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The result is a much cleaner system with the AST serving as a well-defined and fixed intermediary between parsing and code generation.
The rest of this document describes two relatively independent code sets. First, the internal operation of the compiler is discussed so that a developer has the ability to add translation to a new language if desired. Second, the internal operation of the C language runtime is described.
The AST approach differs from that used in Google's protobuf compiler. Google's compiler also has, effectively, an AST underlying it, but its overall structure is mostly hidden. Instead, the Google compiler uses a relatively pure O-O approach in which the nodes of the tree are mapped to classes that are extended with code to perform various semantic and code-generation operations.
Aside from the relative merits of C++ (Google) versus Java (Unidata), the resulting AST-based compiler provides a much more centralized approach for defining the generation of the output code. Basically this means that one can look at the file CGenerator.java and see in one place all the procedures involved in code generation. Extending the system to provide, say, support for translation to another language, then just requires the developer to duplicate CGenerator.java and modify it to generate the desired new language output.
The parser initially produces a raw AST tree. The file ProtobufSemantics.java walks the raw tree and (1) checks for semantic legality and (2) modifies the tree to fully implement some the interface procedures listed in Appendix A.
The semantic processor performs the following actions in the following order.
Object optionLookup(String key);
The setAnnotation procedure associates an arbitary object with a specified AST node. The getAnnotation function returns any annotation associated with a specified AST node. If there is no annotation, then the Java value null is returned.
The C code translator makes little use of the annotation capability. It annotates the top level AST.File node with instances of the following structure.
static public class Annotation { String fileprefix = null; String filebase = null; Listincludes = null; }
The fileprefix is the directory and the filebase is the filename of the output file. The includes field list the set of #includedirectives to place in the output file.
The Runtime structure is defined as follows.
struct Runtime { unsigned long long uid; ast_iomode mode; ast_runtime_ops* ops; void* stream; int err; }The fields have the following semantics.
| uid | — | A unique number identifying who created runtime object: e.g. ast_byteio. |
| mode | — | Track the mode of operation: encode/write/serialize, decode/read/deserialize, and free. |
| ops | — | Dispatch table for standardized operations (see below). |
| stream | — | Opaque object on which I/O is performed. |
| err | — | This is a place to store error return codes. It is the ast equivalent to the C errno global variable. |
Additionally, ast_runtime.h defines a type, called bytes_t, that is used to support the reading and writing of counted byte strings. Its definition is as follows.
typedef struct bytes_t{ size_t nbytes; unsigned char* bytes; } bytes_t;
| AST_NOERR | — | The normal return value when no error occurred. This is always defined to have the value zero. All other ast error codes are defined as negative integers. |
| AST_EOF | — | An end-of-file was encountered in reading from a stream. This may or may not be a fatal error. |
| AST_ENOMEM | — | Allocatable memory is exhausted. |
| AST_EFAIL | — | Generic failure of some kind; log output may provide more information. |
| AST_EIO | — | An I/O error occurred in reading or writing a stream. |
Additional errors will be added over time, so this list may be out of data with respect to ast_runtime.h.
The function ast_strerror will convert an error code to a printable string providing a readable error message.
The ast runtime system comes with one such I/O mechanism that reads and writes in-memory byte strings. Additional I/O mechanisms can be defined and used as long as they adhere to the semantics of the operations. It would be possible, for example, to write a I/O mechanism wrapping a C FILE* based input/output, or socket-based I/O.
The standardized operations (ast_runtime_ops) are defined as follows.
struct ast_runtime_ops { size_t (*write)(ast_runtime*,size_t,uint8_t*); size_t (*read)(ast_runtime*,size_t,uint8_t*); ast_err (*mark)(ast_runtime*,size_t); ast_err (*unmark)(ast_runtime*); ast_err (*reclaim)(ast_runtime*); void* (*alloc)(ast_runtime*,size_t); void (*free)(ast_runtime*,void*); };
The semantics of the operations are defined in the following table.
| write | — | Writes N bytes of data to the stream object. |
| read | — | Reads N bytes of data from the stream object. |
| mark | — | Marks the input stream so that an attempt to read more than N bytes will cause an EOF. Multiple nested marks are supported. |
| unmark | — | Restore previous mark limit in LIFO order. |
| reclaim | — | Reclaim (i.e, free) this ast_runtime instance. |
| alloc | — | Wrapper for malloc/calloc; allocates a chunk of memory. |
| free | — | Wrapper for the free function; frees previously allocated memory. |
Each function is passed the controlling ast_runtime object as its first argument. This allows the I/O mechanism to access at least the opaque stream object, cast it as necessary, and then operate on it.
For each such I/O mechanism, one additional procedure is usually implemented: one to create the opaque stream object.
Looking at ast_byteio.h, these two procedures are defined as follows.
ast_err ast_byteio_new(ast_iomode, void* buf, size_t len, ast_runtime**);
This procedure creates either a readable or writeable (as defined by the first argument) byte stream. For reading, the actual underlying buffer and its size are provided. For writing, the buffer is NULL and will be created by the procedure. There are actually two return values, the ast error code is returned as the procedure value. Additionally, an ast_runtime object is created and a pointer to it is returned in the last argument.
| Type | Primary Fields ("getters") | Notes | |
|---|---|---|---|
| — |
| Parent type for all AST types. | |
| Root | — |
| Root node of the AST tree. |
| File | — |
| Initial .proto file or import file. |
| EnumValue | — |
| |
| Package | — |
| |
| Enum | — |
| |
| EnumValue | — |
| |
| Extend | — |
| |
| Extensions | — |
| |
| Field | — |
| |
| Group | — |
| Not implemented. |
| Message | — |
| |
| Option | — |
| |
| CompoundConstant | — |
| |
| Pair | — |
| |
| RPC | — |
| |
| Service | — |
| |
| PrimitiveType | — |
|


PICO-8 Escape Upper Case Strings
Source string
Use upper case for upper case. Do not add surrounding quotes.
Encoded string
Paste this directly in to PICO-8. It will print
the way the source looked.
by @CasualEffects. This tool is open source at my github.


| Concept Framework 2.2 documentation | Contents | Index |
| Name | Type | Access | Version | Deprecated |
| Justify | property | public | version 1.0 | no |
| Read/Write property Justify[=JUSTIFY_LEFT | JUSTIFY_RIGHT | JUSTIFY_CENTER | JUSTIFY_FILL] |
| Sets or gets the alignment of the lines in the text of the label relative to each other. JUSTIFY_LEFT is the default value when the control is first created. |
| Documented by Eduard Suica, generation time: Sun Jan 27 18:15:07 2013 GMT | (c)2013 Devronium Applications |


Class TranscriptNormalization.Builder (2.0.0)
public static final class TranscriptNormalization.Builder extends GeneratedMessageV3.Builder<TranscriptNormalization.Builder> implements TranscriptNormalizationOrBuilder
Transcription normalization configuration. Use transcription normalization
to automatically replace parts of the transcript with phrases of your
choosing. For StreamingRecognize, this normalization only applies to stable
partial transcripts (stability > 0.8) and final transcripts.
Protobuf type google.cloud.speech.v1p1beta1.TranscriptNormalization
Static Methods
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()
Returns
Methods
addAllEntries(Iterable<? extends TranscriptNormalization.Entry> values)
public TranscriptNormalization.Builder addAllEntries(Iterable<? extends TranscriptNormalization.Entry> values)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameter
| Name |
Description |
| values |
Iterable<? extends com.google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry>
|
Returns
addEntries(TranscriptNormalization.Entry value)
public TranscriptNormalization.Builder addEntries(TranscriptNormalization.Entry value)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameter
Returns
addEntries(TranscriptNormalization.Entry.Builder builderForValue)
public TranscriptNormalization.Builder addEntries(TranscriptNormalization.Entry.Builder builderForValue)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameter
Returns
addEntries(int index, TranscriptNormalization.Entry value)
public TranscriptNormalization.Builder addEntries(int index, TranscriptNormalization.Entry value)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameters
Returns
addEntries(int index, TranscriptNormalization.Entry.Builder builderForValue)
public TranscriptNormalization.Builder addEntries(int index, TranscriptNormalization.Entry.Builder builderForValue)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameters
Returns
addEntriesBuilder()
public TranscriptNormalization.Entry.Builder addEntriesBuilder()
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Returns
addEntriesBuilder(int index)
public TranscriptNormalization.Entry.Builder addEntriesBuilder(int index)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameter
| Name |
Description |
| index |
int
|
Returns
addRepeatedField(Descriptors.FieldDescriptor field, Object value)
public TranscriptNormalization.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Parameters
Returns
Overrides
build()
public TranscriptNormalization build()
Returns
buildPartial()
public TranscriptNormalization buildPartial()
Returns
clear()
public TranscriptNormalization.Builder clear()
Returns
Overrides
clearEntries()
public TranscriptNormalization.Builder clearEntries()
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Returns
clearField(Descriptors.FieldDescriptor field)
public TranscriptNormalization.Builder clearField(Descriptors.FieldDescriptor field)
Parameter
Returns
Overrides
clearOneof(Descriptors.OneofDescriptor oneof)
public TranscriptNormalization.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
Returns
Overrides
clone()
public TranscriptNormalization.Builder clone()
Returns
Overrides
getDefaultInstanceForType()
public TranscriptNormalization getDefaultInstanceForType()
Returns
getDescriptorForType()
public Descriptors.Descriptor getDescriptorForType()
Returns
Overrides
getEntries(int index)
public TranscriptNormalization.Entry getEntries(int index)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameter
| Name |
Description |
| index |
int
|
Returns
getEntriesBuilder(int index)
public TranscriptNormalization.Entry.Builder getEntriesBuilder(int index)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameter
| Name |
Description |
| index |
int
|
Returns
getEntriesBuilderList()
public List<TranscriptNormalization.Entry.Builder> getEntriesBuilderList()
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Returns
getEntriesCount()
public int getEntriesCount()
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Returns
getEntriesList()
public List<TranscriptNormalization.Entry> getEntriesList()
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Returns
getEntriesOrBuilder(int index)
public TranscriptNormalization.EntryOrBuilder getEntriesOrBuilder(int index)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameter
| Name |
Description |
| index |
int
|
Returns
getEntriesOrBuilderList()
public List<? extends TranscriptNormalization.EntryOrBuilder> getEntriesOrBuilderList()
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Returns
| Type |
Description |
| List<? extends com.google.cloud.speech.v1p1beta1.TranscriptNormalization.EntryOrBuilder> |
|
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
Overrides
isInitialized()
public final boolean isInitialized()
Returns
Overrides
mergeFrom(TranscriptNormalization other)
public TranscriptNormalization.Builder mergeFrom(TranscriptNormalization other)
Parameter
Returns
public TranscriptNormalization.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Returns
Overrides
Exceptions
mergeFrom(Message other)
public TranscriptNormalization.Builder mergeFrom(Message other)
Parameter
Returns
Overrides
mergeUnknownFields(UnknownFieldSet unknownFields)
public final TranscriptNormalization.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
Returns
Overrides
removeEntries(int index)
public TranscriptNormalization.Builder removeEntries(int index)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameter
| Name |
Description |
| index |
int
|
Returns
setEntries(int index, TranscriptNormalization.Entry value)
public TranscriptNormalization.Builder setEntries(int index, TranscriptNormalization.Entry value)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameters
Returns
setEntries(int index, TranscriptNormalization.Entry.Builder builderForValue)
public TranscriptNormalization.Builder setEntries(int index, TranscriptNormalization.Entry.Builder builderForValue)
A list of replacement entries. We will perform replacement with one entry
at a time. For example, the second entry in ["cat" => "dog", "mountain cat"
=> "mountain dog"] will never be applied because we will always process the
first entry before it. At most 100 entries.
repeated .google.cloud.speech.v1p1beta1.TranscriptNormalization.Entry entries = 1;
Parameters
Returns
setField(Descriptors.FieldDescriptor field, Object value)
public TranscriptNormalization.Builder setField(Descriptors.FieldDescriptor field, Object value)
Parameters
Returns
Overrides
setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
public TranscriptNormalization.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
Parameters
Returns
Overrides
setUnknownFields(UnknownFieldSet unknownFields)
public final TranscriptNormalization.Builder setUnknownFields(UnknownFieldSet unknownFields)
Parameter
Returns
Overrides


Before Change
ids = ["EXAMPLE_{}".format(n) for n in range(1, num_examples + 1)]
// create a list of dictionaries as the features
index_width_for_feature_name = math.floor(math.log10(num_features)) + 1
feature_names = []
for n in range(start_feature_num, start_feature_num + num_features):
index_str = str(n).zfill(index_width_for_feature_name)
After Change
// that would be output by `model_params()` instead of the
// original names since that"s what we would get from SKLL
if use_feature_hashing:
index_width_for_feature_name = int(floor(log10(feature_bins))) + 1
hashed_feature_names = []
for i in range(feature_bins):
index_str = str(i + 1).zfill(index_width_for_feature_name)


Here, we provide .txt file downloads of several major entities in our database. Enjoy.
All data are freshly served from our database so that they are always up-to-date.
To download, click any filename listed below.
Notice that some downloads may take quite some time.
If you benefit from any of these, please kindly cite our work,
see Help/FAQ page.


Introduction
Mil Palabras is a unique forum.
It is a forum for
learning Spanish by communicating in Spanish, with other learners, on conversational topics.
It is unique in that the software requires you to use common words; using common words allows other
intermediate level users to read what you write, and helps ensure that you can read what others
write.
As you write, the forum's editer provides interactive assistance on what words are available,
and color-codes non-permissible words. Allowed words include the one thousand most commonly
used words in Spanish.
Why was it created?
There are many websites and other resources for beginners learning their first hundred words in
Spanish. There are
also many resources for advanced students; practically every Spanish language website is a
learning opportunity.
In between, resources for students who know a few hundred words and basic grammar
but who need practice in conversational context are scarce.
This web
application hopes to help with that third group, helping people with minimal
conversational ability practice and improve, in a casual interpersonal style.
Who created it?
I am a
native speaker of English who has studied Spanish for years.
I have
taken two years of community college Spanish, experienced a month of Spanish cultural
immersion education in Mexico, and worked a month on hurricane relief in Puerto Rico (all of
this years ago) but still seem mediocre at Spanish.
I hope to improve
my Spanish here, practicing with you.
David Keeney
Glosario de 1.000 palabras agradables.
- a
- b
- c
- d
- e
- f
- g
- h
- i
- j
- k
- l
- m
- n
- o
- p
- q
- r
- s
- t
- u
- v
- x
- y
- z
Haga clic en una letra inicial (a la izquierda) para mostrar palabras.


Loading your projects...
Hello!
Sign In via User
TOKEN
{{ setupMessage }}
Sign in
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Your Projects
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{{ item.name }}


{{< layout}} {{$pageTitle}}Your old and new passports{{/pageTitle}} {{$header}}
We have to send your old and new passport separately. Your new passport will be sent by special delivery. This is included in the passport fee.


Who we are? That is one of the most import questions a prospective client can ask us. Our answer - one of the most respected law firms in the country that cares about your case, your business, and solbing your problem. We invite you to get to know our attorneys better and understand the depth of experience and talent we have in the links below.


When a valid argument is sound, this means that the conclusion of the argument can be inferred from true statements. When a valid argument is unsound, this means the conclusion of the argument is being inferred from false statements thus making the actual argument less credible.
in-context


Maven project template generator
Project metadata
Group
Artifact
Package
Version
Name
Description
Packaging
Java version
Project type
URL
License
Developer information
E-mail
Name
URL
Id
Distribution management
Use distribution management
Id
Repository
SCM
Use SCM
Type
Relative path
Integrations
Use
JGitFlow plugin
Use
Travis CI
Use
Coveralls
Include common dependencies
Generate


Control
What learnings did you take from this chapter?
There is quite a lot of content covered in this chapter. I guess the important bits for me were the importance of choice over compulsion, the idea of choosing not to feed the monsters, and the references yet again to the importance of focusing on your breathing (the pause).
How did you find the two minutes of mindfulness?
I've been doing one of the meditations on the Headspace website each day. These are 10 minutes and combine several of the practices covered in SIY, particularly the body scan that we looked at last week. I find them really useful because it is guided meditation and this help me to flex that attention muscle, bringing attention back when the mind strays. It always seems a lot longer than just 10 minutes, which I guess is a sign that I am slowing right down, and I always feel calmer and more focused afterwards.


The medicine word is in blue.
The effect word is in red.
Text id
text
Your name:
The relation between medicine word and effect word:
Medicine and its target symptom
Medicine and its side effect
Medicine and its withdrawal effect
Other relation
No relation
The outcome of the relation:
IMPROVED: The symptom is improved or cured
NOT IMPROVED: The symptom is NOT improved
IN PROGRESS: The patient takes the medicine for the symptom, but hasn't mention its outcome yet
Connot determine / unknown
The outcome of the relation:
CAUSED: The side effect is caused by the medicine
NOT CAUSED: The side effect is NOT caused/did not happen by the medicine
Connot determine / unknown
The outcome of the relation:
CAUSED: The withdrawal effect is caused by quitting or reducing the medicine
NOT CAUSED: The withdrawal effect is NOT caused/did not happen by quitting or reducing the medicine
Connot determine / unknown
The case of the relation:
INSTANCE: The relation is described in a real case or instance
KNOWLEDGE: The relation is described in a reference of knowledge, article or data etc.
Unknown or diferent from any of the options above


"""
The implementation code for the command-send GDS CLI commands
"""
import difflib
from typing import Iterable, List
import fprime_gds.common.gds_cli.misc_utils as misc_utils
import fprime_gds.common.gds_cli.test_api_utils as test_api_utils
from fprime.common.models.serialize.type_exceptions import NotInitializedException
from fprime_gds.common.data_types.cmd_data import CommandArgumentsException
from fprime_gds.common.gds_cli.base_commands import QueryHistoryCommand
from fprime_gds.common.pipeline.dictionaries import Dictionaries
from fprime_gds.common.templates.cmd_template import CmdTemplate
from fprime_gds.common.testing_fw import predicates
[docs]class CommandSendCommand(QueryHistoryCommand):
"""
The implementation for sending a command via the GDS to the spacecraft
"""
@staticmethod
[docs] def get_closest_commands(
project_dictionary: Dictionaries, command_name: str, num: int = 3
) -> List[str]:
"""
Searches for the closest matching known command(s) to the given command
name.
:param project_dictionary: The dictionary object for this project
containing the item type definitions
:param command_name: The full string name of the command to search for
:param num: The maximum number of near-matches to return
:return: A list of the closest matching commands (potentially empty)
"""
known_commands = project_dictionary.command_name.keys()
closest_matches = difflib.get_close_matches(command_name, known_commands, n=num)
return closest_matches
@staticmethod
[docs] def get_command_template(
project_dictionary: Dictionaries, command_name: str
) -> CmdTemplate:
"""
Retrieves the command template for the given command name
:param project_dictionary: The dictionary object for this project
containing the item type definitions
:param command_name: The full string name of the command to return a
template for
:return: The CmdTemplate object for the given command
"""
return project_dictionary.command_name[command_name]
@staticmethod
[docs] def get_command_help_message(
project_dictionary: Dictionaries, command_name: str
) -> str:
"""
Returns a string showing a help message for the given GDS command.
:param project_dictionary: The dictionary object for this project
containing the item type definitions
:param command_name: The full string name of the command to return a
help message for
:return: A help string for the command
"""
command_template = CommandSendCommand.get_command_template(
project_dictionary, command_name
)
return misc_utils.get_cmd_template_string(command_template)
@classmethod
[docs] def _get_item_list(
cls,
project_dictionary: Dictionaries,
filter_predicate: predicates.predicate,
) -> Iterable[CmdTemplate]:
"""
Gets a list of available commands in the system and return them in an
ID-sorted list.
:param project_dictionary: The dictionary object for the project
containing the command definitions
:param filter_predicate: Test API predicate used to filter shown
channels
"""
# NOTE: Trying to create a blank CmdData causes errors, so currently
# just using templates (i.e. this function does nothing)
create_empty_command = lambda cmd_template: cmd_template
command_list = test_api_utils.get_item_list(
item_dictionary=project_dictionary.command_id,
search_filter=filter_predicate,
template_to_data=create_empty_command,
)
return command_list
@classmethod
[docs] def _get_upcoming_item(
cls,
api,
filter_predicate,
min_start_time="NOW",
timeout=5.0,
):
"""
NOTE: Doesn't use _get_upcoming_item; sign that this should not use QueryHistory as a base class, and should refactor when time's available
"""
@classmethod
[docs] def _get_item_string(
cls,
item: CmdTemplate,
json: bool = False,
) -> str:
"""
Converts the given command template into a human-readable string.
:param item: The CmdTemplate to convert to a string
:param json: Whether or not to return a JSON representation of "temp"
:return: A readable string version of "item"
"""
return misc_utils.get_cmd_template_string(item, json)
@classmethod
[docs] def _execute_command(
cls,
connection_info: misc_utils.ConnectionInfo,
search_info: misc_utils.SearchInfo,
command_name: str,
arguments: List[str],
):
"""
Handle the command-send arguments to connect to the Test API correctly,
then send the command via the Test API.
For more details on these arguments, see the command-send definition at:
Gds/src/fprime_gds/executables/fprime_cli.py
"""
dictionary, ip_address, port = tuple(connection_info)
is_printing_list, ids, components, search, json = tuple(search_info)
search_filter = cls._get_search_filter(ids, components, search, json)
if is_printing_list:
cls._log(cls._list_all_possible_items(dictionary, search_filter, json))
return
# ======================================================================
pipeline, api = test_api_utils.initialize_test_api(
dictionary, server_ip=ip_address, server_port=port
)
# ======================================================================
try:
api.send_command(command_name, arguments)
except KeyError:
cls._log("'%s' is not a known command" % (command_name))
close_matches = CommandSendCommand.get_closest_commands(
pipeline.dictionaries, command_name
)
if close_matches:
cls._log("Similar known commands: {}".format(close_matches))
except NotInitializedException:
temp = CommandSendCommand.get_command_template(
pipeline.dictionaries, command_name
)
cls._log(
"'%s' requires %d arguments (%d given)"
% (command_name, len(temp.get_args()), len(arguments))
)
cls._log(cls.get_command_help_message(pipeline.dictionaries, command_name))
except CommandArgumentsException as err:
cls._log("Invalid arguments given; %s" % (str(err)))
cls._log(cls.get_command_help_message(pipeline.dictionaries, command_name))
# ======================================================================
pipeline.disconnect()
api.teardown()
# ======================================================================
@classmethod
[docs] def handle_arguments(cls, *args, **kwargs):
"""
Handle the given input arguments, then execute the command itself
NOTE: This is currently just a pass-through method
"""
connection_info = misc_utils.ConnectionInfo(
kwargs["dictionary"], kwargs["ip_address"], kwargs["port"]
)
search_info = misc_utils.SearchInfo(
kwargs["is_printing_list"],
kwargs["ids"],
kwargs["components"],
kwargs["search"],
kwargs["json"],
)
cls._execute_command(
connection_info, search_info, kwargs["command_name"], kwargs["arguments"]
)


Micah 2:5
Therefore thou shalt have none that shall cast a cord by lot in the congregation of the LORD.


Quest Items
When you hunt monsters assigned as part of a quest, they will occasionally drop quest items. These items do not appear on the ground. They automatically appear in your Inventory, making a distinctive sound.
You may check your quest items on the Quest tab of the Inventory window (Tab key or ALT+V).
If you wish to view ongoing quests, quest items, and quest contents, just click on the Quest button on the bottom-right corner of the screen. (Shortcut Key: ALT+U
Close Window


| DOSE: |
4 hits |
smoked |
Cannabis |
(plant material) |
I've read stories here of people having similar experiences. But I just dont understand why this happened with such a small amount of weed. I'll start by explaining that I had bought about $40 worth of weed earlier that day. $20 worth of 'regs' (plain old weed, nothing special) and $20 worth of 'hydro' (some potent stuff).  So I thought I knew exactly what to expect. I was wrong.
I got two of my friends to drive me to a hidden smoking spot (so as not to get caught) and I ended up being the only one who smoked. I packed a very small bowl with the hydro and puffed away. I was amazed at how quickly I felt the effects, so after finishing that bowl, I was done. No repacking, no extra joints on the side. Just one bowl.
We got in the car and started driving home. I felt VERY strange. In no time at all I fell into a very bad trip. I thought that someone was grabbing my trachea. I literally saw someone grabbing my throat and crunching it to bits, every time I would breath in. I knew this was bad news. The hallucinations got so bad that I rolled down the window and threw whatever weed I had left (about $35 worth) right out the window.  Just at that moment a car pulled out in front of us. I thought it was an ambulance and that my friends knew how bad I was tripping and they were trying to catch up to it so it could take me to the hospital. I almost started to cry because I thought that I had reached the end. I thought I was dying. I sat upright and looked down at my legs and arms and chest and I saw only the outlines of my body in bright colorful lines.
At this time I thought 'so this is what it feels like to die'. It didnt hurt at all, I was in no pain... Which made it that much scarier. I couldnt feel anything. The whole ride home seemed to take about an hour (it's really just a 4 minute drive). Eventually, we made it home and into my friends basement. Here is where the problems started getting worse. I had remembered that earlier I thought I was dying, so being in this kids basement (which had no windows and was very dark inside) was hell. I thought my friends had both brought me to hell and they just laughed at me while they watched TV.  I noticed my hands were VERY clammy and I was VERY pale.
I dont remember anything after this point. I dont know how I got home, what happened when I got home, or how long it took me to get home.
I stopped smoking for about 3 months.  I have not yet had an experience quite as terrible as that one night... and I hope I never do.
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.


Friendship, cooperation and progress
We are a positive and loving team. From the beginning of the project design, all team members actively participate in project construction and take advantage of their abilities. Through discussion and cooperation, we have made a lot of progress both within and outside the competition. We developed a close and trusting relationship and became very good friends.
Shizheng Tian
Shuojin Liang
Mingwei Li
Ziyuan Wei
Jingqi Zhang
Tianhui Xu
Zhenru Zhang
Yiyang Yao
Yiyang Lu
Yuanyuan Liu
Emily Mo
Xiaohui Jiang
Xinran Li
Gaochen Zhang
Hongyong Li
Changcheng Li
Signature: Shuojin


Correspondence
Funding allocations to the Higher Education Funding Council for England (HEFCE) for 1 April 2015 to 31 March 2016.
Ref: BIS/14/P199 PDF , 35.4KB , 2 pages
PDF , 36.6KB , 6 pages
PDF , 24.9KB , 5 pages
A letter to Higher Education Funding Council for England ( HEFCE ) , providing details of the allocations we’ll be making to HEFCE for the financial year 1 April 2014 to 31 March 2015. It also includes the priorities we wish to set the council for that year.
The Higher Education Funding Council for England ( HEFCE ) distributes public money for teaching, research, knowledge exchange and related activities.
Published: 10 February 2013


Humans spend approximately one third of their lives asleep. This site is dedicated to informing the public about why sleep is so special, how sleep affects our lives, and why you're probably sleep deprived.
Learn MoreThere are two types of sleep: non-rapid eye movement sleep (NREM), and rapid eye movement sleep (REM).
Within NREM sleep, there are 4 different stages of sleep. Stages 3 and 4 of NREM are referred to as slow wave sleep (SWS).
During REM sleep, your body becomes paralyzed and you experience sporadic eye movements. Dreams usually occur in REM sleep.
Your wake/sleep cycle, or 'circadian rhythm', is controlled by an innate biological clock. The superchaismatic nucleus determines when to secret melatonin, a sleep hormone.
Throughout the night, you cycle through the stages of NREM and REM every 90 minutes. The four stages of NREM happen first, and REM is the final stage of sleep.
The average person should sleep around 9 hours a night. The average college student probably gets about two thirds of that. Not getting enough sleep can have potentially devastating effects, both short term and long term. Here are just a few reasons why you should clock in your 9 (not 8!) hours every night.
Lack of sleep has drastic negative effects on your body’s immune system. After just 4 hours of sleep reduction, there is a 70% reduction in cancer-defense (NK) cells.
Sleep loss is associated with impaired glucose tolerance. After just a week of sleeping for 4 hours a night, patients had glucose tolerance levels similar to those were significantly at risk of diabetes.
After 16 hours of wakefulness, your performance on alertness tasks degrades below the performance of those who are legally drunk. Falling asleep at the wheel is the cause of more than 100,000 accidents per year.
One in five medical residents will seriously harm a patient due to a fatigue related error.
 Lack of sleep can cause overactive emotions, and may even lead to permanent psychological damage in severe cases.
This site was created by Alex Pan, a student in the Spring 2017 Psychology 133 class. The sleep outreach project is designed to educate the public about sleep, using any medium of communication. Websites are a convenient, accessible way to spread information quickly — hence why this site was created!
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