The Scarlet Letter
by Nathaniel Hawthorne
The Scarlet Letter, published in 1850, is set in Puritan New England in the 17th century. Exploring the issues of grace, legalism, and guilt, it tells the story of Hester Prynne, a Puritan woman who commits adultry then struggles to create a new life.
Source: Hawthorne, N. (1850). The Scarlet Letter. Boston, MA: Ticknor and Fields.
- Preface to the Second Edition
- Preface to the novel.
- Introduction: “The Custom-House”
- This introduction provides a frame for the main narrative of The Scarlet Letter. The nameless narrator, who shares quite a few traits with the book’s author, takes a post as the “chief executive officer,” or surveyor, of the Salem Custom House. This section introduces us to the narrator and establishes his desire to contribute to American culture.
- Chapter 1: “The Prison-Door”
- This first chapter contains little in the way of action, instead setting the scene and introducing the first of many symbols that will come to dominate the story.
- Chapter 2: “The Market-Place”
- The narrator introduces the reader to Hester Prynne and begins to explore the theme of sin, along with its connection to knowledge and social order.
- Chapter 3: “The Recognition”
- In the crowd that surrounds the scaffold, Hester suddenly spots her husband, who sent her to America but never fulfilled his promise to follow her. Chillingworth makes inquiries and Hester is preached to.
- Chapter 4: “The Interview”
- Hester and her husband come face to face for the first time when he is called to her prison cell to provide medical assistance.
- Chapter 5: “Hester at Her Needle”
- The narrator covers the events of the years after Hester’s imprisonment.
- Chapter 6: “Pearl”
- Hester’s one consolation is her daughter, Pearl, who is described in great detail in this chapter.
- Chapter 7: “The Governor’s Hall”
- Hester pays a visit to Governor Bellingham’s mansion. She has two intentions: to deliver a pair of ornate gloves she has made for the governor, and to find out if there is any truth to the rumors that Pearl, now three, may be taken from her.
- Chapter 8: “The Elf-Child and the Minister”
- Bellingham, Wilson, Chillingworth attempt to take Pearl away, but Dimmesdale convinces them of Pearl's value to Hester.
- Chapter 9: “The Leech”
- By renaming himself upon his arrival in Boston, Chillingworth has hidden his past from everyone except Hester, whom he has sworn to secrecy. He incorporates himself into society in the role of a doctor, and since the townsfolk have very little access to good medical care, he is welcomed and valued. An opportuniy arises when Dimmesdale comes into his care.
- Chapter 10: “The Leech and His Patient”
- The inwardly tortured minister soon becomes Chillingworth’s greatest puzzle. The doctor relentlessly and mercilessly seeks to find the root of his patient’s condition.
- Chapter 11: “The Interior of a Heart”
- Chillingworth continues to play mind games with Dimmesdale, making his revenge as terrible as possible.
- Chapter 12: “The Minister’s Vigil”
- Dimmesdale mounts the scaffold where his behavior becomes absurd and neurotic followed by the next day’s powerful sermon and the mystery of the burning A in the sky.
- Chapter 13: “Another View of Hester”
- Seven years have passed since Pearl’s birth. Hester has become more active in society.
- Chapter 14: “Hester and the Physician”
- Hester resolves to ask Chillingworth to stop tormenting the minister. Chillingworth recalls his former days of glory and subsequent transformation into an evil man.
- Chapter 15: “Hester and Pearl”
- As Chillingworth walks away, Hester goes to find Pearl. She realizes that, although it is a sin to do so, she hates her husband. Hester and Pearl discuss the meaning of the scarlet letter.
- Chapter 16: “A Forest Walk”
- Intent upon telling Dimmesdale the truth about Chillingworth’s identity, Hester waits for the minister in the forest, because she has heard that he will be passing through on the way back from visiting a Native American settlement.
- Chapter 17: “The Pastor and His Parishioner”
- In the forest, Hester and Dimmesdale are finally able to escape both the public eye and Chillingworth. They join hands and sit in a secluded spot near a brook. Hester tells Dimmesdale that Chillingworth is her husband. The former lovers plot to steal away on a ship to Europe, where they can live with Pearl as a family.
- Chapter 18: “A Flood of Sunshine”
- The decision to move to Europe energizes both Dimmesdale and Hester. Dimmesdale declares that he can feel joy once again, and Hester throws the scarlet letter from her chest. Hester reveals Pearl's heritage.
- Chapter 19: “The Child at the Brook-Side”
- Hester calls to Pearl to join her and Dimmesdale. From the other side of the brook, Pearl eyes her parents with suspicion.
- Chapter 20: “The Minister in a Maze”
- As the minister returns to town, he can hardly believe the change in his fortunes. But all is not as it seems as his behavior becomes erratic, leading to a strange meeting with Chillingworth.
- Chapter 21: “The New England Holiday”
- Echoing the novel’s beginning, the narrator describes another public gathering in the marketplace, but this time the purpose is to celebrate the installation of a new governor. Hester receives uneasines news about her journey and Chillingworth.
- Chapter 22: “The Procession”
- The majestic procession passes through the marketplace. Hester is disheartened to see the richness and power of Puritan tradition displayed with such pomp. News about Chillingworth worries Hester about her future.
- Chapter 23: “The Revelation of the Scarlet Letter”
- Dimmesdale finishes his Election Day sermon, which focuses on the relationship between God and the communities of mankind, and has an encounter with Hester to whom he makes a confession.
- Chapter 24: “Conclusion”
- The book’s narrator discusses the events that followed Dimmesdale’s death and reports on the fates of the other major characters.


{% extends "layout.html" %}
{% block page_title %}
Is your business registered for VAT in the UK? - About the business - Anti-money laundering registration - GOV.UK
{% endblock %}
{% block content %}
{% include "includes/phase_banner_research.html" %}


Properties of particular interest to
dotnet publish
:
-
PublishProfile=Properties\PublishProfiles\profile.pubxml
: Use a .pubxml to set publish-related
properties. See
Visual Studio publish profiles (.pubxml) for ASP.NET Core app deployment
for more information.
-
PublishReadyToRun=true
: Compiles application assemblies as ReadyToRun (R2R) format. R2R is a form of
ahead-of-time (AOT) compilation. For more information, see
ReadyToRun images.
Available since .NET Core 3.0 SDK.
-
PublishSingleFile=true
: Packages the app into a platform-specific single-file executable. The executable is
self-extracting and contains all dependencies (including native) that are required to run the app. When the app is first run,
the application is extracted to a directory based on the app name and build identifier. Startup is faster when the application
is run again. The application doesn't need to extract itself a second time unless a new version is used.
For more information about single-file publishing, see the
single-file bundler design document
.
Available since .NET Core 3.0 SDK.
-
PublishTrimmed=true
: Trims unused libraries to reduce the deployment size of an app when publishing a
self-contained executable. For more information, see
Trim self-contained deployments and executables
.
Available since .NET Core 3.0 SDK.
Note that it is recommended to specify all of these, other than
PublishProfile
, in a publish profile rather than
here.


Donate to our organization to help protect one of the world's most cherished environments.
By donating $10, you will plant one tree in the Amazon rainforest and help counter deforestation.
We also accept sponsorship donations to fund research and rescue missions. Sponsorship money will also go toward sending our volunteer teams to the Amazon to plant trees and help the animals.
Without our sponsors, we would not be able to send teams to plant trees and rescue our wildlife.
Donation Amount
One Time Monthly
$10
$30
$50
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Other
Credit Card Info
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Last Name:


Uses of Class
org.bukkit.event.block.BlockEvent
Package
Description
-
Uses of BlockEvent in com.destroystokyo.paper.event.block
Modifier and TypeClassDescriptionclass
Called when a beacon effect is being applied to a player.class
Fired anytime the server intends to 'destroy' a block through some triggering reason.class
Called when TNT block is about to turn intoTNTPrimed
-
Uses of BlockEvent in io.papermc.paper.event.block
Modifier and TypeClassDescriptionclass
Called when a beacon is activated.class
Called when a beacon is deactivated, either because its base block(s) or itself were destroyed.class
Called when aRaider
is revealed by a bell.class
Called when a bell is rung.class
Called when a block forces another block to break and drop items.class
Called when a block tries to dispense an item, but its inventory is empty.class
Called when the server tries to check the lock on a lockable tile entity.class
class
Called when theEnderDragon
is defeated (killed) in aDragonBattle
, causing aMaterial.DRAGON_EGG
(more formally:BlockGrowEvent.getNewState()
) to possibly appear depending onDragonEggFormEvent.isCancelled()
. -
Uses of BlockEvent in org.bukkit.event.block
Modifier and TypeClassDescriptionclass
Called when a block is broken by a player.class
Called when a block is destroyed as a result of being burnt by fire.class
Called when we try to place a block, to see if we can build it here or not.class
Called when an ItemStack is successfully cooked in a block.class
Called when a player stops damaging a Block.class
Called when a block is damaged by a player.class
Called when an equippable item is dispensed from a block and equipped on a nearby entity.class
Called when an item is dispensed from a block.class
Called if a block broken by a player drops an item.class
An event that's called when a block yields experience.class
Called when a block explodesclass
Called when a block fades, melts or disappears based on world conditionsclass
Called with the block changes resulting from a player fertilizing a given block with bonemeal.class
Called when a block is formed or spreads based on world conditions.class
Represents events with a source block and a destination block, currently only applies to liquid (lava and water) and teleporting dragon eggs.class
Called when a block grows naturally in the world.class
Called when a block is ignited.class
Fired when a single block placement action of a player triggers the creation of multiple blocks(e.g.class
Thrown when a block physics check is called.class
Called when a piston block is triggeredclass
Called when a piston extendsclass
Called when a piston retractsclass
Called when a block is placed by a player.class
Called when a Sculk sensor receives a game event and hence might activate.class
Called when a redstone current changesclass
Event fired when a dispenser shears a nearby sheep.class
Called when a block spreads based on world conditions.class
Called when a brewing stand starts to brew.class
Called when a Campfire starts to cook.class
class
Called when a block is formed by entities.class
Called when the fluid level of a block changes due to changes in adjacent blocks.class
Deprecated.draft APIclass
Called when leaves are decaying naturally.class
Called when the moisture level of a soil block changes.class
Called when a note block is being played through player interaction or a redstone current.class
Called when a sign is changed by a player.class
Called when a sponge absorbs water from the world. -
Uses of BlockEvent in org.bukkit.event.inventory
Modifier and TypeClassDescriptionclass
Called when the brewing of the contents inside the Brewing Stand is complete.class
Called when an ItemStack is about to increase the fuel level of a brewing stand.class
Called when an ItemStack is successfully burned as fuel in a furnace.class
This event is called when a player takes items out of the furnaceclass
Called when an ItemStack is successfully smelted in a furnace.class
Called when a Furnace starts smelting.


tropomi_ut_no2
Process and apply the cloud-slicing approach to partial columns of NO2 from S5P/TROPOMI for June 2019 to May 2020.
The default is to obtain seasonal means at 1x1 for partial columns above clouds with cloud fraction >=0.7 and within the cloud top pressure range of 450-180 hPa.
Options are available to use cloud information from either the FRESCO-S or ROCINN-CAL cloud product, to obtain seasonal means at 2x2.5 or 4x5, and to use a cloud fraction threshold of 0.8, 0.9, or 1.0.
usage:
[-h] [--trop_dir TROP_DIR] [--out_dir OUT_DIR] [--season SEASON]
[--start_date START_DATE] [--end_date END_DATE] [--grid_res GRID_RES]
[--cloud_product CLOUD_PRODUCT] [--cloud_threshold CLOUD_THRESHOLD]
[--pmin PMIN] [--pmax PMAX]
optional arguments:
-h, --help show this help message and exit
--trop_dir TROP_DIR Directory containing tropomi data
--out_dir OUT_DIR Directory to contain finished netcdf4
--season SEASON Can be jja, son, djf, mam
--start_date START_DATE
Start date of processing window (yyyy-mm-dd)
--end_date END_DATE End date of processing window (yyyy-mm-dd)
--grid_res GRID_RES Can be 1x1, 2x25, 4x5
--cloud_product CLOUD_PRODUCT
can be fresco or dlr-ocra
--cloud_threshold CLOUD_THRESHOLD
recommended value is 07. Can also test 08, 09, 10
--pmin PMIN Lower bound on cloud height. Defaults to 180.
--pmax PMAX Upper bound on cloud height. Defaults to 450.
-
class
uptrop.tropomi_ut_no2.
CloudData
(file_path, data_type) Class for containing the data for cloud filtering and analysis.
-
__init__
(file_path, data_type) Reads either the tropomi file (if data_type = ‘fresco’) or the ocra file (if data_type = ‘dlr-ocra’) at file_path and returns an instance of CloudData. Calls either read_fresco_file or read_ocra_file.
- Parameters
file_path (str) – Path to the file containing cloud_data
data_type (str) – Can be ‘fresco’ or ‘dlr-ocra’
-
read_fresco_file
(file_path) Reads and filters the fresco data in a s5p file
- Pixels are dropped if
they have type 255 (ocean)
they have type 252 (coastline; ATBD treats as ‘suspect’)
they have less thant 1% cover
there is a potential misclassification of snow/ice as cloud
- Parameters
file_path (str) – The path to the fresco file
-
read_ocra_file
(file_path) Reads, filters and preprocesses the data in a dlr-ocra file.
uptrop.height_pressure_converter is called to convert ocra cloud-top heights to pressure values for cross-compatability with fresco data.
- Pixels are dropped if
They are over snow/ice scenes
They have quality less than 0.5
- Parameters
file_path (str) – The path to the ocra data
-
-
class
uptrop.tropomi_ut_no2.
GridAggregator
(dellat, dellon) A class for aggregating higher-resolution data into grid squares
-
__init__
(dellat, dellon) Creates a grid aggregator across the entire world of resolution dellon, dellat
- Parameters
dellat (float) – vertical size of the aggregation grid in decimal degrees
dellon (float) – Horizontal size of the aggregation grid in decimal degrees
- Returns
A GridAggregator object
- Return type
-
add_slice
(i, j, t_cld, t_col_no2) Extracts the upper troposphere gc_data, gc_data error and mean cloud pressure for grid square [i,j]
This method uses the cloud-slicing function uptrop.cloud_slice_ut_no2.cldslice Once calculated, the a weighting is derived from cloud pressure. The weighted upper tropospheric gc_data and error is added to the rolling total for this season. If the cloud slicing fails, then the reason is added to loss_count for the end report.
- Parameters
i (int) – X-index of grid square
j (int) – Y-index of grid square
t_cld (list of floats) – A list of cloud pressures
t_col_no2 (list of floats) – A list of gc_data values, of same length as t_cld
-
apply_cloud_slice
(n_slices=40) Applies the cloud slicing algorithm to each square in the populated grid.
This method walks over every gridsquare in gno2/gstrat/gcldp. After sanity checks and conversion to hPa, that pixel’s data is split into [len(pixel)/n_slices] subsets, each of n_slices length. The split is alternating, so in the cast of three subsets pixel_data[0] goes to subset 0, pixel_data[1] to subset 1, pixel_data[2] to subset 2, pixel_data[3] to subset 0, pixel_data[4] to subset 1 and so on. Each subset then has apply_slice applied to it. If a pixel has less than 100 data points in it, subsetting is skipped and apply_slice is run on the pixel’s entire dataset.
- Parameters
n_slices (int) – The number of pixels to apply to each subset
-
calc_seasonal_means
() Calculates the mean no2 mixing ratio using Gaussian weights or counts. This is to be applied at the end of processing to get the final data that will be saved and plotted.
-
-
class
uptrop.tropomi_ut_no2.
TropomiData
(file_path) A class for extracting, preprocessing and containing data from a s5p tropomi file.
-
__init__
(file_path) Reads the tropomi file at file_path and prepares it for processing.
- Parameters
file_path (str) – Path to the netcdf4 file containing the tropomi data
-
calc_geo_column
() Calculates the geometric AMF and tropospheric vertical column of this data. Includes application of a bias correction to the stratospheric and tropospheric columns. These are obtaind from comparing TROPOMI to Pandora surface observations.
The correction addresses an underestimate in TROPOMI stratospheric NO2 variance and a factor of 2 overestimate in TROPOMI tropospheric NO2.
-
cloud_filter_and_preprocess
(cloud_data, cldthld, pmax, pmin) Filters this tropomi data using the cloud information in cloud_data
- Removes data where
There is no cloud data
The fraction of cloud is less than the specified cloud threshold
Cloud heights are not in the range pmin-pmax
Quality value is greater than 0.45
- Parameters
cloud_data (uptrop.tropomi_ut_no2.CloudData) – Instance of CloudData
cldthld (float) – The cloud fraction threshold to be used
pmax (float) – The maximum cloud height to be used in processing
pmin (float) – The minimum cloud height to be used in processing
-


Search
Supports initials or words.
Such as: food
Enter initial: F
Enter word: FOOD
Oooops! It doesn't exist.


Press n or j to go to the next uncovered block, b, p or k for the previous block.
| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| index.js |
|
91.95% | 320/348 | 86.06% | 210/244 | 96% | 48/50 | 92.09% | 303/329 |
| stylefunction.js |
|
66.23% | 255/385 | 61.09% | 179/293 | 78.57% | 11/14 | 66.05% | 251/380 |
| util.js |
|
75.86% | 88/116 | 61.01% | 36/59 | 80% | 8/10 | 76.69% | 79/103 |


Learning Backbone Marionette
Ch3: Router
How to run this example?
Open index.html in browser
Press F12, go to console tab
See the console logs and browser URL to understand the flow
Home
Dashboard
About Us
Read Book
Hello Guest
Hello hegdeashwin


Fire guts Aliu Mahama’s hotel in Tamale
The Mafara hotel belonging to late Vice President Aliu Mahama, in Tamale, has been gutted by fire.
Information gathered indicates that the entire guest rooms at the hotel have been burnt down except the kitchen and conference room of the hotel.
Properties including TV sets, furniture, beds and other valuables of the hotel were burnt into ashes.
The cause of the fire at the hotel was not immediately known but some believed the fire started due to a faulty air-conditioning in one of the rooms.
DGN Online gathered that officials of an insurance company (name withheld ) which insured the hotel visited the scene to ascertain the cause and take records of the damage caused.
Work at the hotel has since been halted and workers are lamenting about when the situation will be fixed.
Meanwhile, authorities of the Ghana Fire Service in the region are yet to make the public know their findings about the fire incident at the Mafara hotel.
The incident occurred on Tuesday.


Germination and emergence are predicted based on the accumulation of thermal time and soil moisture based on the SHOOTGRO model (Wilhelm et al., 1993. Ecological Modelling 68:183-203). Four soil water levels are available and the user inputs the condition of the soil at planting time. The GDD required for germination increases and the elongation rate decreases as water content decreases. After planting, precipitation can shift the soil moisture condition upwards toward Optimum but there is no provision reducing the soil moisture level due to evaporation.
The four soil moisture levels are: Optimum, Medium , Dry and Planted in Dust . These are shown for Winter Wheat in Table 1 below which is found in Table 2 in the reference cited above. Other input parameters include the lower and upper Water-filled pore space (wfpslo and wfpsup), Germination time (germgdd ), and Elongation rate (ergdd ).
Daily precipitation is read in from the weather file and is given in mm.
Seeds planted in soil water conditions considered to be ‘Planted in Dust’ are assumed to not begin the germination process until some minimal soil water level is reached (i.e., 'Dry' conditions). Therefore, seeds Planted in Dust must move up to the Dry soil moisture level before beginning the accumulation of thermal time required for germination. At least seven mm or more of precipitation must be received before soil moisture can move from Planted in Dust to Dry conditions.
In order to provide a more realistic process of germination, two intermediate soil moisture levels were added. These occur between Optimum and Medium and between Medium and Dry. These were necessary because germination was occurring too rapidly with only the four soil moisture conditions named above.
Once germination has occurred, the elongation rate of the coleoptile (ergdd) is increased based on the input elongation rate in mm per growing degree-day for the soil moisture condition. This value is multiplied by the day’s growing degree-day value and added to the elongation amount already achieved. Once the elongation length is greater than the planting depth , emergence has occurred.
TABLE 1 (from Table 2 in the Ref. cited above)
Seed germination and seedling elongation rates for several broad categories of seedbed conditions.
Seedbed condition |
Water-filled pore space (%) |
Germination time (GDDa) |
Elongation rate (mm GDD-1) |
Optimum |
> 45 |
80 |
0.50 |
Barely adequate |
35-45 |
90 |
0.40 |
Dry |
25-35 |
110 |
0.33 |
Planted in Dust |
<25 |
- |
- |
| aGDD = growing degree-day (base 0°C) Wheat |
Links to: GDD Methods


Using CSS- Cascading Style Sheet.
Bold, biggest header1
Do I have 4 spaces ? Nope, No spaces more than one counted !
Paragraph in normal font
- Item1
THis is without a bullet coz no li used- Item2
- 40Sixty
- Steps to
- Fish around
- Under the sun
REFRESH ?
Page 1
New Page WIP


{% with page='components' %} {% include '%s/nav.html' % navbarDir %} {% endwith %}
Toggle navigation
Sidebar menu
CLARIAH Components
How to
Collection
Collection analyser
Collection selector
Named query selector
Search
Facet search
Named query search
Comparative search
Play-out
Segmenting player
JWPlayer
Vimeo player
YouTube player
Image viewer
Annotation
Commenting
Classifying
Linking
Data visualisations
Line chart (TODO)


DatasetFactory¶
AudioDatasetBase¶
-
class
shabda.data.
AudioDatasetBase
(hparams, unknown_label='_unknown_')[source]¶ Bases:
object
Interface class for Audio Datasets.
Any audio dataset is expected to inherit this class and give implementation. AudioDataset expectations are: - Provide list of training, validation and test files files - Expose list of lables and predefined labels, which then can be used for label indexing
-
get_dataset_name
()[source]¶ Inheriting class must take care of implementing this method :return: Name of the dataset
-
get_index_2_label
(index)[source]¶ Returns the label string, considering the predefined labels :param index: int :return: label: string
-
get_label_2_index
(label)[source]¶ Returns the index of the label, considering the predefined labels :param label: string :return: index: int
-
get_labels
()[source]¶ Inheriting class must take care of implementing this method :return: List of lables for the dataset under consideration
-
get_labels_dim
()[source]¶ Returns the total number of labels in this dataset along with any deafault lables like silent, back_ground_noise, if any. :return: int
-
get_num_train_samples
()[source]¶ Inheriting class must take care of implementing this method :return: int Number of training sample for current dataset
-
get_one_hot_encoded
(label)[source]¶ Returns the one-hot encoded array of the label :param label: string :return: np.array
-
get_predefined_labels
()[source]¶ Inheriting class must take care of implementing this method :return: list of predefined lables. Eg: [“_silence_”, “_background_noise_”]
-
get_test_files
()[source]¶ Inheriting class must take care of implementing this method :return: list of test files
-
get_test_labels
()[source]¶ Inheriting class must take care of implementing this method :return: list of labels in sync with test files
-
get_train_files
()[source]¶ Inheriting class must take care of implementing this method :return: list of files for training
-
get_train_labels
()[source]¶ Inheriting class must take care of implementing this method :return: list of labels in sync with training files
-
get_val_files
()[source]¶ Inheriting class must take care of implementing this method :return: list of validation files
-
get_val_labels
()[source]¶ Inheriting class must take care of implementing this method :return: list of labels in sync with validation files
-
init
()[source]¶ Inheriting class must take care of implementing this method Should call self._setup_labels() along with other initializations :return: None
-
FreeSoundAudioDataset¶
-
class
shabda.data.
AudioDatasetBase
(hparams, unknown_label='_unknown_')[source] Bases:
object
Interface class for Audio Datasets.
Any audio dataset is expected to inherit this class and give implementation. AudioDataset expectations are: - Provide list of training, validation and test files files - Expose list of lables and predefined labels, which then can be used for label indexing
-
static
default_hparams
()[source] Exposes the default params for this module :return: dict: params
-
get_dataset_name
()[source] Inheriting class must take care of implementing this method :return: Name of the dataset
-
get_index_2_label
(index)[source] Returns the label string, considering the predefined labels :param index: int :return: label: string
-
get_label_2_index
(label)[source] Returns the index of the label, considering the predefined labels :param label: string :return: index: int
-
get_labels
()[source] Inheriting class must take care of implementing this method :return: List of lables for the dataset under consideration
-
get_labels_dim
()[source] Returns the total number of labels in this dataset along with any deafault lables like silent, back_ground_noise, if any. :return: int
-
get_num_train_samples
()[source] Inheriting class must take care of implementing this method :return: int Number of training sample for current dataset
-
get_one_hot_encoded
(label)[source] Returns the one-hot encoded array of the label :param label: string :return: np.array
-
get_predefined_labels
()[source] Inheriting class must take care of implementing this method :return: list of predefined lables. Eg: [“_silence_”, “_background_noise_”]
-
get_test_files
()[source] Inheriting class must take care of implementing this method :return: list of test files
-
get_test_labels
()[source] Inheriting class must take care of implementing this method :return: list of labels in sync with test files
-
get_train_files
()[source] Inheriting class must take care of implementing this method :return: list of files for training
-
get_train_labels
()[source] Inheriting class must take care of implementing this method :return: list of labels in sync with training files
-
get_val_files
()[source] Inheriting class must take care of implementing this method :return: list of validation files
-
get_val_labels
()[source] Inheriting class must take care of implementing this method :return: list of labels in sync with validation files
-
init
()[source] Inheriting class must take care of implementing this method Should call self._setup_labels() along with other initializations :return: None
-
load_labels_index_map
(file_path)[source] Reads the JSON file from the path and loads them into dataset label indexer :param file_path: File path of the JSON :return: None
-
store_labels_index_map
(file_name='labels_index_map.json')[source] Stores teh current label index as json, as per the path labels_index_map_store_path specified in the params Full store path: labels_index_map_store_path/dataset_name/ :return: None
-
static


--- title: rocStats layout: post ---
While I was at Rensselaer Polytechnic Institute I was very active in the Outing Club. We ran all sorts of trips out each weekend. Examples include hiking, white water rafting, white kayaking, caving, cross country skiing, ice climbing, and rock climbing. As you might expect college students don't really have the money to own gear for all of these trips, so one of the perks of having a very active outing club was that we had gear for all of these activities.
Now the combination of being a club that needs to keep track of a bunch of gear and being at a technology school, means of course we had a website with a database keeping track of it all. I helped with the recent migration and reconstruction of this site so a year later I was curious to see how it was working. So I sent a message to the current webmaster and got a dump of our rentals table. Turns out in the last school year we had 893 rentals! Now this intrigued me. With this sample could I pull some meaning out of what gear was being borrowed? We shall see.
First step was to figure out which types of gear were being signed out. Fortunately the system we use for id-ing our gear helps with that. We use a XX-YY-00 type format where XX is a 2 letter code for the activity, YY is a 2 letter code for the type of equipment, and 00 is the 2 digit number for the individual item. For example WM-TN-01 is Winter Mountaineering, Tent, 01. Below is the split of rentals by activity.
| Activity | Number of Rentals |
|---|---|
| Winter Mountaineering | 356 |
| Kayaking | 198 |
| Canoeing | 122 |
| Ice Climbing | 57 |
| Caving | 56 |
| Rock Climbing | 46 |
| Cross Country Skiing | 28 |
| Mountain Biking | 19 |
| Photography | 5 |
| First Aid | 4 |
| Rafting | 2 |
This in some respects isn't surprising. Winter Mountaineering as an activity includes tents, sleeping bags, flashlights and most general camping equipment. This means it should have the most rentals for 2 reasons, you need these items to do a lot of other activities and we have a fall outing club wide camping trip every fall that is our single most attended event.
Now Looking deeper beyond the activity level I wanted to look at what types of items are being signed out. The following bar chart shows the number of rentals by item.
Looking at this graph we can see that winter mountaineering items are all near the top, particularly Sleeping Bags (SB), Tents (TN), and Backpacks (BP). But there seems to be some outliers here too, PFDs (CN-PF), Booties (KA-BT), and Helmets (KA-HT). The reason these show up are because of one day trips. When we go on a trip white water rafting we will sign out gear like this in bulk but just for a couple hours. This leads to looking into length of rentals.
Next we look at the average time out by item. This is shown in the graph below.
Now what does this tell us. Rather than what I hoped which was proving that sleeping bags, tents and backpacks are the hottest items, it actually showed me that there are other items closely guarded. So Bivy Sacks (BV), water pumps (WP), and bike bags (BG) or the most popular items...or more likely the items that everyone forgets to sign back in. Welp the system doesn't always work the way you want it to, but something for the Pit Chair (gear manager of sorts) to keep in mind is people who sign out things like rock climbing ropes (RC-DR) should be pestered to bring them back otherwise it may sit in their dorm room all year.
Something else I wanted to look at was specific items within a item type like sleeping bag. For instance is sleeping bag 01 signed out much more frequently than 02 or 03? This could mean that we should get more of the brand, temperature or style of the first bag. First I looked at sleeping bags.
| Inventory Code | Name | Description | Count |
|---|---|---|---|
| WM-SB-41 | Sleeping Bag | Black and Blue Kelty 45 degree | 6 |
| WM-SB-26 | Sleeping Bag | 0 deg light blue | 5 |
| WM-SB-34 | sleeping bag | blue 20F Mountain Hardware | 5 |
| WM-SB-01 | Sleeping Bag | 32 deg orange/gray | 4 |
| WM-SB-02 | Sleeping Bag | 0 deg grey/red | 4 |
| WM-SB-06 | Sleeping Bag | 15 deg marmot blue/gray | 4 |
| WM-SB-09 | Sleeping Bag | orange/gray 32 deg | 4 |
| WM-SB-11 | Sleeping Bag | Mountain hardware, orange/gray | 4 |
| WM-SB-03 | Sleeping Bag | Yellow, marmot | 3 |
| WM-SB-08 | Sleeping Bag | 15 deg, blue/gray | 3 |
From this table we see that there really isn't any special sleeping bag. In fact this just tells us that it's good that we have a variety of bags because it seems that all different types and temperatures are signed out regularly. The next item I wanted to look at was tents.
| Inventory Code | Name | Description | Count |
|---|---|---|---|
| WM-TN-24 | Tent | Mountain Hard Wear, drifter 3 | 5 |
| WM-TN-29 | Marmot Limelight 2P | Orange 2-person tent with ground tarp, stakes, and rainfly. 3-season, lightweight design. | 5 |
| WM-TN-08 | Tent | 2 person, 3 season, gray bag | 4 |
| WM-TN-21 | Tent | blue bag | 4 |
| WM-TN-34 | Tent | 3 person, 3 season, orange/gray ozark trail bag | 4 |
| WM-TN-09 | Tent | 2 person, 3 season, dark blue bag | 3 |
| WM-TN-11 | Tent | 3 season | 3 |
| WM-TN-22 | Tent | blue bag | 3 |
| WM-TN-23 | Tent | 2 person, 4 season, orange bag | 3 |
| WM-TN-25 | Tent | 2 person, 3 season, gray bag | 3 |
Again nothing really interesting about tents, except that maybe better descriptions would be a good thing, but I digress (also totally a joke for all I know it could have been me who entered it). Finally I looked at backpacks.
| Inventory Code | Name | Description | Count |
|---|---|---|---|
| WM-BP-19 | Osprey Kode 38 | Blue | 5 |
| WM-BP-12 | Orange Ospray | Orange Ospray backpack | 4 |
| WM-BP-13 | Backpack | 40L courser | 4 |
| WM-BP-21 | Osprey Kode 38 | Dark Green | 4 |
| WM-BP-25 | Osprey Exos 46 | Brown and Grey, UltraLight | 4 |
| WM-BP-09 | EMS Ridgeline | Red and Black, Small | 3 |
| WM-BP-34 | Gregory | Black and Green | 3 |
| WM-BP-01 | Backpack | North Face, blue, terra 40, large backpack | 2 |
| WM-BP-22 | Osprey | Green/grey | 2 |
| WM-BP-24 | Osprey Talon | 22L Backpack | 2 |
Finally we can pull some significance out of this data.
So I posted some pretty pictures and some tables, but what does it mean. There were a couple key things I got out of this analysis. First of all the number of rentals per activity table could be used to help allocate the budget. I already know that Winter Mountaineering is the largest budget of the club so we got that one right, but we could look into other activities that have smaller budgets but maybe a lot of rentals. Another key point would be that there are some items that people aren't returning. From the Average Time Out graph we can see that some items are out for months without being returned. Finally although not as convincing as I might have hoped we do have some items that more popular than others in terms of sleeping bags, tents and backpacks therefore it might be worth considering getting more of those types when purchasing new gear. All the code used for this project is on GitHub at rocStats.


CHARTS
Data is messy, often unbalanced and mislabeled. Data visualizations can be even messier, with lines overlapping, visuals being cluttered, or data points to small to be shown. The facet approach partitions a plot into a matrix of panels. Each panel shows a different subset of the data.
There are two main functions for faceting :


I had seen The Boy Friend being performed by Edinburgh Music Theatre a couple of years before, and thought that it would be fun to do. But when I got the part of Bobby, and realised that I was playing opposite Amanda, it gave me pause for thought. She is so good on stage and such a good dancer, I wondered if it would ever be possible to dance beside her and not appear to be out of my depth!
I don't know whether I managed it, but I certainly had a great time trying. The rehearsals and the show itself all went smoothly. And it was lovely to be working with Dorothy again, who had directed me in Carousel way back in... Ulp!... 1991!
As if that wasn't enough to make me feel old, I also got my twenty-five year long service award from NODA, presented to my by Jim Allardyce! It was presented to me on stage after the show - lovely! But then again, I was being given my twenty-five year award in a show where I was playing a seventeen year old. Something wrong there, surely!


This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment, and found herself walking.
Beautifully designed, fully customizable
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Jane Stevens
UI Designer
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Betty Weaver
Blogger
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Web Designer
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Craig Wagner
Web Developer
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Entrepreneur
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Perfect landing page for web and mobile apps
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CSS Text Effects
I am clipped. I am clipped.I am clipped.I am clipped.I am clipped.I am clipped.I am clipped.I am clipped.I am clipped.I am clipped.I am clipped.
I've ellipsis.I've ellipsis. I've ellipsis. I've ellipsis. I've ellipsis. I've ellipsis. I've ellipsis. I've ellipsis. I've ellipsis.
Hover over me. Hover over me. Hover over me. Hover over me. Hover over me. Hover over me. Hover over me.
I can break words. I can break words. I can break words. I can break words. I can break words. I can break words. I can break words.


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Date of birth
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{{item.datebirth | date:'dd/MM/yyyy'}}
Edit
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Ezekiel 37:5
Thus saith the Lord GOD unto these bones; Behold, I will cause breath to enter into you, and ye shall live:


Advanced Settings
Advanced settings are accessed by clicking on
the 'Brain button', a feature well-known to users of the original PHD.
PHD2 has a considerably larger set of parameters that can be
adjusted to optimize your guiding performance. Although these
are
called "advanced" settings, they are not particularly difficult to
understand, and you shouldn't hesitate to explore them. All
of
the fields on these forms include "tool tips", small message windows
that describe each field in some detail. Simply
"hover" the
cursor over the field to see the tool-tip. In many cases,
this
will provide all the information you need. Because there are
many more settings available, the Advanced Dialog in PHD2 is organized
into tabs, and the documentation will follow that model as well.
Global Tab
Guiding Tab
Camera Tab
Mount Tab
AO Tab
Rotator Tab
Global Tab
Most of the fields on the global tab deal with overall program
functions and behavior.
General Parameters
The remaining controls on the
'Global' tab are well-described by their respective tool-tips, but they
are summarized here for completeness:
- 'Reset Configuration' - restores all settings to their
initial values as if PHD2 had been freshly installed
- 'Image format' - specifies the file format if
star-image logging is enabled
- 'Dither
RA only' - for companion apps that use the PHD2 server interface,
specifies that dithering should be done only on the RA axis
- 'Dither scale' - controls the magnitude of the dithering
operation
- 'Noise
reduction' - specifies the algorithm to use for handling noisy guide
camera images - those for which dark frames are not sufficient.
Choices include None, 2x2 mean, and 3x3 median. Both 2x2 mean
and 3x3
median will reduce the noise considerably. 3x3 median is especially
effective at removing hot pixels and neither will significantly affect
guiding accuracy. However, creating a bad-pixel map is
likely to be a better solution with less impact on your ability to
detect faint stars.
- 'Time lapse' - imposes a fixed delay between
guide exposures. This can be useful if the guide exposures
are
very short and you don't want to overload either the mount or the
camera
link with very high traffic rates.
- 'Focal length' - the focal length of the guide scope (millimeters). This provides one of
two parameters needed by PHD2 to compute the image scale and thus
report guiding performance in units of arc-seconds. The other
parameter required for this is the guide camera pixel size, located on the
'Camera' tab.
- 'Language' - determines the language
used in the PHD2 user interface, subject to available localization.
Changing this will usually require a program restart
- 'Auto restore calibration' - tells
PHD2 to automatically reload the most recent calibration data as soon
as the equipment is connected. Be sure to read the Auto calibration section to understand the implications
and potential risks of doing this.
- 'Auto Exposure' - these are the settings that control Auto exposure time.
- Min Exposure - the minimum exposure time. PHD2 will not set the exposure time
less than this value, even if the guide star SNR is higher than the target SNR value.
If the min exposure time is set too low, you could end up sending excessive small
corrections to the mount which may not give the best guiding results. Users with AO
units would want to set this to a lower value, since rapid small corrections are often
desirable with an AO.
- Max Exposure - the maximum exposure time. Before a guide star is selected, PHD2
will set the exposure time to the maximum value. Once a guide star is selected, PHD2
will then incrementally decrease the exposure time until the desired SNR is reached.
- Target SNR - this is the average SNR value that PHD2 will attempt to achieve
by adjusting the exposure time. SNR can fluctuate from frame to frame even with a
fixed exposure duration, so be sure to account for that when choosing a target SNR
value. PHD2 will reject frames when SNR drops below 3.0. The default value of 6.0
should provide enough of a cushion to prevent fluctuations from causing the SNR to
go below 3.0.
Guiding Tab
There are relatively few parameters found on the 'Guiding' tab.
Their use is as follows:
- 'Always
Scale Images' - tells PHD2 to always rescale the guider image to fit
the display window. PHD2 does this automatically in almost
all
cases, so you'll rarely need to do this.
- 'Fast
re-center after calibration or dither' - during calibration or
dithering, the mount may be moved a significant distance from the
initial "lock" position. If you click this checkbox, PHD2 will
move the mount back to the lock position as quickly as possible,
using the largest guide commands permitted by the 'Max Duration' settings of
your guide algorithms. This is only an optimization, so the use
of this checkbox is completely optional.
- 'Search
region' - specifies the size of the "tracking rectangle", in units of
pixels. You may need to increase this value if your mount
does
not perform well or, more commonly, if it's not well-aligned on the
celestial
pole.
- 'Star mass change detection' - tells PHD2 to monitor the
brightness and size of the guide star compared to the sky background.
- 'Star mass tolerance' - if 'star mass change detection'
is checked, PHD2 will trigger a 'lost star' error if the measured
brightness and size vary by more than this percentage. This
might be useful if you have two stars inside the tracking rectangle and
you want to be sure that PHD2
doesn't mistakenly switch stars.
It can also
prevent errors caused by thin clouds, high camera noise, or alpha
particle "hits";
but it may be unreliable if you are guiding on a faint star. If
you are getting too many 'lost star' errors when the star is plainly
visible on the display, try increasing the value of this setting.
Setting the value to 100 effectively disables the warnings
entirely.
Camera Tab
The controls on the 'Camera' tab are used as follows:
- 'Use subframes' - For cameras that support this
feature, PHD2
will download only a subframe of each guide
exposure. This is very useful for cameras with slow download times
and can mean the difference between the camera being too slow
to effectively guide and being quite responsive. When this feature is
enabled,
only a small subframe (100x100 pixels) will be downloaded once a star
is selected. This feature applies to both calibration and guiding.
During initial looping without a selected star, the full
frame is downloaded, but once a star is selected, only this small
subframe is downloaded. If you are using subframes but want to
see the full frame to select a different star, just click anywhere
outside the subframe.
- 'Pixel
size' - The guide camera pixel size in microns. This is the second of two parameters needed by PHD2 to compute the
guider image scale and thus report guider statistics in units of
arc-seconds. Refer to your camera documentation to determine
the
correct value.
- 'Camera gain' - Sets the gain level for the many cameras
that support this feature. If
you really want to use a bright star and a longer exposure duration or
if your camera makes a very noisy image, try reducing this parameter.
- 'Disconnect
nonresponsive camera after (seconds) - Camera malfunctions will
sometimes occur, often because of faulty USB connections. In many
cases, the camera will not return the requested image data, and PHD2
will appear to "hang." This parameter determines
how long PHD2 should wait for a response after the expected exposure
time has expired. For example, a timeout value of 5 seconds in
conjunction with an exposure time of 2 seconds will tell PHD2 to wait
up to 7 seconds for a response. If the data are not received
within that period, PHD2 will attempt to halt the operation, disconnect
the camera, and display an alert message in the main window.
Since a hardware problem is likely the underlying issue, this
recovery attempt won't always succeed. It is advisable to be
generous with these timeout values to avoid spurious recovery
actions. Also, if you are using a guide camera that shares
electronics with the main imaging camera, you should set this timeout
to a large
value, well above the maximum expected time for a full-frame download
from the main imager. At this time, this affects users of
the SBIG driver that is packaged with Sequence Generator Pro.
Regardless of whether PHD2 is able to handle the situation
gracefully, the underlying problem is almost certainly in the
hardware or the camera driver and will need to be resolved before
guiding is continued.
Mount Tab
The
mount tab is the most complex area of Advanced Settings largely because
it has so many parameters and UI controls. Most of these
settings
are closely tied to the various guiding algorithms, and the
contents of the dialog will change significantly if you
change the algorithm selections. For that reason, all the
parameters related to guide algorithms will be treated together, in a
separate section.
The remaining controls, the ones that are independent of the guiding
algorithm selections, are described below.
- 'Clear calibration'
- tells PHD2
you want to clear the calibration data currently being used and
re-calibrate
before
guiding is restarted. You might do this for a variety of
reasons
- you might have moved the guide camera, you might have done a meridian
flip and want to recalibrate on the "new" side of the pier, etc.
You can also accomplish the same result by doing a Shift-Click on
the guiding icon on the main page, which will force a re-calibration.
- 'Enable
guide output'
- this is normally checked because it tells PHD2 to send guide
commands to the mount. But there are some
circumstances
where you might want to disable this, usually because you want to
observe the uncorrected behavior of the mount. For example,
you
can disable guider output in order to see the general shape and
amplitude of your mount's periodic error or to check the amount of
drift from polar mis-alignment.
- 'Calibration
step-size' - specifes the duration of the guide pulse that
PHD2
will use during calibration. Its use is described in the
'Auto
Calibration' section of the 'Basic Use' help page. You can
adjust
the value depending on whether the guide star is moving too quickly
or too slowly during calibration. As a general guideline, it
is
good to calibrate within about 30 degrees of the celestial equator
(declination = 0), and to use a calibration step size that will result
in 8-14 steps in each direction. The 'calculate...' button
to
the right of this control will launch a dialog that can help you
compute an appropriate value:
To
use the calculator,
be sure the topmost three edit controls are
correctly filled in. If you have already specified the focal
length and
the camera pixel size in the 'Global' and 'Camera' tabs respectively,
those fields will already be populated in this form. If you are
using an ASCOM connection to your mount, the fields for "Guide speed"
and "Calibration declination" will also have the correct values.
Otherwise, you'll need to supply them yourself. The guide
speed is specified as a multiple of sidereal speed - most mounts will
use something
like
1X or 0.5X sidereal, but you can choose something else. You
can
leave the 'calibration steps' field at the default value of 12, which
is likely to result in a good calibration. Use of a significantly
smaller value raises the likelihood that seeing errors or small mount
errors will cause calibration errors . As you change the values
in these fields, PHD2
will recalculate your current image scale and a recommended value for the
calibration step-size. If you then click on 'Ok', that value
will
be inserted into the calibration step-size field of the 'Mount' dialog.
Clicking 'Ok' will also populate the focal length and camera
pixel size fields in the 'Global' and 'Camera' tabs, so any changes you
made in the calculator will be reflected there as well. However,
this will not be done if you click on 'Cancel' in the calculator
dialog.
- 'Max Duration RA' - specifies the maximum allowed
guide pulse duration for right ascension. You might reduce
this
below the default value if you want to avoid "chasing" a large
deflection that could be caused by a spurious event (e.g. a sudden 'hot
pixel') .'
- 'Max Duration Declination' - specifies the
maximum allowed guide pulse duration for declination (same as above but
for declination). This can be useful to avoid over-correction and subsequent direction reversal in declination
guiding.
- 'Reverse Dec
output after meridian flip' - tells PHD2 how to adjust the
calibration data when you use the function to "flip" it. Some
mounts track their 'side of pier' state and automatically reverse the
direction of guide commands in declination. Others, perhaps
most
mounts, do not do this. In either case, PHD2 needs to know if
the
mount will automatically change its behavior based on
side-of-pier. You may have difficulty finding information about
how your mount behaves in this respect, so it's probably easiest to
just run a quick experiment. With the checkbox disabled,
calibrate on one side of the pier, then move the mount to the other
side. Select 'Flip Calibration' under the 'Tools' menu, and
start guiding. If the guiding works normally, leave the box
un-checked; but if you see run-away in declination, check the box and
repeat the experiment.
- 'Assume
Dec orthogonal to RA' - Normally, the calibration process independently
computes the camera angles for both right ascension and declination.
There is no need for great precision on these values, and the
default behavior normally works well. However, if your mount has
very high periodic error or you are dealing with very bad seeing
conditions, you may want to force the RA and Declination angles to be
perpendicular. If you choose that option, PHD2 will compute the
camera angle for RA, then assert a declination angle that is orthogonal
to it.
All of the other controls in this dialog are related to the guiding
algorithms you have chosen and are described here: Guiding Algorithms and Settings
AO Tab
Most
of the edit controls at the top of the AO tab have the same meaning as
their counterparts on the 'Mount' tab. The guiding algorithms
apply to the control of the tip/tilt optical element in the AO device
itself, not to the "bump" commands sent to the mount. Since the
AO device is not trying to move a heavy piece of equipment, you can
afford to be more aggressive in your guide algorithm parameters.
If you use a hysteresis-based algorithm, for example, you should
probably start with a high level of aggressiveness, perhaps 100%.
Or you can choose the 'None' algorithm, which means there will be
no damping or history-based algorithm applied at all. In that
case, each correction will be based only on the most recent guide frame
and will make a 100% correction of the most recent deflection.
You
can use the four parameters at the bottom of the AO tab to control the
calibration process and the manner in which 'bump' operations are done.
The 'calibration step' field tells PHD2 the amount to move the
tip/tilt element in each of the up/down/left/right directions, in
units of AO steps, during calibration. The guide star position is measured at the
beginning and end of each leg of the calibration, and the 'samples to
average' parameter tells PHD2
how many samples to take at each of these
points. Averaging images is important because the seeing will
always cause the guide star to "bounce around" a bit. As
discussed earlier, the AO unit can make corrections
only within a limited range of guide star movement. You
will want to initiate mount 'bump' corrections before these limits
are actually reached, and the 'bump percentage' field is used for that
purpose. To move the mount, the full bump correction is
accomplished in steps - the 'bump step' field controls the size of
these increments. If the bump operation has begun and the guide
star remains outside the "bump percentage" area, PHD2
will increase the
bump size until the guide star is back within that range.
Additional movement from that point to the center position will
continue at the specified "bump step size". This complexity is
required in order to maintain good guiding, with no elongated stars,
even as the mount is being bumped. During the bump operation, the
AO is continuing to make corrections, so the long "mount bump" is
continuously offset by adjustments in the AO.
Rotator Tab
Currently,
the rotator tab has only one control, and this window is likely to
change in the future. The "Reversed" checkbox can be used for
optical systems that reverse the image because they have an odd number
of mirrors. The direction and amount of rotation is used to
adjust the calibration data, so PHD2 follows the ASCOM standard:
"the rotator position is expressed as an angle from 0 up to but
not including 360 degrees, counter-clockwise against the sky."


This example demonstrates how to use the writeReplace/readResolve methods. The readResolve method resolves the object read from the stream before it is returned to the caller. The writeReplace method allows a Serializable class of an object to nominate its own replacement in the stream before the object is written.
This example creates a symbol class for which only one instance of each each symbol binding exists. The Symbol class defines the readResolve method. A symbol is created from outside using the symbollookup method. The symbollookup method finds and returns a symbol if one already and, if one does not already exist, creates one. This assures uniqueness within one VM. Then, when the readResolve method is called as the symbol is being read, a preexisting equivalent Symbol object is substituted from the hashtable to maintain the unique identity constraint, if such a symbol exists. If the symbol does not exist, the new symbol is added to the hashtable and returned. This assures uniqueness when dealing with more than one VM.


Raffler can be used to simulate any raffle or sweepstakes drawing. It's as easy as entering the contestants, setting the amount of tickets purchased, and clicking Pick a Winner!
Our system uses non-uniform distribution in order to pick the winner. What this means to you is, the higher the amount of tickets are for a contestant, the higher their chances are of winning!
P.S.
 Instead, if you have a dollar or two laying around, I'd appreciate the donation to keep this site online!


Turtle commands can be represented in Haskell using an algebraic data type:
type Distance = Float type Angle = Float data Command = Go Distance | Turn Angle | Sit | Command :#: CommandThe last line declares an infix data constructor. We have already seen such constructors in Lab 5, where we used them for the binary connectives of propositional logic. While ordinary constructors must begin with a capital letter, infix constructors must begin with a colon. Here, we have used the infix constructor :#: to join two commands.
 This will open a new graphics window and draw the turtle graphic. For example,
*Main> display (Go 30 :#: Turn 120 :#: Go 30 :#: Turn 120 :#: Go 30)draws the triangle described above.
When you close the graphics window, GHCi will exit as well, so you will need to re-load your code to draw another picture.
p :#: Sit = p Sit :#: p = p p :#: (q :#: r) = (p :#: q) :#: rWe can omit parentheses in expressions with :#: because, wherever they are placed, the meaning remains the same. In this assignment, when we say that two commands are equivalent we mean that they are the same according to the equalities listed above.
However, to evaluate an expression Haskell has to place parentheses; if you ask it to show a command, it will also show where it has placed them:
*Main> Sit :#: Sit :#: Sit Sit :#: (Sit :#: Sit)
split :: Command -> [Command]that converts a command to a list of individual commands containing no :#: or Sit elements. For example,
*Main> split (Go 3 :#: Turn 4 :#: Go 7) [Go 3, Turn 4, Go 7]
join :: [Command] -> Commandthat converts a list of commands into a single command by joining the elements together.
 Give both its type and definition.
copy :: Int -> Command -> Commandwhich given an integer and a command returns a new command consisting of the given number of copies of the given command, joined together. Thus, the following two com- mands should be equivalent:
copy 3 (Go 10 :#: Turn 120) Go 10 :#: Turn 120 :#: Go 10 :#: Turn 120 :#: Go 10 :#: Turn 120
pentagon :: Distance -> Commandthat returns a command which traces a pentagon with sides of a given length.
Sample output is shown in Figure 2.
Go 0 = Sit Go d :#: Go e = Go (d+e) Turn 0 = Sit Turn a :#: Turn b = Turn (a+b)So the Sit command is equivalent to either moving or turning by zero, and any sequence of consecutive moves or turns can be collapsed into a single move or turn (as long as moves have a non-negative distance!).  (If your version of join adds aSit command, you will need to define a new version which does not.)
data Command = ... | GrabPen Pen | Branch Commandwhere Pen is defined as:
data Pen = Colour Float Float Float | InklessThese give two additional forms of path. • GrabPen p, where p is a pen: causes the turtle to switch to a pen of the given colour. The following pens are predefined:
white, black, red, green, blue :: PenYou can create pens with other colours using the Colour constructor, which takes a value between 0 and 1.0 for each of the red, green and blue components of the colour. The special Inkless pen makes no output; you can use Inkless to create disjoint pictures with a single command. • Branch p, where p is a path: draws the given path and then returns the turtle to direction and position which it had at the start of the path (rather than leaving it at the end). Pen changes within a branch have no effect outside the branch.
To see the effect of branching, draw the following path.
let inDirection angle = Branch (Turn angle :#: Go 100) in join (map inDirection [20,40..360])
angle: 90 start: +f rewrite: f → f+f-f-f+fEach symbol in the string generated by an L-System represents a path command: here, + and - represent clockwise and anticlockwise rotation and f represents a forward movement. Which symbols represent which commands is a matter of convention.
In this system, only the symbol f is rewritten, while the + and - symbols are not. The rewriting replaces the straight lines with more complex figures.
Here is how to generate a picture with an L-System. Begin with the start pattern. Then apply the rewrite rule some number of times, replacing the character on the left by the sequence on the right. For instance, applying the above rule three times gives the following strings in successive steps:
Step Pattern 0 +f 1 +f+f-f-f+f 2 +f+f-f-f+f+f+f-f-f+f-f+f-f-f+f-f+f-f-f+f+f+f-f-f+f +f+f-f-f+f+f+f-f-f+f-f+f-f-f+f-f+f-f-f+f+f+f-f-f+f +f+f-f-f+f+f+f-f-f+f-f+f-f-f+f-f+f-f-f+f+f+f-f-f+f 3 -f+f-f-f+f+f+f-f-f+f-f+f-f-f+f-f+f-f-f+f+f+f-f-f+f -f+f-f-f+f+f+f-f-f+f-f+f-f-f+f-f+f-f-f+f+f+f-f-f+f +f+f-f-f+f+f+f-f-f+f-f+f-f-f+f-f+f-f-f+f+f+f-f-f+fNote that you could continue this process for any number of iterations.
After rewriting the string the desired number of times, replace each character that remains by some drawing commands. In this case, replace f with a move forward (say, by 10 units), replace each + by a clockwise turn through the given angle, and replace each - by an anticlockwise turn through the given angle.
Converting L-Systems to functions that return turtle commands is straightforward. For example, the function corresponding to this “triangle” L-System can be written as follows:
triangle :: Int -> Command triangle x = p :#: f x where f 0 = Go 10 f x = f (x-1) :#: p :#: f (x-1) :#: n :#: f (x-1) :#: n :#: f (x-1) :#: p :#: f (x-1) n = Turn 90 p = Turn (-90)Study the above definition and compare it with the L-System definition on the previous page. The above definition is included in LSystem.hs, so you can try it out by typing (for instance):
display (triangle 5)A couple of things are worth noting. The symbols from the system that are rewritten are imple- mented as functions that take a “step number” parameter—in this case, only f is rewritten. When we have taken the desired number of steps, the step number bottoms-out at 0, and here f is just in- terpreted as a drawing command. The symbols that are not rewritten are implemented as variables, such as n and p. In general, there will be one definition in the where clause for each letter in the L-System.
A rewrite rule for the L-System may contain clauses in square brackets, which correspond to branches.  Here we use different pens to draw the segments generated by different symbols: this is not part of the description of the L-system, but it generates prettier pictures.
angle: 60 start: f rewrite: f → g+f+g g → f-g-f
angle: 60 start: f- -f- -f- - rewrite: f → f+f- -f+f
angle: 90 start: l rewrite: l → +rf-lfl-fr+ r → -lf+rfr+fl-Note: Not all of the symbols here need to move the turtle. Check your result against the pictures at http://en.wikipedia.org/wiki/Hilbert_curve and adjust the final values (e.g. r 0 = ...) until it looks like those.


https://www.codegrepper.com/code-examples/javascript/javascript+regex+url
www.google.com
https://app.slack.com/client/T71E5T1A8/C8WKYE7E3
https://app.slack.com/client/T71E5T1A8/C8WKYE7E3
https://app.slack.com/client/T71E5T1A8/C8WKYE7E3
jshdkajshdkhsaadhttp://www.w3schools.com/dkfjskdjf
abc


Create and visualize a module network of regulatory genes |
Added by GenomeSpaceTeam on 2015.05.22
Last updated on over 3 years ago.
Does my gene expression dataset contain a module network of regulatory genes? Does the network have any special features?
This recipe provides one method for creating and visualizing a module network of regulatory genes. An example use of this recipe is a case where an investigator may want to evaulate an expression dataset to find regulatory genes such as transcription factors, and then determine if they are connected in a network.
In particular, the regulatory genes of interest are genes which regulate other genes associated with an embryonic stem cell (ESC) state.  This recipe recapitulates research by Wong et al., in Cell Stem Cell (2008), "Module map of stem cell genes guides creation of epithelial cancer stem cells."
We use a gene expression dataset of primary human breast cancer tumor samples (described in Chin, K. et al, Cancer Cell, 2006), and create a module network by projecting a set of stemness regulators onto the gene expression dataset, using Genomica. A module network is a model which identifies regulatory modules from gene expression data, especially modules of co-regulated genes and their regulators. The module also identifies the conditions under which the regulation can occur.
After obtaining the module network, we visualize it using Cytoscape. Since the network is very large, we then filter it to just a subnetwork of stemness regulators and their connections, again using Cytoscape. This provides us with a visual representation of the stemness regulators as they appear projected onto a breast cancer tumor dataset.
To complete this recipe, we will need a gene expression dataset and a list of genes which we believe to be important transcriptional regulators. In this example, we use a gene expression dataset of primary human breast cancer tumor samples, which is fully described in Chin, K. et al, Cancer Cell (2006). We also use a set of genes which are believed to be regulators of the embryonic stem cell state, called "stemness regulators". This gene set of stemness regulators can be obtained by following the recipe for completing Stepwise Linkage Analysis of Microarray Signatures (SLAMS). We will need the following datasets, which can be downloaded from the following folders:
Public
> RecipeData
> ExpressionData
> breasttumor.preprocessed.collapsed.tab
: this file contains the gene expression profile of primary human breast cancer tumor samples. The original dataset has been log-transformed, row-centered on the mean, and has had the probe IDs collapsed to HUGO Gene Symbols.
Public
> RecipeData
> GeneSets
> stemness_regulators.geneset.tab
: this file contains a list of genes which are believed to regulate genes associated with the embryonic stem cell state.
A subnetwork of genes which regulate the transcription of embryonic stem cell-associated signature genes. This subnetwork shows the connections between regulators of this 'stemness signature'.
First, we will build a network of regulatory modules from co-regulated genes with similar expression profiles, from the breast cancer tumor sample dataset.
Public
> RecipeData
> ExpressionData
> breasttumor.preprocessed.collapsed.tab
) by clicking and dragging the file to the Genomica tool icon.Algorithms > Create a Module Network…
Regulation
, set Maximum tree depth
to 5.Candidate regulator genes
, click GenomeSpace Load…
.Public
> RecipeData
> GeneSets
> stemness_regulators.geneset.tab
), then click Select
.Run
to create a module network.GenomeSpace > Export Network to GenomeSpace…
OK
to close the pop-up and continue with the export (see below).stemness_network.ndb
.Save As
.Next, we will import the network results from GenomeSpace into Cytoscape, and show the gene regulatory network. In this example, we use Cytoscape 3.4.
NOTE: This recipe requires Cytoscape 3.3 or higher to be installed. Please navigate to www.cytoscape.org to download the latest version.
cytoscape.jnlp
file. Double-click this file to launch Cytoscape.Start New Session
label, choose With Empty Network
.OK
.NDB Reader
app. To install this, use the following steps:
Apps > App Manager
.Install
to install it. Once the app is installed it allows you to load NDB files, without modifying any of the Cytoscape menu items.File > Import > Network > GenomeSpace
.stemness_network.ndb
. Choose Select
to load the file. This may take a few moments. Once the file is loaded, it will prompt you to import the network to a network view. Choose a network and click OK
.OK
to create the network. This may take a few minutes.This will create a network of 13,341 nodes and 117,815 edges. This is an incredibly large, dense network. It is best to filter the network before attempting to visualize it using a different Cytoscape layout, such as a force-directed layout or a degree-sorted circular layout. In the next step, we will filter the network to a more manageable size.
In this step, we will filter the existing network of 13,341 nodes to just the nodes that are known to be regulators of the embryonic stemness signature. We will filter by the gene set described in stemness_regulators.geneset.tab
.
File > Import > Table > GenomeSpace
.Public
> RecipeData
> GeneSets
> stemness_regulators.geneset.tab
). Click Select
. This will bring up an import menu, allowing you to choose which names and symbols are imported into Cytoscape.OK
to import the file.Control Panel
section, click the Select
tab.Default Filter
section, click the +
button to add a new filter, and choose Column Filter
.node.INGENESET
as the attribute to filter on.Between
1 and
1 inclusive
.Apply
.Apply
button.New Network From Selection, All Edges
. This will create a new subnetwork with just the stemness regulators and their connections. You should obtain a network of 48 nodes and 334 edges.Layout > Edge Weighted Spring Embedded Layout
Layout > Circular Layout
Layout > Degree Sorted Circle Layout
Scale
function. Select Layout > Scale
, then use the sliding scale bar to increase or decrease network density.Style
tab in the Control Panel
. You can choose preset styles using a drop-down menu. You can create your own styles by clicking on the Defaults
pane, then adjusting the parameters.Edge
tab in the bottom part of the pane. Then click on EDGE_COLOR
, choose a new color, and click OK
. Some variables take numeric inputs, such as EDGE_LINE_WIDTH
.This is an example interpretation of the results from this recipe. First, we created a module network of genes that regulate the stemness signature, by projecting it onto a breast cancer tumor gene expression dataset using Genomica. We then used Cytoscape to visualize the connections between these genes, and to filter a large network down into a subnetwork of just stemness regulators.
We can see from the resulting subnetwork (see below) that, e.g. genes such as POLR2K and MYC are stemness regulators with many connections to other stemness regulators. In contrast, genes such as SQLE or PSCA have fewer connections to other stemness regulators. We can describe POLR2K and MYC as having higher 'degree', than genes such as SQLE or PSCA. These connections in the network imply that perturbing the regulation of POLR2K or MYC would have a larger effect on the embryonic stem cell expression signature (because their multiple connections allow perturbations to propagate in the network) than perturbing other genes that have fewer connections.


I had originally made an ROC curve for this section, but the area under the curve was great enough to render the chart useless. Instead I have made these matrices which compare how changing the confidence threshold affects the true positive rate. The chart on the left assumes every prediction is correct regardless of the confidence, while the chart on the right only accepts predictions made with a probability above 95%. In this way I have turned a multi-class classification problem into a binary classification problem.
In this case, each classification is a representation of the following:
- True Positive: Correctly classified images with confidence above the threshold.
- True Negative: Incorrectly classified images with confidence below the threshold.
- False Positive: Incorrectly classified images with confidence above the threshold.
- False Negative: Correctly classified images with confidence below the threshold.
Due to the starting model being overfit on the training data, and this leading to me using the test data for model evaluation, there is a possibility that we will overfit to our test data. The best solution to this issue (and a great solution to almost any data science problem) would be to get more data. While it was not possible for me to acquire thousands more photos for testing purposes, my sister agreed to take a few pictures for me. She lives in England, and while the signs there are not entirely the same, these photos provide a difficult and interesting final test.


Wellness
SDE4 - a WELL certified building - is focused on increased well-being and productivity of its occupants through the development of healthy and active workplaces.
Lighting
SDE4 supports occupants to remain synchronized with their circadian rhythm by offering abundant access to diffused natural light while minimizing glare.
Air
SDE4 optimizes cross ventilation through its envelope design, giving occupants access to fresh air, and control over their immediate surroundings.
Movement
SDE4's dynamic design & open spaces encourage movement and promote physical activity to its occupants.
SustainABLE NUS
NUS Sustainability Strategic Plan identifies the university’s key environmental impact areas and articulates the goals to be implemented for reaching its targets by 2020.
IFC2CityGML
The project directly supports Virtual Singapore and enables digital transformation in the development of new types of smart cities services and applications.
BIM - SPATIO TEMPORAL PLATFORM
The project integrates visual analytics, temporal sensor data, and spatial BIM information within a single platform for improved building management.
Indoor Localization
The 120 Steerpath blueetooth beacons can be used for personal indoor localization, navigation, and occupancy prediction.


You can gain quick access to the available tool windows: hover your mouse pointer over icon in the lower left corner of the IDE window.
The list of tool windows shows up, leaving you with the task of selecting the desired one:


|
SphinxBase
5prealpha
|
Simple portable thread functions. More...
Go to the source code of this file.
Typedefs | |
| typedef struct sbthread_s | sbthread_t |
| Thread object. | |
| typedef struct sbmsgq_s | sbmsgq_t |
| Asynchronous message queue object. | |
| typedef struct sbmtx_s | sbmtx_t |
| Mutex (critical section) object. | |
| typedef struct sbevent_s | sbevent_t |
| Event object. | |
| typedef int(* | sbthread_main )(sbthread_t *th) |
| Entry point for a thread. | |
Functions | |
| SPHINXBASE_EXPORT sbthread_t * | sbthread_start (cmd_ln_t *config, sbthread_main func, void *arg) |
| Start a new thread. | |
| SPHINXBASE_EXPORT int | sbthread_wait (sbthread_t *th) |
| Wait for a thread to complete. | |
| SPHINXBASE_EXPORT void | sbthread_free (sbthread_t *th) |
| Free a thread object. | |
| SPHINXBASE_EXPORT cmd_ln_t * | sbthread_config (sbthread_t *th) |
| Get configuration object from a thread. | |
| SPHINXBASE_EXPORT void * | sbthread_arg (sbthread_t *th) |
| Get argument pointer from a thread. | |
| SPHINXBASE_EXPORT sbmsgq_t * | sbthread_msgq (sbthread_t *th) |
| Get message queue from a thread. | |
| SPHINXBASE_EXPORT int | sbthread_send (sbthread_t *th, size_t len, void const *data) |
| Send an asynchronous message to a thread. More... | |
| SPHINXBASE_EXPORT sbmsgq_t * | sbmsgq_init (size_t depth) |
| Create a message queue. More... | |
| SPHINXBASE_EXPORT void | sbmsgq_free (sbmsgq_t *q) |
| Free a message queue. | |
| SPHINXBASE_EXPORT int | sbmsgq_send (sbmsgq_t *q, size_t len, void const *data) |
| Post a message to a queue. | |
| SPHINXBASE_EXPORT void * | sbmsgq_wait (sbmsgq_t *q, size_t *out_len, int sec, int nsec) |
| Wait for a message from a queue. | |
| SPHINXBASE_EXPORT sbmtx_t * | sbmtx_init (void) |
| Create a mutex. | |
| SPHINXBASE_EXPORT int | sbmtx_trylock (sbmtx_t *mtx) |
| Try to acquire a mutex. | |
| SPHINXBASE_EXPORT int | sbmtx_lock (sbmtx_t *mtx) |
| Acquire a mutex. | |
| SPHINXBASE_EXPORT int | sbmtx_unlock (sbmtx_t *mtx) |
| Release a mutex. | |
| SPHINXBASE_EXPORT void | sbmtx_free (sbmtx_t *mtx) |
| Dispose of a mutex. | |
| SPHINXBASE_EXPORT sbevent_t * | sbevent_init (void) |
| Initialize an event. | |
| SPHINXBASE_EXPORT void | sbevent_free (sbevent_t *evt) |
| Free an event. | |
| SPHINXBASE_EXPORT int | sbevent_signal (sbevent_t *evt) |
| Signal an event. | |
| SPHINXBASE_EXPORT int | sbevent_wait (sbevent_t *evt, int sec, int nsec) |
| Wait for an event to be signalled. | |
Simple portable thread functions.
Definition in file sbthread.h.
| SPHINXBASE_EXPORT sbmsgq_t* sbmsgq_init | ( | size_t | depth | ) |
Create a message queue.
| depth | Depth of the queue. |
Definition at line 444 of file sbthread.c.
References ckd_calloc, and ckd_free().
Referenced by sbthread_start().
| SPHINXBASE_EXPORT int sbthread_send | ( | sbthread_t * | th, |
| size_t | len, | ||
| void const * | data | ||
| ) |
Send an asynchronous message to a thread.
Each thread gets a message queue by default, so this is just a wrapper around sbmsgq_send().
Definition at line 728 of file sbthread.c.
References sbmsgq_send().


Stop Settling for Mono Playback
As dual speakers become the norm in smartphone design, let us briefly examine and explain why one should always expect to hear their music in stereo.
Though I have many audiophilical sentiments and preferences, I cannot — by conscience — fully claim the title because I’ve never been able to justify the allotment of funds necessary for the obligatory equipment. (And my digital compressor usage in the production of Drycast and Futureland has been manifestly vulgar.) That said, audio engineering is one of the few topics which I can actually speak on with almost-academic authority, and my pretentiousness-capacitated preoccupation with quality-of-life compels me to bring up a ludicrously-rational standard that most of us have continued to undershoot for far too long.
Two weeks ago, the abundantly-rumored omission of the 3.5mm audio port in the iPhone 7 was finally settled. We played our own part in feeding the “controversy,” yeah, but I believe Apple was actually quite tardy in labeling smartphone-bound analog audio as archaic, though I’m not going to waste words in that discussion — it is definitely oversaturated, at this point — because I think mono audio is an even more prevalent topic.
Honestly, out of all the missing features we’ve lamented over in the past decade, stereo speakers should’ve been the most aggravating. The gigantic difference, of course, is that the industry (and — by muddled extension — the consumers) has been all but silent in that regard. I write you, now, because we should all be colossally disappointed with ourselves.
Two channels. Left and right. Read: Mono vs. Stereo But why should you care? What if Google — somehow — failed to provide you with a significant difference?
One channel of sound is — in terms of locale — rigidly static in your perception. Doubling the data creates a spectrum, adding dimensionality, which is infinite, ya know. Playing back audio in stereo, through two or more diaphragms (the fundamental hardware unit of sound reproduction,) now enables the exhibition of audio pictures.
If this is entirely new to you, I want you to do something. Find yourself a pair of headphones or a set of two or more computer speakers. (If these aren’t available, consider your car’s sound system. If it’s at all current and healthy, it’s gonna do the trick.) Bother to discover “L” and “R.” That is, left and right. Orient accordingly. Download this 37-second clip I recorded at BikeFest with my Zoom H2n. (Be advised: the preview is formatted in 5.1 surround, so it’s a very large file for its playback length.) Listen, obviously, and then listen again on your singular smartphone “loudspeaker.”
That’s what I’m talking about.
Why am I being so abashedly patronizing? Why am I transgressing against our particular assumptions about you — the informed, savvy millennial audience? Because the vast majority of playback I hear in day-to-day life is still from a singular diaphragm; a singular source.
A topical example: I am shown a YouTube video on an iPhone. (A pre-iPhone 7 device, that is.)
Walking downtown, I pass a small band of adolescent skateboarders listening to Cannibal Ox on a Samsung Galaxy Note.
Worst of all: I find myself watching a Netflix film on my iPhone, in bed, not having bothered to wear the $200 pair of QC15s sitting within arm’s length.
Informed or not, consumers are neglecting audio, and dimensionality, alone is worth a change. Recent years have allowed the unlimited bandwidth assumption to become habit, so even the vast majority of today’s spoken word programs (like podcasts) — which, in general, stay in the “center” of their mix, making little to no use of the left-right spectrum — are produced in stereo, now. In many cases (including a few of ours,) this doublesizing is often for the sake of introductory themes, alone. If you care to imagine a more data-frugal society, the “waste” is ridiculous. It is not unrealistic to expect such a reality in the near future, but the same holds true for the reverse.
In the present’s abundance, though, the result is simply a decrease — as a whole — in playback’s “full experience,” if the hardware is not changed. Imagine how great it’d be if a digital audio formatting standard could be developed that’d enable a singular file to be mono or stereo, if needed/utilized, to trim off redundancy, sorta like variable bit rate. Get on that, would ya?
So, why haven’t we become more diligent about our sound experiences? 1 billion iPhones in circulation, all with mono playback by default, are suspect culprits, I think. Of course, there are other devices, but none as influential — even the iPod, funny enough — on fundamental digital functions like music playback. And honestly, when is it appropriate or suave to take the extra steps?
I want to show you this song. Let me untangle my headphones… Yes, okay. Put them in. I’m going to sit here in silence for four minutes, looking into your eyes as you listen to the entirety of this track.
It’s never going to be socially acceptable. But what about wireless alternatives? Apple’s new Air Pods look absurd, but their by-computing optimization of the Bluetooth audio standard is revolutionary, in a small way, in propelling the “hearables” paradigm into the mainstream, if only for a moment. If — in a strangely-audiocentric future — we are always wearing multipurpose sound reproduction devices in our ears, perhaps the waste of the Mono Monstrosity will be finally resolved. Until then, I suppose all we can do is give it an extra thought, for our own quality-of-life’s sake.


{%extends "layout.html"%} {% block content %}
The type of file chosen is not supported. Please select a file ending with .mid or .xml.


THE FOX AND THE HOUND A film review by Steve Rhodes Copyright 1996 Steve Rhodes
RATING (0 TO ****): **
THE FOX AND THE HOUND (1981) is a Disney animated movie now available on video. It tells the story of a fox named Tod (Keith Mitchell as the younger Tod and Mickey Rooney as the older Tod) and his friend, a hunting dog named Copper (Corey Feldman as the younger Copper and Kurt Russell as the older Copper). The story has two distinct parts. In the first, these two natural enemies are constant playmates.
The movie starts like the older Disney movies do. There is a long title sequence with slow music and no action, but the ending has no credits at all, just the words, "The End." My son was eager for me to hit fast forward during the opening credits. How movies have changed. Today, we have action and rock music during the opening credits, and the ending credits go on forever thanking everyone right down to the caterer and the star's masseuse.
Once the directors (Ted Berman, Richard Rich, and Art Stevens) yell action, we have Tod and his mother being chased by hounds and hunters.  This is handled so low key that youngsters will probably not even realize it happened. Tod is then taken in by an Owl named Big Mama (Pearl Bailey). Big Mama in turn finds a home for Tod with Widow Tweed (Jeanette Nolan).
Eventually Tod meets Copper, and being young and innocent, they do not believe that they will ever become enemies. They spend the first half of the movie frolicking in the forest. This part, which is actually over half of the elapsed time, is the main problem with the film. The zillions of writers (Ted Berman, Larry Clemmons, Vance Gerry, Steve Hulett, Earl Kress, Daniel P. Mannix, Burny Mattinson, David Michener, and Peter Young) on the film are able to craft characters, each of which are cute and as sweet as can be. The producers (Ron Miller, Wolfgang Reitherman, and Art Stevens) get top notch actors and actresses to do the voices. They forget only one item; they forget to give the characters anything to do. There is no tension in the long first part and almost nothing happens. The writers invent wonderful characters and then run out of ideas. What a waste.
 Tod can not accept the changes and tells Big Mama, "He won't change and we'll keep on being friends forever." Big Mama responds, "Forever is a long long time and time has a way of changing things." The ending to the show is completely predictable, but this is a kid's show afterall.
The drawing (Daniela Bielecka, David Block, and Phil Nibbelink) is beautifully realistic with bright and lively colors. Granted, this is not art on par with THE LION KING, but it is impressive in its own right. The music (Buddy Baker) is pleasant, but the songs are poor and totally forgettable.
THE FOX AND THE HOUND runs 1:23 which is too long given the dearth of ideas in the script. It is rated G, but there are some scenes that have the potential to scare kids under say 5. These include a enormous and threatening grizzly bear, snarling animals fighting, and animals constantly being shot at with a shotgun. My son Jeffrey (almost 7) saw the movie for the first time with me and he liked it. There is not enough here for me to recommend the film, but I did like all of the characters so I am giving the film **.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEW WRITTEN ON: March 31, 1996
Opinions expressed are mine and not meant to reflect my employer's.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


TABLE OF INTERACTIONS WITH
ANTIHYPERTENSIVES EXCEPT ALPHA BLOCKERS
From the French ANSM drug interactions document of September 2016, p. 25
ANTIHYPERTENSIVES EXCEPT ALPHA BLOCKERS CLASS CODE: C0-001 |
ALPHA BLOCKERS FOR UROLOGICAL USE CLASS CODE: G04CA-001 |
Increase of the hypotensive effect. Greater risk of orthostatic hypotension |
Take into account |
ANTIHYPERTENSIVES EXCEPT ALPHA BLOCKERS CLASS CODE: C0-001 |
ANTIHYPERTENSIVE ALPHA BLOCKERS CLASS CODE: C02CA0-001 |
Increase of the hypotensive effect. Greater risk of orthostatic hypotension |
Take into account |


Transformation Wizard Avant-Garde:
As I just told you, you need 10 million Adena.
I don't see that much money on you...


stonktastic.optimization package¶
Submodules¶
stonktastic.optimization.optimizePolyReg module¶
-
class
stonktastic.optimization.optimizePolyReg.
polyRegOptResultClass
(optSubSet=[''], subDf=Empty DataFrame Columns: [] Index: [], optPolyValue=1, polyDf=Empty DataFrame Columns: [] Index: [])¶ Bases:
object
Class for holding processed values for Jupyter notebook analysis
- Values:
optSubSet (list): List of best indicators to use for Polynomial Regression in terms of accuracy/resource cost subDf (dataframe): Dataframe with all subset/time/score values for graphing optPolyValue (int): The optimum polynomial level to use in terms of accuracy/time polyDf (dataframe): Dataframe with all polynomial levels as well as the time it took to process and the accuracy of those predictions
-
stonktastic.optimization.optimizePolyReg.
polyRegPolynomialOpt
(stonk)¶ Generates a Polynomial Regression model and test the model with varying polynomial degress ranks them based on accuracy/time.
- Parameters
stonk (str) – stock ticker that will be used for optimization
- Returns
int: Optimum Polynomial Degree in terms of time/accuracy using the stock ticker provided dataframe: complete dataframe for stock ticker with all polynomial degree levels, times and scores
-
stonktastic.optimization.optimizePolyReg.
polyRegVariableOpt
(stonk)¶ Generates a Polynomial Regression model and test the model with varying indicators of values and ranks them based on accuracy/time.
- Parameters
stonk (str) – stock ticker that will be used for optimization
- Returns
list: Top list of indicators in terms of time/accuracy for Polynomial Regression using the stock ticker provided dataframe: complete dataframe for stock ticker with all subsets, times and scores
-
stonktastic.optimization.optimizePolyReg.
runPolyRegOptimization
(stonk)¶ Full optimization test for Polynomial Regression looking at both the indicator subsets and polynomial degrees
The class has defaults loaded in so you do not have to run both optimizers at once.
- Parameters
stonk (str) – the stock ticker that will be used for optimization
- Returns
polyRegOptResultClass (class): Class storing the top subset and polynomial degrees as well as full dataframes with the complete results from both optimization test.
stonktastic.optimization.optimizeRanFor module¶
-
stonktastic.optimization.optimizeRanFor.
ranForEstimatorOpt
(stonk)¶ Generates a random forest model and test the model with varying amounts of decision trees (estimators) of values and ranks them based on accuracy/time.
- Parameters
stonk (str) – stock ticker that will be used for optimization
- Returns
int: Optimum number of estimators in terms of time/accuracy for Random Forest using the stock ticker provided dataframe: complete dataframe for stock ticker with all estimators, times and scores
-
class
stonktastic.optimization.optimizeRanFor.
ranForOptResultClass
(optSubSet=[''], subDf=Empty DataFrame Columns: [] Index: [], optEstimators=100, estimatorsDf=Empty DataFrame Columns: [] Index: [])¶ Bases:
object
Class for holding processed values for Jupyter notebook analysis
- Values:
optSubSet (list): List of best indicators to use for Random Forest in terms of accuracy/resource cost subDf (dataframe): Dataframe with all subset/time/score values for graphing optEstimators (int): Most optimum number of trees to use in random forest calculations estimatorsDf (dataframe): Dataframe with all estimator amounts as well as the time it took to process and the accuracy of those predictions
-
stonktastic.optimization.optimizeRanFor.
ranForVariableOpt
(stonk)¶ Generates a random forest model and test the model with varying indicators of values and ranks them based on accuracy/time.
- Parameters
stonk (str) – stock ticker that will be used for optimization
- Returns
list: Top list of indicators in terms of time/accuracy for Random Forest using the stock ticker provided dataframe: complete dataframe for stock ticker with all subsets, times and scores
-
stonktastic.optimization.optimizeRanFor.
runRanForOptimization
(stonk)¶ Full optimization test for Random Forest looking at both the indicator subsets and estimators
The class has defaults loaded in so you do not have to run both optimizers at once.
- Parameters
stonk (str) – the stock ticker that will be used for optimization
- Returns
ranForOptResultClass (class): Class storing the top subset and estimators as well as full dataframes with the complete results from both optimization test.
Module contents¶
The optimization part of the project that test a single stock for the optimum values to use in terms of cost/accuracy.
This intergrates heavily into the Jupyter notebook reports
optimizePolyReg : Code for optimizing Polynomial Regression
optimizeRanFor : Code for optimizing Random Forest


com.serio.felis.assembly
类
AddressParticipleFactory
CouponCodeParticipleFactory
CrmEmailSubscribeParticipleFactory
CustomerParticipleFactory
DefaultParticipleFactory
OrderAddressParticipleFactory
OrderLineParticipleFactory
OrderParticipleFactory
OrderRequestParticipleFactory
ParticipleFactory
PayInfoLogParticipleFactory
PayInfoParticipleFactory
ReplyParticipleFactory
ReviewParticipleFactory