provides the following list of the most common issues with data:
- Column headers are values, not variable names.
- Multiple variables are stored in one column.
- Variables are stored in both rows and columns.
- Multiple types of observational units are stored in the same table.
- A single observational unit is stored in multiple tables.
11.6 Summary
In this chapter we covered the following topics
- Binary counting (base 2 counting)
- How computers store information (What is a bit?)
- Bits, bytes, kilobytes, megabytes and gigabytes.
- Encoding (ASCII and Unicode UTF-8)
- Estimating dataset size.
- Tidy data principles
This is a reference to a famous Leo Tolstoy quote. Try to find it!↩


Analysis and review of: THE SILENCERS (1966)
Copyright 1998 Afterburner Email me at<EMAIL_ADDRESS>Rating (out of 10): N/A. See review for further details.
-----
A great many of us will be familiar with the image of Hugh Hefner (Founder and Publisher of Playboy magazine) at one of his famous parties. We can picture in our mind's eye Hef walking around Playboy Mansion in his robe and pajamas, cocktail in one hand, cigarette in the other. (Actually, Hef usually smoked a pipe. But for the sake of argument, we'll say it was a cigarette.) He's smiling. He's mingling. He's making small talk, with amusing bon mots and double entendres.
Now imagine that, instead of a robe and pajamas, Hef is decked out in attire that would have been fashionably casual for an affluent male in the 1960s. And while keeping everything else the same (the cigarette, the cocktail, the utterly casual attitude), imagine Hef in the underground lair of an evil Arch-Villain ready to visit death and destruction across the globe. Oh, and make Hef a James Bond-esque spy.
You now have the basic feel for the four Matt Helm movies, of which "The Silencers" (1966) was the first.
The Matt Helm movies were made as star vehicles for Dean Martin. By the mid-60s, Dino had cemented his reputation as a boozing playboy, and the Matt Helm role was written with this in mind. In the movies, Matt Helm is a super-secret, super-competent spy for the Intelligence Counter Espionage (ICE) organization. His cover in all four movies is that he's also a world-famous photographer of gorgeous women, and he works for "Slaymate" magazine (which fits in nicely with the Hugh Hefner analogy...). And in all four movies, he casually wanders his way through whatever the bad guys have to throw at him.
The plot of "The Silencers" involves an attempt by the evil "Big O" organization to start World War III. This involves taking control of a missile launch via computer and forcing the missile to impact a spot near Alamagordo, New Mexico. The catch is that, at precisely the same time, the U.S. will be conducting underground nuclear testing at the same spot. When the missile impacts, the U.S. Southwest will be covered by a glowing cloud, the U.S. will assume it's the Russians, and will retaliate.
It's never fully explained how this will benefit the "Big O" organization. Presumably they plan on stepping into the power void left behind when the two superpowers wipe each other out. No one seemed to stop and think about the consequences of being the most powerful organization in a world ravaged by nuclear destruction. But no matter. One doesn't watch Matt Helm movies in order to enjoy finely crafted plots.
So what would you watch these movies for? Cheese. Camp. Corn. These movies are all that, and more. The evil mastermind behind the "Big O" organization is a Chinese man named Tung-Tze. Of course, he's played by the very American Victor Buono. In one scene, for no real reason I can determine, we see him opening a can of "Low calorie chop suey."
Like any super-secret spy, Helm has the gruff boss (played by James Gregory, whom folks may remember as Inspector Luger on the "Barney Miller" TV show), the gorgeous babes, and the nifty gadgets. Among the gorgeous babes are his secretary, Lovey Kravezit (pronounced "Craves it"), who takes dictation while covered in suds and sharing a swimming-pool-sized bathtub, his exotic, vampish partner Tina, and the accident-prone Gail Hendricks, who may or may or may not be an agent of "Big O." And among his many gadgets are a gun which fires backwards toward the person pulling the trigger, a sports coat with exploding buttons (which make whistling sounds like a falling bomb, for no reason that can be adequately explained), and a circular bed that, at the push of a button, will whisk the occupants of the bed to another room and dump them into the previously mentioned swimming-pool-sized bathtub.
Star Trek fans will also be pleased to note that, along the way, Helm is menaced by an evil assassin played by Roger C. Carmel ("Harry Mudd," from the original Star Trek series).
By no stretch of the imagination is "The Silencers" a quality movie. The effects were lame even for the time the movie was made. The acting is wooden. The dialogue consists largely of Martin tossing off one-liners in response to various other lines and situations. The fight scenes are some of the worst ever to be committed to celluloid. And no matter how dire his situation, Martin ambles through the movie as though he's looking for the nearest bar.
But as I alluded to earlier, the folks who will enjoy this movie will like it *because* of the above flaws, not despite them. If you want to see a completely cheezy, corny spy flick, filled with exaggerated versions of the typical spy trappings (and which inspired a lot of the riffs in "Austin Powers: International Man of Mystery"), check out "The Silencers" (and indeed the rest of the Matt Helm series). You won't be disappointed.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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blocks.Material()¶
-
class
feign.blocks.
Material
(*args, **kwargs)¶ A class used to represent a Material.
- Parameters
*args (str) – ID of the material
-
_id
¶ ID of the material
- Type
str
-
density
¶ density of material in (g/cm3)
- Type
float
-
path
¶ path to attenuation coefficient file
- Type
str
-
color
¶ color of material when plotting
- Type
str
-
set_color
(color=None)¶ The function to set the color of Material in case the geometry is plotted.
- Parameters
color (str) – color of the material in hex format
-
set_density
(density=None)¶ The function to set the density of the Material
- Parameters
density (float) – density of material in g/cm2
-
set_path
(path=None)¶ The function to set the path to the attenuation data of the Material.
- Parameters
path (tuple (str,int)) – the path of the file, and the column which contains the data.


User ID:
Selfie:
Download Selfie
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hello this is about knolder lets have some html practice in class and trying to test div tags. hello this is about knolder lets have some html practice in class and trying to test div tags. hello this is about knolder lets have some html practice in class and trying to test div tags. hello this is about knolder lets have some html practice in class and trying to test div tags. hello this is about knolder lets have some html practice in class and trying to test div tags. hello this is about knolder lets have some html practice in class and trying to test div tags.
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Dear Young 'Trep,
Being an entrepreneur is single handedly the most risky, testing and gut-wrenching venture anyone can take on.
You are essentially making the decision to go against the current that life wants to take you on and risking everything.
Being an entrepreneur means becoming so addicted to success that there is nothing anyone can say or do that will alter your drive.
You will fail...and you will fail again. But what keeps you going?

There will be ups and downs, but it is how you react in the times your are down that defines who you are and whether you will be successful.
No billionaire or successful entrepreneur ever got that way by taking the easy route.
No billionaire ever got that way because he sat behind his desk in his cubicle all day & accepted his paycheck every 2 weeks.
Being an entrepreneur means going against what feels natural and comfortable.
You need to have a small amount of insanity stirring within your soul to be able to risk everything for success.
To outsiders looking in you may look crazy, because you are. They may not understand and they may question everything you do.
But as an entrepreneur this does not phase you. You stay motivated. You stay focused. You grind it out. You roll with the punches and you keep moving forward at all costs.
Being an entrepreneur is saying "fudge it, I'm going for it and I'm not looking back." It is becoming so addicted to success that some days you can hardly sleep.
This type of life isn't for everyone. For most, the amount of stress and pain that comes along with failure is too much to bear.
But it is what must be done if you are to experience true success. The most financially successful people in the world did not come to be that way because a low-risk, guaranteed home run business opportunity just fell into their lap out of the sky. This story does not exist.
 Your vision is what keeps your wheels turning during times of doubt.
 Days when you ask yourself "how did I even get here... what am I even doing?"

Every day you wake up you must risk everything. And risk it again the next day and again. A small bet never pays dividends 100 fold. Go all in and accept that the journey is the reward.
Keep grinding no matter what life throws at you and above all else never ever quit.
Sincerely,
Your Future Self


"High Art"
Reviewed by Heather Picker
Written and Directed by Lisa Cholodenko. Starring Ally Sheedy and Radha Mitchell, with Patricia Clarkson and Gabriel Mann. 1998, 101 min., Rated R.
Getting high and photography are very much a part of Lucy Berliner's (Ally Sheedy) life. With that in mind, the title of "High Art" takes on a double-meaning, as do many things in this, the feature-film debut of director Lisa Cholodenko, who also wrote the script. Lucy is a has-been, once at the top of her game as a photographer. She has sunk into oblivion with live-in girlfriend Greta (Patricia Clarkson), a washed-up, drugged out German actress who worked with Fassbinder.
Syd (Radha Mitchell, "Love and Other Catastrophes") has just been promoted to an assistant editor status at Frame magazine, which is about what else but photography. Her job mainly consists of running errands for her superiors, to which live-in boyfriend James (Gabriel Mann) is sympathetic. One evening, while taking a bath, Syd notices a leak coming from the ceiling, and decides to visit her neighbor's apartment upstairs to see if she can fix it. By this time, anyone who doesn't know what will end up happening between Syd and Lucy may have a feeling of deja vu, as women going to another residence to do handywork, usually plumbing, always seem to end up romantically involved with the woman of the house ("French Twist," "Bound").
As Syd tries to fix the leak, engaging in small talk with Lucy, she notices the beautiful photographs that cover the walls of the apartment. Within days, Syd has found out more about Lucy's career, and the two of them meet with Syd's bosses for lunch, in hopes of Lucy shooting a cover for Frame. Lucy reticently agrees to the project, but on the condition that Syd will be her editor.
The attraction between the women grows, and when Lucy wants to get away for the weekend to work, and asks Syd to join her, James, who has become suspicious of Syd's involvement with Lucy, asks if she has slept with her. She says no, and when he asks if she's leading up to it, she professes that she doesn't know. And later, when things heat up between her and Lucy, she admits that she doesn't know what to do, also tearfully tells Lucy that she thinks she is falling in love with her. The next morning, Lucy takes photographs that she will eventually ask to be used for Frame.
When the two go back home, Lucy decides that things have to change. She wants to turn her life around. She is through with drugs, and she is through with Greta. But addictions are never that easy to overcome, and withdrawn resignation from a chance at happiness is something Lucy is familiar with. Syd returns to her apartment to find that James has left, and he is avoiding her phone calls. Lucy and Syd meet again, and this time Lucy gives her the photographs that she took over their weekend together, and says that she wants Frame to use them. Syd is incomfortable with this idea, so Lucy also leaves photos of Greta before going back upstairs, where Greta accepts that their relationship is over, but implores Lucy to stay one more night. The ending came as a bit of a surprise to me the first time I saw it. The decisions that both characters make were predictable, but one took much more courage than the other.
Cholodenko shot "High Art" in a simple yet visually mesmerizing way that has a documentary feel. The camera is perpetually close to Lucy and Syd, maybe in symbolism to their relationship. There is a scene in which Syd comes home after doing drugs at Lucy's, and initiates sex with James. But she is pathetically wasted, and soon falls asleep. During this, the camera is much farther away than it is during the Syd/Lucy scene, which is quietly beautiful in it's unintrusive style and honest dialogue.
The script, a nice counterpart to the glorification of heroin chic, will be considered pretentious by some. Syd comments on the composition of some of Lucy's photos, and Lucy replies that it's been a long time since she has been deconstructed. I found the script to be so sufficient in character development, and observant, natural dialogue that I wasn't bothered by such exchanges. "High Art" can also boast some of the best acting of 1998. Clarkson steals every scene she is in, with a voice deeper than that of Garbo and the look of someone who has been lost in her own universe for years and has given up living. William Sage, as Arnie, a hanger-on who lounges around the apartment all day and into the night as to not miss out on the sharing of heroin and anything else he can get his hands on, gives a similarily strong, on-target performance.
Sheedy's work here is Oscar-caliber. As Lucy she is world weary and defiant, struggling to deal with the contrasts between the life she has known for the ten years since she dropped from public conscience and the sense of rediscovery that Syd has brought to her life. She wants to turn her life around, but at the same time is skeptical that it is possible. Telling her mother (played by Tammy Grimes), who is a rich old woman who can't believe that her daughter lives with a woman much less a "German" (which is all she refers to Greta as), that she has a love issue and a drug problem, or a drug issue and a love problem, you can see her gathering the strength to tell her mom about her drug addiction. But as soon as she does, her mother gets up and walks away, meeting her daughter's expectations. You can tell that this has happened time and again in their strained relationship.
Mitchell and Sheedy have chemistry, and Mitchell is convincing as Syd, movingly conveying her admiration for Lucy as it evolves from a crush to infatuation. She is engaged by being in the presence of such a gifted artist, and her need to be around that fits so perfectly with Lucy's own needs to be appreciated that in the end several interesting questions are posed, including how subjective Syd could ultimately be about Lucy's work because of their relationship and her own involvement with the photographs. Would Syd and Lucy have become involved if the art aspect was completely removed from the situation? See "High Art" and judge for yourself.
© 1999 Heather Picker
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