| Top |
| GtkActionGroup * | gtk_action_group_new () |
| const gchar * | gtk_action_group_get_name () |
| gboolean | gtk_action_group_get_sensitive () |
| void | gtk_action_group_set_sensitive () |
| gboolean | gtk_action_group_get_visible () |
| void | gtk_action_group_set_visible () |
| GtkAccelGroup * | gtk_action_group_get_accel_group () |
| void | gtk_action_group_set_accel_group () |
| GtkAction * | gtk_action_group_get_action () |
| GList * | gtk_action_group_list_actions () |
| void | gtk_action_group_add_action () |
| void | gtk_action_group_add_action_with_accel () |
| void | gtk_action_group_remove_action () |
| void | gtk_action_group_add_actions () |
| void | gtk_action_group_add_actions_full () |
| void | gtk_action_group_add_toggle_actions () |
| void | gtk_action_group_add_toggle_actions_full () |
| void | gtk_action_group_add_radio_actions () |
| void | gtk_action_group_add_radio_actions_full () |
| gchar * | (*GtkTranslateFunc) () |
| void | gtk_action_group_set_translate_func () |
| void | gtk_action_group_set_translation_domain () |
| const gchar * | gtk_action_group_translate_string () |
| GtkAccelGroup * | accel-group | Read / Write |
| gchar * | name | Read / Write / Construct Only |
| gboolean | sensitive | Read / Write |
| gboolean | visible | Read / Write |
| struct | GtkActionGroup |
| struct | GtkActionGroupClass |
| struct | GtkActionEntry |
| struct | GtkToggleActionEntry |
| struct | GtkRadioActionEntry |
Actions are organised into groups. An action group is essentially a map from names to GtkAction objects.
All actions that would make sense to use in a particular context should be in a single group. Multiple action groups may be used for a particular user interface. In fact, it is expected that most nontrivial applications will make use of multiple groups. For example, in an application that can edit multiple documents, one group holding global actions (e.g. quit, about, new), and one group per document holding actions that act on that document (eg. save, cut/copy/paste, etc). Each window’s menus would be constructed from a combination of two action groups.
Accelerators are handled by the GTK+ accelerator map. All actions are
assigned an accelerator path (which normally has the form
<Actions>/group-name/action-name
) and a shortcut is associated with
this accelerator path. All menuitems and toolitems take on this accelerator
path. The GTK+ accelerator map code makes sure that the correct shortcut
is displayed next to the menu item.
The GtkActionGroup implementation of the GtkBuildable interface accepts GtkAction objects as <child> elements in UI definitions.
Note that it is probably more common to define actions and action groups in the code, since they are directly related to what the code can do.
The GtkActionGroup implementation of the GtkBuildable interface supports a custom <accelerator> element, which has attributes named “key“ and “modifiers“ and allows to specify accelerators. This is similar to the <accelerator> element of GtkWidget, the main difference is that it doesn’t allow you to specify a signal.
1 2 3 4 5 6 7 8 9 10 |
<object class="GtkActionGroup" id="actiongroup"> <child> <object class="GtkAction" id="About"> <property name="name">About</property> <property name="stock_id">gtk-about</property> <signal handler="about_activate" name="activate"/> </object> <accelerator key="F1" modifiers="GDK_CONTROL_MASK | GDK_SHIFT_MASK"/> </child> </object> |
GtkActionGroup *
gtk_action_group_new (const gchar *name
);
gtk_action_group_new
has been deprecated since version 3.10 and should not be used in newly-written code.
Creates a new GtkActionGroup object. The name of the action group is used when associating keybindings with the actions.
Since: 2.4
const gchar *
gtk_action_group_get_name (GtkActionGroup *action_group
);
gtk_action_group_get_name
has been deprecated since version 3.10 and should not be used in newly-written code.
Gets the name of the action group.
Since: 2.4
gboolean
gtk_action_group_get_sensitive (GtkActionGroup *action_group
);
gtk_action_group_get_sensitive
has been deprecated since version 3.10 and should not be used in newly-written code.
Returns TRUE
if the group is sensitive. The constituent actions
can only be logically sensitive (see gtk_action_is_sensitive()
) if
they are sensitive (see gtk_action_get_sensitive()
) and their group
is sensitive.
Since: 2.4
void gtk_action_group_set_sensitive (GtkActionGroup *action_group
,gboolean sensitive
);
gtk_action_group_set_sensitive
has been deprecated since version 3.10 and should not be used in newly-written code.
Changes the sensitivity of action_group
Since: 2.4
gboolean
gtk_action_group_get_visible (GtkActionGroup *action_group
);
gtk_action_group_get_visible
has been deprecated since version 3.10 and should not be used in newly-written code.
Returns TRUE
if the group is visible. The constituent actions
can only be logically visible (see gtk_action_is_visible()
) if
they are visible (see gtk_action_get_visible()
) and their group
is visible.
Since: 2.4
void gtk_action_group_set_visible (GtkActionGroup *action_group
,gboolean visible
);
gtk_action_group_set_visible
has been deprecated since version 3.10 and should not be used in newly-written code.
Changes the visible of action_group
.
Since: 2.4
GtkAccelGroup *
gtk_action_group_get_accel_group (GtkActionGroup *action_group
);
gtk_action_group_get_accel_group
has been deprecated since version 3.10 and should not be used in newly-written code.
Gets the accelerator group.
the accelerator group associated with this action
group or NULL
if there is none.
[transfer none]
Since: 3.6
void gtk_action_group_set_accel_group (GtkActionGroup *action_group
,GtkAccelGroup *accel_group
);
gtk_action_group_set_accel_group
has been deprecated since version 3.10 and should not be used in newly-written code.
Sets the accelerator group to be used by every action in this group.
Since: 3.6
GtkAction * gtk_action_group_get_action (GtkActionGroup *action_group
,const gchar *action_name
);
gtk_action_group_get_action
has been deprecated since version 3.10 and should not be used in newly-written code.
Looks up an action in the action group by name.
Since: 2.4
GList *
gtk_action_group_list_actions (GtkActionGroup *action_group
);
gtk_action_group_list_actions
has been deprecated since version 3.10 and should not be used in newly-written code.
Lists the actions in the action group.
an allocated list of the action objects in the action group.
[element-type GtkAction][transfer container]
Since: 2.4
void gtk_action_group_add_action (GtkActionGroup *action_group
,GtkAction *action
);
gtk_action_group_add_action
has been deprecated since version 3.10 and should not be used in newly-written code.
Adds an action object to the action group. Note that this function
does not set up the accel path of the action, which can lead to problems
if a user tries to modify the accelerator of a menuitem associated with
the action. Therefore you must either set the accel path yourself with
gtk_action_set_accel_path()
, or use
gtk_action_group_add_action_with_accel (..., NULL)
.
Since: 2.4
void gtk_action_group_add_action_with_accel (GtkActionGroup *action_group
,GtkAction *action
,const gchar *accelerator
);
gtk_action_group_add_action_with_accel
has been deprecated since version 3.10 and should not be used in newly-written code.
Adds an action object to the action group and sets up the accelerator.
If accelerator
is NULL
, attempts to use the accelerator associated
with the stock_id of the action.
Accel paths are set to <Actions>/group-name/action-name
.
action_group |
the action group |
|
action |
the action to add |
|
accelerator |
the accelerator for the action, in
the format understood by |
[allow-none] |
Since: 2.4
void gtk_action_group_remove_action (GtkActionGroup *action_group
,GtkAction *action
);
gtk_action_group_remove_action
has been deprecated since version 3.10 and should not be used in newly-written code.
Removes an action object from the action group.
Since: 2.4
void gtk_action_group_add_actions (GtkActionGroup *action_group
,const GtkActionEntry *entries
,guint n_entries
,gpointer user_data
);
gtk_action_group_add_actions
has been deprecated since version 3.10 and should not be used in newly-written code.
This is a convenience function to create a number of actions and add them to the action group.
The “activate” signals of the actions are connected to the callbacks
and their accel paths are set to <Actions>/group-name/action-name
.
[skip]
action_group |
the action group |
|
entries |
an array of action descriptions. |
[array length=n_entries] |
n_entries |
the number of entries |
|
user_data |
data to pass to the action callbacks |
Since: 2.4
void gtk_action_group_add_actions_full (GtkActionGroup *action_group
,const GtkActionEntry *entries
,guint n_entries
,gpointer user_data
,GDestroyNotify destroy
);
gtk_action_group_add_actions_full
has been deprecated since version 3.10 and should not be used in newly-written code.
This variant of gtk_action_group_add_actions()
adds a GDestroyNotify
callback for user_data
.
[skip]
action_group |
the action group |
|
entries |
an array of action descriptions. |
[array length=n_entries] |
n_entries |
the number of entries |
|
user_data |
data to pass to the action callbacks |
|
destroy |
destroy notification callback for |
[nullable] |
Since: 2.4
void gtk_action_group_add_toggle_actions (GtkActionGroup *action_group
,const GtkToggleActionEntry *entries
,guint n_entries
,gpointer user_data
);
gtk_action_group_add_toggle_actions
has been deprecated since version 3.10 and should not be used in newly-written code.
This is a convenience function to create a number of toggle actions and add them to the action group.
The “activate” signals of the actions are connected to the callbacks
and their accel paths are set to <Actions>/group-name/action-name
.
[skip]
action_group |
the action group |
|
entries |
an array of toggle action descriptions. |
[array length=n_entries] |
n_entries |
the number of entries |
|
user_data |
data to pass to the action callbacks |
Since: 2.4
void gtk_action_group_add_toggle_actions_full (GtkActionGroup *action_group
,const GtkToggleActionEntry *entries
,guint n_entries
,gpointer user_data
,GDestroyNotify destroy
);
gtk_action_group_add_toggle_actions_full
has been deprecated since version 3.10 and should not be used in newly-written code.
This variant of gtk_action_group_add_toggle_actions()
adds a
GDestroyNotify callback for user_data
.
[skip]
action_group |
the action group |
|
entries |
an array of toggle action descriptions. |
[array length=n_entries] |
n_entries |
the number of entries |
|
user_data |
data to pass to the action callbacks |
|
destroy |
destroy notification callback for |
[nullable] |
Since: 2.4
void gtk_action_group_add_radio_actions (GtkActionGroup *action_group
,const GtkRadioActionEntry *entries
,guint n_entries
,gint value
,GCallback on_change
,gpointer user_data
);
gtk_action_group_add_radio_actions
has been deprecated since version 3.10 and should not be used in newly-written code.
This is a convenience routine to create a group of radio actions and add them to the action group.
The “changed” signal of the first radio action is connected to the
on_change
callback and the accel paths of the actions are set to
<Actions>/group-name/action-name
.
[skip]
action_group |
the action group |
|
entries |
an array of radio action descriptions. |
[array length=n_entries] |
n_entries |
the number of entries |
|
value |
the value of the action to activate initially, or -1 if no action should be activated |
|
on_change |
the callback to connect to the changed signal |
|
user_data |
data to pass to the action callbacks |
Since: 2.4
void gtk_action_group_add_radio_actions_full (GtkActionGroup *action_group
,const GtkRadioActionEntry *entries
,guint n_entries
,gint value
,GCallback on_change
,gpointer user_data
,GDestroyNotify destroy
);
gtk_action_group_add_radio_actions_full
has been deprecated since version 3.10 and should not be used in newly-written code.
This variant of gtk_action_group_add_radio_actions()
adds a
GDestroyNotify callback for user_data
.
[skip]
action_group |
the action group |
|
entries |
an array of radio action descriptions. |
[array length=n_entries] |
n_entries |
the number of entries |
|
value |
the value of the action to activate initially, or -1 if no action should be activated |
|
on_change |
the callback to connect to the changed signal |
|
user_data |
data to pass to the action callbacks |
|
destroy |
destroy notification callback for |
Since: 2.4
gchar * (*GtkTranslateFunc) (const gchar *path
,gpointer func_data
);
GtkTranslateFunc
has been deprecated since version 3.10 and should not be used in newly-written code.
The function used to translate messages in e.g. GtkIconFactory and GtkActionGroup.
path |
The id of the message. In GtkActionGroup this will be a label or tooltip from a GtkActionEntry. |
|
func_data |
user data passed in when registering the function. |
[closure] |
void gtk_action_group_set_translate_func (GtkActionGroup *action_group
,GtkTranslateFunc func
,gpointer data
,GDestroyNotify notify
);
gtk_action_group_set_translate_func
has been deprecated since version 3.10 and should not be used in newly-written code.
Sets a function to be used for translating the label
and tooltip
of
GtkActionEntrys added by gtk_action_group_add_actions()
.
If you’re using gettext()
, it is enough to set the translation domain
with gtk_action_group_set_translation_domain()
.
action_group |
||
func |
||
data |
data to be passed to |
|
notify |
a GDestroyNotify function to be called when |
Since: 2.4
void gtk_action_group_set_translation_domain (GtkActionGroup *action_group
,const gchar *domain
);
gtk_action_group_set_translation_domain
has been deprecated since version 3.10 and should not be used in newly-written code.
Sets the translation domain and uses g_dgettext()
for translating the
label
and tooltip
of GtkActionEntrys added by
gtk_action_group_add_actions()
.
If you’re not using gettext()
for localization, see
gtk_action_group_set_translate_func()
.
action_group |
||
domain |
the translation domain to use for |
[allow-none] |
Since: 2.4
const gchar * gtk_action_group_translate_string (GtkActionGroup *action_group
,const gchar *string
);
gtk_action_group_translate_string
has been deprecated since version 3.10 and should not be used in newly-written code.
Translates a string using the function set with
gtk_action_group_set_translate_func()
. This
is mainly intended for language bindings.
Since: 2.6
struct GtkActionGroupClass { GObjectClass parent_class; GtkAction *(* get_action) (GtkActionGroup *action_group, const gchar *action_name); };
struct GtkActionEntry { const gchar *name; const gchar *stock_id; const gchar *label; const gchar *accelerator; const gchar *tooltip; GCallback callback; };
GtkActionEntry
has been deprecated since version 3.10 and should not be used in newly-written code.
GtkActionEntry structs are used with gtk_action_group_add_actions()
to
construct actions.
const gchar * |
The name of the action. |
|
const gchar * |
The stock id for the action, or the name of an icon from the icon theme. |
|
const gchar * |
The label for the action. This field should typically be marked
for translation, see |
|
const gchar * |
The accelerator for the action, in the format understood by
|
|
const gchar * |
The tooltip for the action. This field should typically be
marked for translation, see |
|
GCallback |
The function to call when the action is activated. |
struct GtkToggleActionEntry { const gchar *name; const gchar *stock_id; const gchar *label; const gchar *accelerator; const gchar *tooltip; GCallback callback; gboolean is_active; };
GtkToggleActionEntry
has been deprecated since version 3.10 and should not be used in newly-written code.
GtkToggleActionEntry structs are used with
gtk_action_group_add_toggle_actions()
to construct toggle actions.
const gchar * |
The name of the action. |
|
const gchar * |
The stock id for the action, or the name of an icon from the icon theme. |
|
const gchar * |
The label for the action. This field should typically be marked
for translation, see |
|
const gchar * |
The accelerator for the action, in the format understood by
|
|
const gchar * |
The tooltip for the action. This field should typically be
marked for translation, see |
|
GCallback |
The function to call when the action is activated. |
|
gboolean |
The initial state of the toggle action. |
struct GtkRadioActionEntry { const gchar *name; const gchar *stock_id; const gchar *label; const gchar *accelerator; const gchar *tooltip; gint value; };
GtkRadioActionEntry
has been deprecated since version 3.10 and should not be used in newly-written code.
GtkRadioActionEntry structs are used with
gtk_action_group_add_radio_actions()
to construct groups of radio actions.
const gchar * |
The name of the action. |
|
const gchar * |
The stock id for the action, or the name of an icon from the icon theme. |
|
const gchar * |
The label for the action. This field should typically be marked
for translation, see |
|
const gchar * |
The accelerator for the action, in the format understood by
|
|
const gchar * |
The tooltip for the action. This field should typically be
marked for translation, see |
|
gint |
The value to set on the radio action. See
|
“accel-group”
property“accel-group” GtkAccelGroup *
The accelerator group the actions of this group should use.
GtkActionGroup:accel-group
has been deprecated since version 3.10 and should not be used in newly-written code.
Flags: Read / Write
“name”
property“name” gchar *
A name for the action.
GtkActionGroup:name
has been deprecated since version 3.10 and should not be used in newly-written code.
Flags: Read / Write / Construct Only
Default value: NULL
“sensitive”
property“sensitive” gboolean
Whether the action group is enabled.
GtkActionGroup:sensitive
has been deprecated since version 3.10 and should not be used in newly-written code.
Flags: Read / Write
Default value: TRUE
“visible”
property“visible” gboolean
Whether the action group is visible.
GtkActionGroup:visible
has been deprecated since version 3.10 and should not be used in newly-written code.
Flags: Read / Write
Default value: TRUE
“connect-proxy”
signalvoid user_function (GtkActionGroup *action_group, GtkAction *action, GtkWidget *proxy, gpointer user_data)
The ::connect-proxy signal is emitted after connecting a proxy to an action in the group. Note that the proxy may have been connected to a different action before.
This is intended for simple customizations for which a custom action class would be too clumsy, e.g. showing tooltips for menuitems in the statusbar.
GtkUIManager proxies the signal and provides global notification just before any action is connected to a proxy, which is probably more convenient to use.
GtkActionGroup::connect-proxy
has been deprecated since version 3.10 and should not be used in newly-written code.
action_group |
the group |
|
action |
the action |
|
proxy |
the proxy |
|
user_data |
user data set when the signal handler was connected. |
Since: 2.4
“disconnect-proxy”
signalvoid user_function (GtkActionGroup *action_group, GtkAction *action, GtkWidget *proxy, gpointer user_data)
The ::disconnect-proxy signal is emitted after disconnecting a proxy from an action in the group.
GtkUIManager proxies the signal and provides global notification just before any action is connected to a proxy, which is probably more convenient to use.
GtkActionGroup::disconnect-proxy
has been deprecated since version 3.10 and should not be used in newly-written code.
action_group |
the group |
|
action |
the action |
|
proxy |
the proxy |
|
user_data |
user data set when the signal handler was connected. |
Since: 2.4
“post-activate”
signalvoid user_function (GtkActionGroup *action_group, GtkAction *action, gpointer user_data)
The ::post-activate signal is emitted just after the action
in the
action_group
is activated
This is intended for GtkUIManager to proxy the signal and provide global notification just after any action is activated.
GtkActionGroup::post-activate
has been deprecated since version 3.10 and should not be used in newly-written code.
action_group |
the group |
|
action |
the action |
|
user_data |
user data set when the signal handler was connected. |
Since: 2.4
“pre-activate”
signalvoid user_function (GtkActionGroup *action_group, GtkAction *action, gpointer user_data)
The ::pre-activate signal is emitted just before the action
in the
action_group
is activated
This is intended for GtkUIManager to proxy the signal and provide global notification just before any action is activated.
GtkActionGroup::pre-activate
has been deprecated since version 3.10 and should not be used in newly-written code.
action_group |
the group |
|
action |
the action |
|
user_data |
user data set when the signal handler was connected. |
Since: 2.4


Warning!
Morris.js charts are no longer maintained by its author. We would recommend using any of the other charts that come with the template.
Morris.js charts are no longer maintained by its author. We would recommend using any of the other charts that come with the template.


Here's a photo of me:
I'm a Computer Science junior at National University of Singapore
I'm part of the NUS Hackers coreteam, the most active tech community in Singapore.
I previously interned at Grab (Singapore, 2015), Garena (Singapore, 2016), Asana (San Francisco, 2017) and Jump Trading (Chicago, 2017).
Here's my public calendar.
You can reach me at my email.


1D Shooter
Start Game
Score: 0
High Score: 0
Rules
Score increases with time
Walker kills give 1000 score
Bullets cost 500 score
Move Left
A
Move Right
D
Shoot Left
J
Shoot Right
L


text
Use this document as a way to quickly start any new project. All you get is this text and a mostly barebones HTML document.


The iPhone 7 and iPhone 7 Plus are smartphones designed, developed, and marketed by Apple Inc. The iPhone 7's overall design is similar to the iPhone 6S, but introduces new color options, water and dust resistance, a new capacitive, static home key, and the removal of the 3.5 mm headphone jack. The device's internal hardware also received upgrades, including a heterogeneous quad-core system-on-chip with improved system and graphics performance, and upgraded 12 megapixel rear-facing cameras with optical image stabilization on all models and an additional telephoto lens on the iPhone 7 Plus model to provide enhanced zoom capabilities.
Samsung Galaxy S7 and Samsung Galaxy S7 Edge are Android smartphones manufactured and marketed by Samsung Electronics.The Galaxy S7's hardware design is largely identical to that of the S6; it retains its metal and glass chassis, but with refinements such as a squarer home button, and a thinner protrusion for the camera. Both models are available in black and gold colors; white, pink, and silver versions are available depending on market.The Galaxy S7 ships with Android Marshmallow and Samsung's proprietary TouchWiz software suite.The new TouchWiz also allows the user to disable the app drawer.
Google Pixel XL smartphone was launched in October 2016. The phone comes with a 5.50-inch touchscreen display with a resolution of 1440 pixels by 2560 pixels at a PPI of 534 pixels per inch.The Google Pixel XL is powered by 1.6GHz quad-core Qualcomm Snapdragon 821 processor and it comes with 4GB of RAM. The phone packs 32GB of internal storage cannot be expanded. As far as the cameras are concerned, the Google Pixel XL packs a 12.3-megapixel primary camera on the rear and a 8-megapixel front shooter for selfies.The Google Pixel XL runs Android 7.1 and is powered by a 3450mAh non removable battery.
The OnePlus 3T is a smartphone made by OnePlus. It is the successor to the OnePlus 3 and was revealed on 15 November 2016. The OnePlus 3T has 6 GB of RAM, the option of 64 or 128 GB of storage, and a Qualcomm Snapdragon 821 processor that can support the new Daydream VR headset from Google. It features the identical Optic AMOLED display, the same Sony IMX 298 rear camera sensor, and the same Dash Charging technology as its predecessor. The phone comes with a faster Qualcomm Snapdragon 821 processor and a bigger 3400 mAh battery.The OnePlus 3T contains the same rear-facing Sony 16 MP sensor as the predecessor. The phone now has an upgraded 16 MP f/2.0 Samsung 3P8SP Sensor with Electronic Image Stabilisation.
LG G5 is an Android smartphone developed by LG Electronics.The G5 received mixed reviews; while the device's design was praised for its shift to a metal construction while maintaining the ability to remove its battery, the modular accessory system was panned for its limited use cases and inability to perform hot swapping. LG's software was also panned for the quality of its customizations. The G5 is constructed with an aluminum unibody chassis; a "micro-dizing" process utilizing a plastic primer was used to conceal seams required for the antenna.The G5 utilizes a Qualcomm Snapdragon 820 system-on-chip accompanied by 4 GB of LPDDR4 RAM, and has 32 GB of internal storage expandable via micro SD card.The LG G5 is bundled with Android 6.0 "Marshmallow".
Nexus 6P codenamed Angler, is an Android smartphone developed and marketed by Google and manufactured by Huawei.Significant changes over previous Nexus devices include an all-aluminum based body that is thinner and lighter than the Nexus 6, a rear fingerprint reader called Nexus Imprint, a faster octa-core Snapdragon 810 v2.1 system-on-chip (SoC), an AMOLED display, better cameras, enhanced LTE connectivity, a reversible USB Type-C dock connector, and the removal of wireless charging.The phone arrives running Android 6.0 Marshmallow. The Nexus 6P features an octa-core Snapdragon 810 v2.1 system-on-chip (SoC) with four ARM Cortex-A57 cores at 1.95 GHz and four ARM Cortex-A53 cores at 1.55 GHz in a big.LITTLE architecture.


Black Friday 2019: Google Pixel 3 For Only $699
by Tegan JonesIf you're in the market for a great phone at a decent price, you can now get a Google Pixel 3 on the cheap. And it's the 128GB version
While the Google Pixel 3 is over a year old, it's still a really solid handset.
It has slightly better specs than its mid-range version, the Google Pixel 3a, which we absolutely adore. And thanks to this Black Friday deal, you can get the flagship Pixel 3 for just $50 more than what the 3a costs.
The Google Pixel 3 is now just $699 on Amazon.
Also, if you've been itching to get your fingertips on Android 10, the Pixrl 3 already has. Even Samsung devices don't have the new operating system yet.
Google's Pixel 3 comes with a 5.4-inch POLED display, Snapdragon 845 processor, 2,915mAH battery, 4GB of RAM and 128GB storage.
As for the battery you're looking at dual 12MP rear cameras and a 8Mp selfie cam. Not bad at all.
Get the deal here
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logback-access-spring-boot-starter-sample-jetty
The sample application of "logback-access-spring-boot-starter" for Jetty.


Fred2.Core package¶
Submodules¶
Fred2.Core.Allele module¶
-
class
Fred2.Core.Allele.
Allele
(_name, prob=None)[source]¶ Bases:
Fred2.Core.Base.MetadataLogger
This class represents an Allele and stores additional information as a dictionary
Parameters: name (str) – the name of the MHC allele (new nomenclature A*01:01)
Fred2.Core.Base module¶
https://docs.python.org/3/library/abc.html
-
class
Fred2.Core.Base.
ACleavageFragmentPrediction
[source]¶ Bases:
object
-
cleavagePos
¶ parameter specifying the position of aa (within the prediction window) after which the sequence is cleaved
Returns:
-
name
¶ Returns the name of the predictor
Returns:
-
predict
(_aa_seq, **kwargs)[source]¶ Predicts the probability that the fragment can be produced by the proteasom
Parameters: _aa_seq (Bio.Seq) – The sequence to be cleaved (must be an instance of Bio.Seq Returns: Returns a AResult object for the specified Bio.Seq
-
registry
= {'ginodi': {'1.0': <class 'Fred2.CleavagePrediction.PSSM.PSSMGinodi'>}}¶
-
supportedLength
¶ Returns the supported lengths of the predictor
Returns: list(int) - Supported peptide length
-
version
¶ parameter specifying the version of the prediction method
-
-
class
Fred2.Core.Base.
ACleavageSitePrediction
[source]¶ Bases:
object
-
cleavagePos
¶ parameter specifying the position of aa (within the prediction window) after which the sequence is cleaved (starting from 1)
Returns:
-
name
¶ Returns the name of the predictor
Returns:
-
predict
(_aa_seq, **kwargs)[source]¶ Predicts the proteom cleavage site of the given sequences
Parameters: _aa_seq (Bio.Seq) – The sequence to be cleaved (must be an instance of Bio.Seq Returns: Returns a AResult object for the specified Bio.Seq
-
registry
= {'proteasmm_i': {'1.0': <class 'Fred2.CleavagePrediction.PSSM.ProteaSMMImmuno'>}, 'netchop': {'3.1': <class 'Fred2.CleavagePrediction.External.NetChop_3_1'>}, 'proteasmm_c': {'1.0': <class 'Fred2.CleavagePrediction.PSSM.ProteaSMMConsecutive'>}, 'pcm': {'1.0': <class 'Fred2.CleavagePrediction.PSSM.PCM'>}}¶
-
supportedLength
¶ Returns the supported lengths of the predictor
Returns: list(int) - Supported peptide length
-
version
¶ parameter specifying the version of the prediction method
-
-
class
Fred2.Core.Base.
AEpitopePrediction
[source]¶ Bases:
object
-
convert_alleles
(alleles)[source]¶ Converts alleles into the interal allele representation of the predictor and returns a string representation
Parameters: alleles (list(Allele)) – The alleles for which the internal predictor representation is needed Returns: Returns a string representation of the input alleles
-
name
¶ Returns the name of the predictor
Returns:
-
predict
(peptides, alleles=None, **kwargs)[source]¶ Predicts the binding affinity for a given peptide or peptide lists for a given list of alleles. If alleles is not given, predictions for all valid alleles of the predictor is performed. If, however, a list of alleles is given, predictions for the valid allele subset is performed.
Parameters: - peptides (Peptide/list(Peptide)) – The peptide objects for which predictions should be performed
- alleles (Allele/list(Allele)) – An Allele or list of Alleles for which prediction models should be used
Returns: Returns a AResult object for the specified Peptides and Alleles
-
registry
= {}¶
-
supportedAlleles
¶ Returns a list of valid allele models
Returns: List of allele names for which the predictor provides models
-
supportedLength
¶ Returns a list of supported peptide lenghts
Returns: List of supported peptide lengths
-
version
¶
-
-
class
Fred2.Core.Base.
AExternal
[source]¶ Bases:
object
Base class for external tools
-
command
¶ defines the external execution path ? :return:
-
get_external_version
(path=None)[source]¶ Returns the external version of the tool by executing >{command} –version
might be dependent on the method and has to be overwritten therefore it is declared abstract to enforce the user to overwrite the method. The function in the base class can be called with super()
Parameters: path ((str)) – - optional specification of executable path if deviant from self.__command
Returns: str - The external version of the tool
-
is_in_path
()[source]¶ checks whether the specified execution command can be found in PATH
Returns: bool - Whether or not command could be found in PATH
-
parse_external_result
(_file)[source]¶ Parses external NetMHC results and returns a AResult object
Parameters: _file (str) – The file path or the external prediction results Returns: AResult - Returns a AResult object
-
prepare_peptide_input
(_peptides, _file)[source]¶ Prepares sequence input for external tools and writes them to _file in the specific format
NO return value!
Param: (list(str)) _peptides: the peptide sequences to write into _file Parameters: _file ((File)) – File handler to input file for external tool
-
-
class
Fred2.Core.Base.
APluginRegister
(name, bases, nmspc)[source]¶ Bases:
abc.ABCMeta
This class allows automatic registration of new plugins.
-
class
Fred2.Core.Base.
ATAPPrediction
[source]¶ Bases:
object
-
name
¶ Returns the name of the predictor
Returns:
-
predict
(peptides, **kwargs)[source]¶ Predicts the TAP affinity for the given sequences
Parameters: list(Peptide)/Peptide – Peptides for which TAP affinity should be predicted Returns: Returns a TAPResult object
-
registry
= {}¶
-
supportedLength
¶ Returns the supported lengths of the predictor
Returns: list(int) - Supported peptide length
-
version
¶ parameter specifying the version of the prediction method
-
-
class
Fred2.Core.Base.
MetadataLogger
[source]¶ Bases:
object
This class provides a simple interface for assigning additional metadata to any object in our data model. Examples: storing ANNOVAR columns like depth, base count, dbSNP id, quality information for variants, prediction scores for peptides etc. Normally used by custom toolbox functions and importers.
The saved values are accessed via
log_metadata()
andget_metadata()
Fred2.Core.Generator module¶
-
Fred2.Core.Generator.
generate_peptides_from_protein
(proteins, window_size, peptides=None)[source]¶ Creates all peptides for a given window size, from a given protein. The result is a generator.
Parameters: - proteins (Protein) – (list of) protein(s) from which a list of unique peptides should be generated
- window_size (int) – size of peptide fragments
-
Fred2.Core.Generator.
generate_peptides_from_variants
(vars, length, dbadapter)[source]¶ generates polymorphic peptides based on given variants
Parameters: - vars (list(Variant)) – list of Variants
- length (int) – length of peptides
- dbadapter – DBAdapter to fetch the transcript sequences
Returns: List(Peptide) – A list of polymorphic peptides
-
Fred2.Core.Generator.
generate_proteins_from_transcripts
(transcripts, table='Standard', stop_symbol='*', to_stop=False, cds=False)[source]¶ Enables the translation from a transcript to a protein instance
Param: list(Transcript)/Transcript - a list of transcripts to translate to proteins Returns: (Protein) – the protein that corresponds to the transcript
-
Fred2.Core.Generator.
generate_transcripts_from_tumor_variants
(normal, tumor, dbadapter)[source]¶ generates all possible transcript variations of the given variants
Parameters: - normal (list(Variant)) – A list of variants of the normal tissue
- tumor (list(Variant)) – A list of variant of the cancer tissue for which transcript should be generated
- dbadapter (ADBAdapter) – a DBAdapter to fetch the transcript sequences
Returns: (Generator(Transcripts)) – a generator of transcripts with all possible variations determined by the given variant list
-
Fred2.Core.Generator.
generate_transcripts_from_variants
(vars, dbadapter)[source]¶ generates all possible transcript variations of the given variants
Parameters: - vars (list(Variant)) – A list of variants for which transcripts should be build
- dbadapter (ADBAdapter) – a DBAdapter to fetch the transcript sequences
Returns: (Generator(Transcripts)) – a generator of transcripts with all possible variations determined by the given variant list
Fred2.Core.Peptide module¶
-
class
Fred2.Core.Peptide.
Peptide
(_seq, proteins=None, variants=None, transcripts=None)[source]¶ Bases:
Fred2.Core.Base.MetadataLogger
,Bio.Seq.Seq
This class encapsulates a Peptide, belonging to one or several Proteins.
Note
For accessing and manipulating the sequence see also
Bio.Seq.Seq
(from Biopython)
Fred2.Core.Protein module¶
-
class
Fred2.Core.Protein.
Protein
(_seq, _gene_id='unknown', _transcript_id=None, _orig_transcript=None, _vars=None)[source]¶ Bases:
Fred2.Core.Base.MetadataLogger
,Bio.Seq.Seq
Protein corresponding to exactly one transcript.
Note
For accessing and manipulating the sequence see also
Bio.Seq.Seq
(from Biopython)Parameters: - gene_id (str) – ID of the genome
- transcript_id (str) – ID of the corresponding transcript
- orig_transcript (Transcript) – Reference to the originating transcript
- _vars (dict(int,list(Variant))) – Nonsynonymous variants that are assoziated with the protein. key=position within protein, value=list of variants at that pos
-
newid
= <method-wrapper 'next' of itertools.count object>¶
Fred2.Core.Result module¶
-
class
Fred2.Core.Result.
AResult
(data=None, index=None, columns=None, dtype=None, copy=False)[source]¶ Bases:
pandas.core.frame.DataFrame
A AResult object is a DataFrame with with multi-indexing.
This class is used as interface and can be extended with custom short-cuts for the sometimes often tedious calls in pandas
-
class
Fred2.Core.Result.
CleavageFragmentPredictionResult
(data=None, index=None, columns=None, dtype=None, copy=False)[source]¶ Bases:
Fred2.Core.Result.AResult
Epitope prediction result
-
filter_result
(expressions)[source]¶ filters a result data frame based on a specified expression consisting of a list of triple with (method_name, comparator, threshold). The expression is applied to each row. If any of the columns full fill the criteria the row remains.
Parameters: expressions (list((str,comparator,float))) – A list of triples consisting of (method_name, comparator, threshold) Returns: filtered result object
-
-
class
Fred2.Core.Result.
CleavageSitePredictionResult
(data=None, index=None, columns=None, dtype=None, copy=False)[source]¶ Bases:
Fred2.Core.Result.AResult
Epitope prediction result
-
filter_result
(expressions)[source]¶ filters a result data frame based on a specified expression consisting of a list of triple with (method_name, comparator, threshold). The expression is applied to each row. If any of the columns full fill the criteria the row remains.
Parameters: expressions (list((str,comparator,float))) – A list of triples consisting of (method_name, comparator, threshold) Returns: filtered result object
-
-
class
Fred2.Core.Result.
Distance2SelfResult
(data=None, index=None, columns=None, dtype=None, copy=False)[source]¶ Bases:
Fred2.Core.Result.AResult
Distance2Self prediction result
-
class
Fred2.Core.Result.
EpitopePredictionResult
(data=None, index=None, columns=None, dtype=None, copy=False)[source]¶ Bases:
Fred2.Core.Result.AResult
A AResult object is a DataFrame with with multi-indexing, where column Id are the prediction model (i.e HLA allele for epitope prediction), row ID the target of the prediction (i.e. peptide) and the second row ID the predictor (i.e BIMAS)
Epitope prediction result
-
filter_result
(expressions)[source]¶ filters a result data frame based on a specified expression consisting of a list of triple with (method_name, comparator, threshold). The expression is applied to each row. If any of the columns full fill the criteria the row remains.
Parameters: expressions (list((str,comparator,float))) – A list of triples consisting of (method_name, comparator, threshold) Returns: filtered result object
-
-
class
Fred2.Core.Result.
TAPPredictionResult
(data=None, index=None, columns=None, dtype=None, copy=False)[source]¶ Bases:
Fred2.Core.Result.AResult
Epitope prediction result
-
filter_result
(expressions)[source]¶ filters a result data frame based on a specified expression consisting of a list of triple with (method_name, comparator, threshold). The expression is applied to each row. If any of the columns full fill the criteria the row remains.
Parameters: expressions (list((str,comparator,float))) – A list of triples consisting of (method_name, comparator, threshold) Returns: filtered result object
-
Fred2.Core.Transcript module¶
-
class
Fred2.Core.Transcript.
Transcript
(_seq, _gene_id='unknown', _transcript_id=None, _vars=None)[source]¶ Bases:
Fred2.Core.Base.MetadataLogger
,Bio.Seq.Seq
A Transcript is the mRNA sequence containing at no or several variations.
Note
For accessing and manipulating the sequence see also
Bio.Seq.Seq
(from Biopython)Parameters: - gene_id (str) – Genome ID
- transcript_id (str) – Transcript RefSeqID
- vars (dict(int,Variant)) – Dict of Variants for specific positions in the transcript. key=position, value=Variant
-
newid
= <method-wrapper 'next' of itertools.count object>¶
Fred2.Core.Variant module¶
-
class
Fred2.Core.Variant.
MutationSyntax
(transID, transPos, protPos, cds, aas)[source]¶ This class represents the mutation syntax of a variant and stores its transcript and protein position
Parameters: - transID (str) – the transcript id
- transPos (int) – the position of the variant within the transcript
- protPos (int) – the protein position of the variant within the transcript
- cds (str) – the complete cds_mutation_syntax string
- aas (str) – the complete protein_mutation_syntax string
-
class
Fred2.Core.Variant.
Variant
(id, type, chrom, genomePos, ref, obs, coding, isHomozygous, isSynonymous, experimentalDesign=None, metadata=None)[source]¶ Bases:
Fred2.Core.Base.MetadataLogger
A Variant contains information about a single genetic modification of the reference genome.
Parameters: - id (str) – Variant id
- type (Enum.VariationType) – An Enum type of the variant either SNP, DEL, or INS
- chrom (str) – The chromosome on which the variant lies
- genomePos (int) – The genomic position of the variant
- ref (str) – The reference seq at the genomic position
- obs (str) – The observed variation at the genomic position
- coding (dict(str,MutationSyntax)) – A dictionary of associated transcripts. Key=transcript_id, value=MutationSyntax
- isHomozygous (bool) – defines if variant is homozygous or not
- isSynonymous (bool) – defines if variant is a synonymous mutation or not
- offsets (dict(str,int)) – the position offset this variant has in a specific transcript-variant key=transcript-variant-id (xxx:FRED2_nn) value=offset
- metadata (defaultdict(list)) – meta information (not relevant for core functionality of Fred2)
-
get_protein_position
(trans_variant_id)[source]¶ Note
May only be used for transcript variants that were created from this variant via :func:
Generator.generate_transcripts_from_variants. ..TODO:: what? that should not be encoded in transcript id but in separate member
returns the specific protein position of a given transcript_id. If variant is not associated with the given transcript id the function throws a KeyError
Parameters: transcriptId (str) – A transcript_id Returns: (int) – the protein position of the variant Raises: KeyError
-
get_transcript_position
(trans_variant_id)[source]¶ Note
May only be used for transcript variants that were created from this variant via :func:
Generator.generate_transcripts_from_variants. ..TODO:: what? that should not be encoded in transcript id but in separate member
returns the specific transcript position of a given transcript_id. If variant is not associated with the given transcript id the function throws a KeyError
Parameters: transcriptId (str) – A transcript_id Returns: (int) – transcript position Raises: KeyError
-
Fred2.Core.Variant.
VariationType
¶ Enum for variation types: type.SNP, type.DEL, type.INS, type.FSDEL, type.FSINS, type.UNKNOWN
alias of
Enum


CLS Medical, Inc
Sales Order Report
First name
Last name
Location
Access
First name
Last name
Location
Access


Tense
: tense
Tense is a feature that specifies the time when the action took / takes / will take place, in relation to the current moment or to another action in the utterance.
Past
: past tense
The past tense denotes actions that happened before the current moment. Past tense in Ukrainian is expressed by a special past form, historically derived from the past participle (also called active participle or l-participle – which is why past is the only tense that also varies in gender). In the pre-past version of the past tense the same form is accompanied by a past auxiliary verb. The past tense form can also be used to form present (or past, depending on the tense of the auxiliary verb) conditional. Since both pre-past and conditionals are formed analytically, they do not receive any morphological tags.
Examples
- Я пішов додому. “I
Masc
have gone home.” - Я пішла додому. “I
Fem
have gone home.” - Я пішло додому. “I
Neut
have gone home.” - Ти пішов додому. “You
Masc
have gone home.” - Ти пішла додому. “You
Fem
have gone home.” - Ти пішло додому. “You
Neut
have gone home.” - Він пішов додому. “He has gone home.”
- Вона пішла додому. “She has gone home.”
- Воно пішло додому. “It has gone home.”
Pres
: present tense
The present tense denotes actions that are happening right now or that usually happen. Only imperfective verbs can have present forms in Ukrainian. Morphologically present forms of perfective verbs have actually a future meaning and are marked Tense=Fut
, see the last example below.
Examples
- Ходжу додому. “I come home.” (Ходити is an imperfective verb.)
- Приходжу додому. “I keep/am coming home.” (Приходити is an imperfective verb.)
- Йду додому. “I go / am going home.” (Йти is an imperfective verb.)
- Прийду додому. “I will come home.” (Прийти is a perfective verb and it has a future meaning on the contrary to the imperfective forms above.)
Fut
: future tense
The future tense denotes actions that will happen after the current moment. It is formed in Ukrainian in one of three ways, partly depending on the aspect of the verb:
- Perfective verbs use present declension patterns to form future tense.
- The verb бути “to be” has a set of distinct future forms. They combine a future stem буд- with present endings.
Although these forms are morphologically very close to the present forms, they are tagged
Tense=Fut
because the same lemma has also present forms and the feature must distinguish the two. - Synthetic future forms are one of distinctive features of Ukrainian among other Slavic languages. They use the infinitive stem and endings being historically shortened future forms of the auxiliary verb мати “to have”. The future synthetic forms of мати “to have”: матиму, матимеш, матиме, матимемо, матимете, матимуть. The future synthetic forms of робити “to do”: робитиму, робитимеш, робитиме, робитимемо, робитимете, робитимуть.
- Note that all the imperfective Ukrainian verbs, with the exception of бути “to be” itself, can also form a periphrastic future, consisting of the future form of the auxiliary бути, _ and the infinitive of the content verb. Only the auxiliary will have
Tense=Fut
, while there will be no tense information at the infinitive.
Examples
- Прийду додому. “I will come home.” (Прийти is a perfective verb.)
- Йтиму додому. “I will be coming home.” (Йти is an imperfective verb, here the syntheic future form is used.)
- Буду йти додому. “I will be coming home.” (Йти is an imperfective verb, the future tense is formed periphrastically here.)
Tense in other languages: [abq] [aqz] [arr] [bej] [bg] [bm] [cs] [cy] [el] [en] [fi] [fr] [ga] [gn] [gub] [hu] [hy] [it] [jaa] [ky] [pcm] [qpm] [ru] [sah] [say] [sl] [sv] [tr] [tt] [u] [uk] [urb] [urj]


{% extends "base.html" %} {% block internal_css %} {% endblock %} {% block body %}
For the latest copy of my resume download it underneath my picture, otherwise check out some of the content below.


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 4-6
Dietary
Pull a festive dessert out of the bag with James Martin's make-ahead Christmas tree-shaped pavlova.
For this recipe you will need a hand-held mixer or a freestanding mixer and a piping bag.
Preheat the oven to 110C/90C Fan/Gas ½. Line a large baking tray with silicone or baking paper.
Whisk the egg whites in a clean bowl using an electric whisk, or in the bowl of a freestanding mixer, until soft peaks form when the whisk is removed.
Add 300g/10½oz of the caster sugar and continue to whisk until the mixture is smooth and glossy and stiff peaks form when the whisk is removed. You should hear the machine dropping down a gear as the meringue mixture reaches the correct consistency. Reduce the speed of the whisks and add the cornflour and vinegar. Whisk until very stiff.
Transfer the meringue mixture to a piping bag. Pipe an outline of a Christmas tree shape onto the prepared baking tray. Pipe a rectangular trunk at the bottom. Fill the outline with the remaining meringue mixture, then smooth the top using a palette knife. With the last of the meringue mixture, pipe around the edge of the Christmas tree shape to form a raised edge.
Bake the Christmas tree in the oven for 1½-2 hours, or until firm, then remove from the oven and set aside to cool.
Meanwhile, make the filling. Bring the remaining caster sugar and 150ml/5fl oz water to the boil in a saucepan. Stir in the ground cloves, ground ginger, cinnamon stick, and lemon and orange peel and boil for a further 4-5 minutes, or until thickened and syrupy. Stir in the blackberries, then remove the pan from the heat and set aside until completely cool.
When the meringue and fruit syrup have cooled, whisk the cream and vanilla bean paste in a bowl until soft peaks form when the whisk is removed, then stir in the crème de cassis.
To assemble, transfer the meringue to a serving platter and spread the cream on top. Spoon over the fruit syrup. Serve immediately.


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---
layout: page
title: "CURRICULUM VITAE"
description: ""
header-img: "img/UCLA.jpeg"
---
Ph.D. candidate University of California, Los Angeles
google scholar
EDUCATION
2021: Ph.D. in Geophysics, University of California, Los Angeles
2018: M.S. in Geophysics University of California, Los Angeles
2016: B.S. in Geophysics，Nanjing University
2015: Visiting Student, University of California, Berkeley
PUBLICATIONS
- Feng, T., Meng, L., Huang. (2020). Detecting Offshore Seismicity: Combining Backprojection Imaging and Matched‐Filter Detection
- Feng, T., & Meng, L. (2018). A High‐Frequency Distance Metric in Ground‐Motion Prediction Equations Based on Seismic Array Backprojections. Geophysical Research Letters, 45(21), 11-612.
- Bao, H., Ampuero, J. P., Meng, L., Fielding, E. J., Liang, C., Milliner, C. W., Feng, T. & Huang, H. (2019). Early and persistent supershear rupture of the 2018 magnitude 7.5 Palu earthquake. Nature Geoscience, 12(3), 200-205.
- Hassani, B., Yong, A., Atkinson, G. M., Feng, T., & Meng, L. (2019). Comparison of site dominant frequency from earthquake and microseismic data in California. Bulletin of the Seismological Society of America, 109(3), 1034-1040.
RESUME
CURRICULUM VITAE


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Conflict.
May 31, 2015
Having thought about it, I can understand why some people say that sometimes, your worst enemy is yourself. In my case, I've more or less been told by my best friends that I'm more selfless than selfish; always going the extra mile for people other than myself (unnecessarily so, they've added). In fact, it didn't really occur to me just how large a problem I had created for myself by shouldering the problems of others until I had asked one of my best friends what their first impression of me was.
"I thought you were a great guy, but it didn't seem like you really had your own opinion."
I thought about that, and of course I asked what she meant by that, and here's what I had come to realize:
I don't mind helping others, I really do live for other people, and part of that mentality comes from my life experiences, my religion, my upbringing, and so forth. I do have my own opinions, but I often try to please as many people as I can by trying to compromise as much as possible, unnecessarily. If you asked me to tell you about the times where I did something selfishly, I could probably only give you 1 or 2 examples at best off the top of my head.
Out of those 2 examples, one of them actually is related to Dev Bootcamp. Yup. My decision to go to Dev Bootcamp is, for me, probably the most selfish decision I've ever made in my life to date, and it's one that I don't regret at all. That's not to say that making the decision to go through with DBC didn't cause me any problems along the way, though.
Breaking the news to my parents wasn't the easiest thing, but it wasn't the hardest thing either. Convincing my parents that I had decided to move out to California primarily for pursuing a career change was. See, I had come out of my first relationship roughly 6-7 months beforehand. Keywords for my parents: "first relationship," keywords for me: "6-7 months."
I'll spare you on all the details, but long story short, my parents thought that my main reason for wanting to go to California was to chase after the girl I had my first relationship with. On the other hand, I was trying to convince them the whole time that I was solely considering San Francisco because that's where a friend of mine was living and he had offered me a place to stay if I got accepted into any of the boot camps out there. You can probably imagine how that conversation kinda went.
I would say that it wasn't wrong for my parents to have the thoughts that they did, because really, they were just worried about me and didn't want me making any decisions I'd later on regret. For some reason though, even though I knew they would probably mention my ex, there was something about the way my dad brought her up in the middle of the conversation that made me angry and upset. But that's not what blew my fuse, it was something that was said towards the end of the conversation where I kind of lost it and decided to go back to my apartment and call it a day because things weren't going well and sometimes it's better to not sit around and beat a dead horse. I won't say what was said, but it was something that came off rude enough to put me at a loss for words and storm off more or less.
Halfway down the road back to my apartment, it's needless to say that I had my thoughts all messed up. I didn't know what I was going to do anymore, but I knew that I wasn't happy with where I was at my work - but I couldn't bring myself to quitting work if my parents weren't happy with me (do ya kinda see or have a feel for my problem here?).
I ended up turning around and going back home. I let go of whatever pride I had because in the end, I reminded myself that my parents were just being parents. I mean, I can only imagine how hard it is for parents to deal with their children and their relationships, that's why they're so overprotective sometimes. I'll leave it at that because, well I would end up writing about how grateful I am to my parents for days otherwise. And the conversation that took place afterwards, as in after I made it back to my parent's house, ended up lasting for another 3 hours. So yeah, it was a rough and busy weekend for me, and it was the same weekend I came back from my California trip, too.
Looking back, I really just wish that I had been able to keep my cool the way I had originally planned to when I thought about how I'd tell my parents and how I imagined the conversation going. But then I realize I'm human and that sometimes, your heart and mind aren't in the same place sometimes and that's how life seems to work. And besides, would you really want to read more than you've already read so far? I just gave you the TDLR of a 5-6 hour conversation, believe it or not. But hey, if you want to hear more about my story or stories for that matter, hit me up and let's grab a bite to eat or get something to drink ya?
So what did I learn after all of that driving back and forth and breaking the news to my family and friends that weekend? A sh*t ton.
I learned that I have an amazing family and some really supportive friends. But I also learned that being family and being friends doesn't mean you always agree on the same thing.  I learned that everyone was actually more relieved than I was, because for once they saw me doing something solely for myself and as funny as it is, they were probably more proud of me than I was of myself.
It's these kinda things that make me realize that I'm a lot luckier than I think I am.


context_processors/
Django context processors. If you want to
include expensive data to compute here, consider to use
variables as functions or temporal caches.error/
Files that render error messages in production. In development
environment we can see the result of these renderizations accessing to the URL
some-url. The error file 503
is crazy.


In our previous plan for the initial distribution accounts, we tried to address concerns that we might dump coins on the market. While this plan was thoughtful, reasonable, and enjoyed a positive reception from the community, it left some lingering concerns about the number of coins we still controlled.
We have never had any intention to sell large numbers of coins. Our reluctance to release more coins sooner, as we have explained many times, was to try to allow for a more equitable distribution of coins. With the previous plan, we only intended to sell a small number of coins to provide us with the resources necessary to cover our modest expenses and devote more time to this project.
However, to an outside observer, there was still an appearance that we were hoarding a large number of coins for ourselves and were looking to cash out a large number of coins at some point. This, the second part of our plan, should lay those concerns to rest.
We will be transferring 100% of the remaining distribution funds to the cycle account.
We have not sold any coins from the distribution accounts, and we will never sell any coins from these accounts. We have only used them for purposes that we felt were reasonable, responsible, and positive for the community: bounties, airdrops to the mesh, a single reimbursement to someone who lost coins due to a confusion between private and public keys, and a single payment to the first non-Nyzo verifier who found this project on GitHub. We will carefully document every transfer from these accounts, and we will gladly explain any transfers that anyone finds to be of concern.
We will complete the transfer of all coins from the distribution accounts to the cycle account by 2019-10-01 00:00:00.000 UTC.
This additional funding will make the initial cycle account more than 150% larger than was initially planned. We hope and expect that this fund will be used wisely to reward positive contributions to Nyzo. We have already seen members of the community provide significant value to this project in the form of tutorials, documentation, analysis, monitoring, and integrations. The community should decide what contributions are valuable, and it would be unfair for us to continue to hold any coins over which we have unilateral control.
The distributed, flexible consensus of Nyzo makes it uniquely amenable to heterogeneity among verifiers in the mesh. With the exception of the version-1 blockchain upgrade, Nyzo has been highly compatible across versions. We hope that other implementations of Nyzo arise — both forks of our version and novel implementations. And we hope that verifiers adopt whatever version of Nyzo works best for their particular needs. If we make a change to the Nyzo software that you don't like, don't use it. If you want other verifiers to use your version instead, fork the repository and convince other verifiers to run your version instead of ours. This has always been an option with Nyzo, and we hope to see this option exercised more in the future.
After we transfer the remainder of the initial distribution funds to the cycle account, we will no longer control Nyzo in any significant way. The official verifiers are gone. The nyzo.co website has no special access to the mesh, and it never has. We will hold less than ∩610,000 after we complete the transfers to the cycle account. This is the sum of all coins that all of our verifiers, official and anonymous, have earned since the beginning of Nyzo. To this date, we have not sold a single coin that any of our verifiers have earned. We currently have only 9 in-cycle verifiers — we had 10, but we recently lost a verifier due to the version-541 sentinel issue.
Overall, we will have far less control over the cycle and, we suspect, control of fewer coins than some early adopters. However, advocating for democracy and decentralization while trying to maintain control of the system doesn't make sense to us.
Nyzo is wonderful in a lot of ways, but we think its strongest attribute is its community. That's why turning over the remainder of the initial-distribution coins to the community is the right thing to do.


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About me
I am currently entering my third year at University of Toronto with double majors in Statistics and Cognitive
Science. I am interested in full-stack web development and software engineering in general. I am currently
interning at Coursera on the Growth-Degrees team for this summer.


Cão da Serra de Aires
| Cão da Serra de Aires Picture | |
| Breed Information | |
|---|---|
| Popularity | # |
| Name | Cão da Serra de Aires |
| Other names | Portuguese Sheepdog, Portuguese Shepherd Dog |
| Origin | Portugal |
| Breed Group | Herding |
| Size | Medium |
| Type | Purebred |
| Life span | 12-14 years |
| Temperament | IntelligentLively |
| Height | 16-22 inches (41-56 cm) |
| Weight | 26-40 pounds (12-18 kg) |
| Colors | BlackFawnGreyWolf grey |
| Litter Size | 3-6 puppies |
| Puppy Price | Average $800 - $1000 USD |
| Breed Characteristics | |
| Adaptability | 3 stars |
| Apartment Friendly | 1 starsThe Portuguese Sheepdog is not recommended for apartment life. Assuming it gets enough exercise it will be calm indoors. As a working dog with a need for exercise, it prefers living outdoors, but does enjoy an occasional visit in the home. |
| Barking Tendencies | 2 starsOccassional |
| Cat Friendly | 3 stars |
| Child Friendly | 4 starsGood with Kids: This is a suitable breed for kids and is known to be playful, energetic, and affectionate around them. |
| Dog Friendly | 3 stars |
| Exercise Needs | 3 starsThe Portuguese Sheepdog has extreme stamina. If it gets enough exercise, including a daily, long walk, it will be calm in the house. This breed does very well in agility. |
| Grooming | 2 starsCheck the all-weather coat regularly for tangles but do not brush the coat too much, as it will affect the texture and the rough look. Trim any excess hairs between the pads of the feet. This breed should be shampooed only when necessary. |
| Health Issues | 2 starsHypoallergenic: No |
| Intelligence | 3 starsRanking: (#). (See All Rankings) |
| Playfulness | 3 stars |
| Shedding Level | 3 starsModerate Shedding: Routine brushing will help. Be prepared to vacuum often! |
| Stranger Friendly | 3 stars |
| Trainability | 3 starsCão da Serra de Aires can compete in dog agility trials, obedience, showmanship, flyball, tracking, and herding events. Herding instincts and trainability can be measured at noncompetitive herding tests. Portuguese Sheepdogs exhibiting basic herding instincts can be trained to compete in herding trials. |
| Watchdog Ability | 3 stars |
| Cão da Serra de Aires Puppy Picture | ||
| Puppy Names | ||
|---|---|---|
| Rank | Male | Female |
| 01 | Buddy | Molly |
| 02 | Max | Bella |
| 03 | Lucky | Chloe |
| 04 | Jack | Luna |
| 05 | Duke | Abbie |
| 06 | Milo | Ellie |
| 07 | Oliver | Zoey |
| 08 | Oscar | Ellie |
| 09 | Leo | Nala |
| 10 | Gus | Annie |
| See All Names › |
| Overview |
|---|
|
The coat of the Portuguese Sheepdog is either long and smooth or with a slight wave. It does not have an undercoat. Colors include yellow-red chestnut, gray, fawn, wolf or black. A few white hairs are permissible but patches of white hairs are not, except for white patches on the chest. The forelimbs are symmetrically covered with long hair. It has a beard, which gives it a mature look. The eyes are round and dark, with a gentle expression. The moderately sized ears usually hang down the sides of the cheeks, but sometimes they are cropped. Cropping is banned in some European countries. For his moderate size, the dog appears rather high on the legs. His feet are particularly adaptable to rough terrain. His legs are strong and his chest is deep. The hair is longer on the head, giving the dog bangs and a banged-up untidy look. It has a wide, sturdy head with a prominent nose, and strong jaws and teeth. The tail is pointed, reaching the dog's hocks. The breed standard states that the ideal Cão da Serra de Aires is "exceptionally intelligent and very lively.", devoted to the flock and the shepherd but wary of strangers and a good night guarding dog. |
| History |
| The Portuguese Sheepdog was developed in Portugal for use as a sheepdog, guard and general farm dog. It is thought to be descended from the Pyrenean Sheepdog with crosses to the Briard. Currently the breed is used particularly in the Alentejo, for guarding and driving all the flocks and herds (sheep, goats, cattle, horses, pigs). The Portuguese Sheepdog was recognized by the United Kennel Club July 1, 2006. |


NCSS Murder
Map
List
Stats
Profiles
Achievements
Admin
Quick Stats
23 people are still alive
14 people were murdered yesterday
Most Wanted
Vivian Li with 18 kills
Murder map


This page can be used to generate drop crafting command! Created by Exortions. If you have any issues, feel free to contact me on Discord (Exo#0001)! Works for 1.13+
Put what items you want to be in the crafting system! E.g.
diamond_sword{Enchantments:[{id:"sharpness",lvl:3}]}
Result when item is crafted e.g.
diamond_sword
Result NBT e.g.
Enchantments:[{id:"sharpness",lvl:5}],HideFlags:31,display:{Name:'[{"text":"A Diamond Sword!","color":"dark_aqua","italic":false,"bold":true}]'}
For datapacks only (Don't do this if you are doing the command)
Put the particle you want to appear when item is crafted (e.g. smoke)
Put the sound you want to hear when item is crafted (e.g. block.lava.extinguish)


This transition indicates that a Vendor sends a request to update an activated listing. Therafter, Distributors are able to complete this request and update required listing data. In case the update request is ignored or rejected, the listing remains the original listing data.
A listing with the Listed status.
A submitted request to update an activated listing.


 What next?
Simply log into the app with your email and password, and you'll soon see additional content from {{ organization }} in your feed.
If you haven't already, you can still download the app, too.
{% elif organization or program %}Compass is a free smartphone app that will send you useful tips that help you reach your goals. Compass is partnered with {{ organization|default:"your organization" }} to help {{ program|default:"you" }} succeed.
You're almost finished. Click the button below to confirm your enrollment in {% if program %}{{ program }}{% else %} {{ organization|default:"Compass" }}{% endif %}.
{% else %}Oops! It looks like you got here by mistake. If you followed a link, to get here, you may want to try it again.
Please feel free to contact us if you need help.
{% endif %}


This overlay pulses in an infinite loop. Use the slider to change the animation's cycle duration.
<ui-overlay animation="pulse" duration="?"></ui-overlay>
Current Duration: 5 seconds.


Before Change
recipyrc = recipyenv.get_recipyrc()
helpers.update_recipyrc(recipyrc, "data", "file_diff_outputs")
exit_code, _ = self.run_script()
assert exit_code == 0, ("Unexpected exit code " + str(exit_code))
_, filediffs = helpers.get_log(recipyenv.get_recipydb())
assert filediffs is None, "Expected filediffs to be None"
exit_code, _ = self.run_script()
assert exit_code == 0, ("Unexpected exit code " + str(exit_code))
_, filediffs = helpers.get_log(recipyenv.get_recipydb())
assert filediffs is not None, "Expected filediffs not to be None"
assert filediffs["filename"] == self.output_file,\
After Change
assert len(files) >= 1, "Unexpected number of files"
assert not isinstance(files[0], list), "Unexpected list"
def test_data_file_diff_outputs(self):
If [data].file_diff_outputs is present then:
* If output files are created, then there are no "filediffs"
for the run.
* If output files with the same content are created, then
there are "filediffs" for the run, with an empty "diffs"
value.
recipyrc = recipyenv.get_recipyrc()
helpers.update_recipyrc(recipyrc, "data", "file_diff_outputs")
helpers.execute_python([self.script, self.input_file,
self.output_file])
_, filediffs = helpers.get_log(recipyenv.get_recipydb())
assert filediffs is None, "Expected filediffs to be None"
helpers.execute_python([self.script, self.input_file,
self.output_file])
_, filediffs = helpers.get_log(recipyenv.get_recipydb())
assert filediffs is not None, "Expected filediffs not to be None"
assert filediffs["filename"] == self.output_file,\
("Expected filediffs["filename"] to be " + self.output_file)


{% extends "layout.html" %} {% block page_title %} Education {% endblock %} {% block content %} {% include "../includes/phase_banner_alpha.html" %} Education Are you currently in education? Yes No How many hours a week are you in education? Less than 16 hours a week 16 hours or more a week Are you currently getting Disability Living Allowance (DLA) or Personal Independence Payment (PIP)? Yes No Course name Address of school, college or university When did your course start? Day Month Year When does the course end? Day Month Year Are you currently in training? Yes No How many hours a week are you in training? Less than 16 hours a week 16 hours or more a week Are you currently getting Disability Living Allowance (DLA) or Personal Independence Payment (PIP)? Yes No Name of training scheme Address where you're being trained When did your training start? Day Month Year When does your training end? Day Month Year Are you currently in an apprenticeship? Yes No How many hours a week are you on your apprenticeship? Less than 16 hours a week 16 hours or more a week Are you currently getting Disability Living Allowance (DLA) or Personal Independence Payment (PIP)? Yes No Apprenticeship name Address of apprenticeship When did your apprenticeship start? DD MM YYYY Day Month Year When does your apprenticeship end? Day Month Year {% endblock %}
Address of school, college or university
Address where you're being trained
Address of apprenticeship
DD MM YYYY


dataset stores JSON objects and can store also data frames similar to that used in Python, R and Julia. This document outlines the ideas behings dataset’s implementation of data frames.
Collections are at the core of the dataset tool. A collection is a pairtree directory structure storing JSON objects in plaintext with optional attachments. The root folder for the collection contains a collection.json file with the metadata associating a name to the pairtree path where the json object is stored. One of the guiding ideas behind dataset was to keep everything in plain text (i.e. UTF-8) whenever reasonable. The dataset project provides Go package for working with dataset collections, a python package (based on a C-shared library included in the Go package) and a command line tool.
Dataset collections are typically stored on your local disc but may be stored easily in Amazon’s S3 (or compatible platform) or Google’s cloud storage. Dataset can also import and export CSV files and with some extra work to/from a Google Sheets.
Dataset isn’t a database (there are plenty of JSON oriented databases out there, e.g. CouchDB, MongoDB and No SQL storage systems for MySQL and Postgresql). dataset’s focus is on providing a mechanism to manage JSON objects, group them and to provide alternative data shapes for the viewing the collection (e.g. data frames and grids).
A grid is a 2D JSON array based on combining a set of keys (rows) and a list of dot paths (columns). It is similar to the data shape you’d use in spreadsheets. It is a convenient data shape for build indexes, filtering and sorting. grid support is also available in dataset’s Python 3.7 package
Here’s an example of a possible grid for titles and authors.
[
["title", "authors"],
["20,000 Leagues under the Sea", "Verne, Jules"],
["All Around the Moon", "Verne, Jules"],
["The Short Reign of Pippin IV", "Steinbeck, John"]
]
If a column is missing a value then you should see a “null” for that cell. Here is an expanded example where we’ve added a link to Project Gutenberg as a third column.
[
["title", "authors", "gutenberg_href"],
["20,000 Leagues under the Sea", "Verne, Jules", "http://www.gutenberg.org/ebooks/6538"],
["All Around the Moon", "Verne, Jules", "http://www.gutenberg.org/ebooks/16457"],
["The Short Reign of Pippin IV", "Steinbeck, John", null]
]
This example creates a two column grid with DOI and titles from a dataset collection called Pubs.ds using the dataset command. Step one, generate a list of keys piping them into dataset using the grid verb. If you didn’t want to use a pipe you could also use an option to read the keys from a file or to use all keys. The dataset keys command sends the keys to standard out one key per line, the dataset grid command reads the keys from standard input (one per line) and then creates a corresponding grid based on the dotpaths provided. In this example we’re using the paths “.doi” and “.title” from our “Pub.ds” collection. If either “.doi” or “.title” is missing in a JSON object then a “null” value will be used. This way the grid rows retain the same number of cells.
dataset keys Pubs.ds |\
dataset grid Pubs.ds .doi .title
The 2D JSON array is easy to process in programming languages like Python. Below is an example of using a grid for sorting across an entire collection leveraging Python’s standard sort method for lists.
import sys
from py_dataset import dataset
from operator import itemgetter
keys = dataset.keys("Pubs.ds")
(g, err) = dataset.grid("Pubs.ds", [".doi", ".title"])
# g holds the 2D arrary
if err != '':
print(f'{err}')
sys.exit(1)
# sort by title
g.sort(key=itemgetter (1))
for row in g:
(doi, title) = row
print(f'{doi} {title}')
Implementing the grid verb started me thinking about the similarity to data frames in Python, Julia and Octave. A frame is an ordered list of objects. It’s like a grid except that rather than have columns and row you have a list of objects and attribute names mapped to values. Frames can be retrieved as a grid (2D array) or as a list of Objects. Frames contain a additional metadata to help them persist. Frames include enough metadata to effeciently refresh objects in the list or even replace all objects in the list. If you want to get back a “Grid” of a frame you can optionally include a header row as part of the 2D array returned. dataset stores frames with the collection so unlike a grid it is available for later processing.
Frames become handy when moving data from JSON documents (tree like) to other formats like spreadsheets (table like). Date frames provide a one to one map between a 2D representation and a list of objects containing key/value pairs. Frames will become the way we define syncronization relationships as well as potentionally the way we define indexing should dataset re-aquire a search ability.
The map to frame names is stored in our collection’s collection.json Each frame itself is stored in a subdirectory of our collection. If you copy/clone a collection the frames can travel with it.
Example creating a frame named “dois-and-titles”
dataset keys Pubs.ds >pubs.keys
dataset frame-create -i pubs.keys Pubs.ds dois-and-titles \
".doi=DOI" \
".title=Title"
Or in python
keys = dataset.keys('Pubs.ds')
frame = dataset.frame_crate('Pubs.ds', 'dois-and-titles', keys, {
'.doi': 'DOI',
'.title': 'Title'
})
Example of getting the contents of an existing frame with all the metadata.
dataset frame Pubs.ds dois-and-titles
An example of getting the frame’s object list only.
dataset frame-objects Pubs.ds dois-and-titles
Or in python getting the full frame with metadata
(frame, err) = dataset.frame('Pubs.ds', 'dois-and-titles')
if err != '':
print(f'Something went wront {err}')
Or only the object list (note: we’re going to check for the frame’s existance first).
if dataset.frame_exists('Pub.ds', 'dois-and-titles'):
object_list = dataset.frame_objects('Pubs.ds', 'dois-and-titles')
Regenerating “dois-and-titles”.
dataset reframe Pubs.ds dois-and-titles
Or in python
keys = dataset.keys('Pubs.ds')
keys.sort()
frame = dataset.frame_reframe('Pubs.ds', 'dois-and-titles', keys)
dataset Pubs.ds keys >updated.keys
dataset frame-refresh -i updated.keys Pubs.ds reframe titles-and-dios
In python
frame = dataset.frame-refresh('Pubs.ds', 'dois-and-titles', updated_keys)
Labels are represented as a JSON array, when we set the labels explicitly we’re replacing the entire array at once. In this example the frame’s grid has two columns in addition the required _Key
label. The _Key
column is implied and with be automatically inserted into the label list. Additionally using frame-labels
will cause the object list stored in the frame to be updated.
dataset frame-labels Pubs.ds dois-and-titles '["Column 1", "Column 2"]'
In python
err = dataset.frame_labels('Pubs.ds', 'dois-and-titles', ["Column 1", "Column 2"])
dataset frame-delete Pubs.ds titles-and-dios
Or in python
err = dataset.frame_delete('Pubs.ds', 'dois-and-titles')
dataset frames Pubs.ds
Or in python
frame_names = dataset.frames('Pubs.ds')
Often when processing data it is useful to pull date into a grid format.
dataset provides a verb “grid” for doing just that. Below we’re going
to create a small dataset collection called grid_test.ds
, populate
it with some simple asymetric data (i.e. each record doesn’t have the
same fields) then turn this into a 2D JSON array suitable for further
processing in a language like Python, R or Julia. The grid verb
is available in the Python module for dataset so we’ll show that too.
In both examples the JSON representing our raw data seen in the file data-grids.json
From an existing collection, grid_test.ds
, create a list of keys.
dataset keys grid_test.ds >grid_test.keys
We have the following keys in our collection “gutenberg:21489”,
“gutenberg:2488”, “gutenberg:21839”, “gutenberg:3186”,
“hathi:uc1321060001561131”. Let’s pick the first one and see what fields
we might want in our grid (notice we’re using the -p
option to pretty
print the JSON record).
dataset read -p grid_test.ds "gutenberg:21489"
The fields that we’re interested in are “._Key”, “.title”, “.authors”,
Now that we have a list of keys we’re interested and and know the dot paths to the fields we’re interested in we can create our grid.
dataset grid -p -i=grid_test.keys grid_test.ds "._Key" ".title" ".authors"
The results are a 2D array wich rows for each key and cells matching the contents of the dot paths. Note that a cell may have a complex structure like that shown with “.authors”
In this example we’re use the dataset python module to read in our raw JSON data (e.g. data-grids.json) and convert it into a dataset collection called “grid_test.ds”. Next we’ll generate our set of keys and finally generate our grid as a python list of lists.
import sys
import json
from py_dataset import dataset
# Read in our test data and convert from JSON into an array of dicts
f_name = 'data-grids.json'
with open(f_name, mode = 'r', encoding = 'utf-8') as f:
src = f.read()
data = json.loads(src)
# create our collection
c_name = 'grid_test.ds'
err = dataset.init(c_name)
if err != '':
print(err)
sys.exit(1)
# load our test data
for key in data:
rec = data[key]
err = dataset.create(c_name, key, rec)
if err != '':
print(err)
sys.exit(1)
# Create a list of keys and list of dot paths
keys = dataset.keys(c_name)
dot_paths = ["._Key", ".title", ".authors"]
# now we can create our grid
(g, err) = dataset.grid(c_name, keys, dot_paths)
if err != '':
print(err)
sys.exit(1)
# Now pretty print our grid
print(json.dumps(g, indent = 4))


Utopia is a website generation framework which provides a robust set of tools to build highly complex dynamic websites. It uses the filesystem heavily for content and provides functions for interacting with files and directories as structure representing the website.
Motivation
The original Utopia project was written in PHP in the early 2000s. It consisted of an XML parser, a database layer and some code to assist with business logic. It was initially designed to reduce the amount of HTML required to build both content-centric websites and business apps. At the time, CSS was very poorly supported and thus a lot of the time, you’d be using quite complex

s with embedded
s to generate simple things like boxes with drop shadows, etc. Utopia provided a core concept - a node - which was essentially a small snippet of HTML, which could be composed into other nodes simply by using a named tag (similar to ColdFusion). Attributes and content were passed in, and thus you could easily build complex pages with simple semantic markup.
At the time, the available frameworks were pretty basic. Utopia was a working, albeit poor, implementation of MVC and supported several commercial websites I developed at the time. I made it, partly just because I could, but also because it served a commercial purpose.
Eventually one day I started using Ruby on Rails. There are aspects of the Rails framework which I like. However, at the time I was using it (starting with version 0.8), I found that it’s flat organisation of controllers and views very limiting. Nested controllers and views make it easier to manage complexity in a web application. Utopia embraces this principle, and applies it to both the controller and view layers. I also developed a model layer with similar principles.
So, Utopia exists because it suits my way of thinking about web applications, and it’s conceptual core has been refined for over a decade. It provides a considered amount of both flexibility, and opinionated behavior.
Is it production ready?
Yes. We’ve used Utopia since about 2010 in production.
Is it fast?
Yes. Trenni includes native Ragel parsers, and Utopia uses Concurrent::Map for multi-thread safe caches. On my laptop, Utopia can process 3000 requests/s rendering content on a single thread.
Installation
Make sure you have git, ruby, and bundler installed and set up correctly.
Install utopia:
$ gem install utopia
Create a new site:
$ mkdir www.example.com
$ cd www.example.com
$ utopia site create
$ rake
Usage
There is an excellent documentation wiki included with the source code. Simply clone this repository and rake documentation:server
. This documentation wiki is editable, so feel free to submit a PR with improvements.
Contributing
- Fork it
- Create your feature branch (
git checkout -b my-new-feature
) - Commit your changes (
git commit -am 'Add some feature'
) - Push to the branch (
git push origin my-new-feature
) - Create new Pull Request
See Also
- Trenni — Template and markup parsers, markup generation.
- Trenni::Formatters — Helpers for HTML generation including views and forms.
- Utopia::Gallery — A fast photo gallery based on libvips.
- Utopia::Analytics — Simple integration with Google Analytics.
- Rack::Freeze — Multi-thread safety in Rack.
- HTTP::Accept — RFC compliant header parser.
- Samovar — Command line parser used by Utopia.
- Mapping — Provide structured conversions for web interfaces.
- Rack::Test::Body — Provide convenient helpers for testing web interfaces.
Applications
- Financier — A small business management platform.
- mail.oriontransfer.net - Mail server account management.
License
Released under the MIT license.
Copyright, 2017, by Samuel G. D. Williams.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Design System
I led the design system effort for AVA, standardizing and optimizing the chatbot's conversational UI.
Principal Product Designer
About the Project
AVA is machine-learning chatbot that files claims and answers questions for more than 53k customers/month. The web app faced a crucial period of growth after its initial release, but faced a myriad of design problems. The MVP design was inconsistent and unpleasant to the new influx of users, causing problems in the app's performance and reputation.
I joined the team in 2018 as a principal product designer and led the design system effort. You can learn more about my work on AVA here.
Major Outcomes
Released the chatbot's first content guide, defining AVA's voice and personality
Documented 100% of the UI components and patterns in AVA's MVP design
Released the first iteration of a design system for the AVA product teams
Standardized and optimized AVA's design components to provide a better user experience
Content Guide
Users interact with AVA in a conversational format, making voice and personality a crucial part of AVA's design.
However, there was no consistent source of truth or reference regarding how AVA spoke to consumers. AVA's MVP voice was inconsistent and overly formal, using dated and stiff phrases ("pardon me" or "how may I serve you").
I collaborated with a senior content strategist to visualize, define, and organize a more modern Content Guide that would become part of a larger design system. The guide was released to the entire team, and is used by product managers, designers, and developers to rework old conversational flows and define new ones.
Design System Foundations
Design systems were a new concept to the AVA team, meaning it required education and collaboration to move the effort forward.
Having worked on two design systems before, I led the effort to architect the design system and determine how it would impact AVA's design process and codebase.
Our first step was to document what existed in AVA's UI.
AVA's design files, design versioning, and front-end code were inconsistent; the design files were often missing crucial assets. This lack of documentation made it difficult for the team to attempt to make AVA's design language consistent.
The design team documented 100% of the UI components, foundational components (such as grid and icons), and patterns for AVA. The UI for each component was re-created in a master Sketch file, with rules for layout and styling, recommendations on component usage, and defining different states of each component.
This initial version of the design system facilitated team discussions about how differently they had viewed or used the same components.
Redesigning for Consistency
After documentation, my next step is to optimize the most problematic components and patterns to the user and monitor their impact.
Next Steps
From a design perspective, the next steps for the team are to iteratively make the app meet AA accessibility standards, improve our CX metrics, and meet industry standards around design. From an engineering perspective, our goal is to begin abstracting parts of the UI into a maintainable pattern library.


Blueprint Seller Luka:
Ah, here it is. There is more information on the parchment, if you're interested. I am going to bed -- I haven't slept in days...


{% include head.html %}
{% include landing.html %}
{{page.title}}
CHECK OUT: ${{page.price}}
{{page.content | strip_html }}
{% include product-image-only.html %}
{% include offer.html %} {% include footer.html %} {% include plugins/livereload.html %} {% include plugins/google-track.html%}


Lesson
Warm-up: One Step Equations
Read
Polynomial Functions
Recall that a monomial is a number, a variable, or the product of a number and one or more variables with whole number exponents. A polynomial is a monomial or a sum of monomials. A polynomial function is a function of the form where , the exponents are all whole numbers, and the coefficients are all real numbers. For this function, an is the leading coefficient, n is the degree, and a0 is the constant term. A polynomial function is in standard form when its terms are written in descending order of exponents from left to right. You are already familiar with some types of polynomial functions, such as linear and quadratic. Here is a summary of common types of polynomial functions.
Core Concepts
| Degree | Leading Coefficient | End Behavior |
|---|---|---|
| Odd | Positive | Down-Up |
| Odd | Negative | Up-Down |
| Even | Positive | Up-Up |
| Even | Negative | Down-Down |
Watch
Example Videos
Do
Think-Pair-Share
Work independently on these questions and then share with your partner(s). Be prepared to discuss as a class.
Decide whether the function is a polynomial function. If so, write it in standard form and state its degree, type, and leading coefficient:
Evaluate the function for the given value of x
Describe the end behavior of the graph of the function
Sketch a graph of the polynomial function f having the given chareteristics.
Use the graph to describe the degree the leading coefficient and the end behavior of the function f .
Closure
What do we know about the graph of without actually graphing the function?
Practice: IXL Skills
- See IXL Learning Skill Plan - Complete all IXL Skills for Lesson 4.1 with a SmartScore of 70 or higher.
- DeltaMath - 4.1 Graphing Polynomial Functions
- End Behavior Graphically
- End Behavior Algebraically
- Determine Features of Polynomial Graph
Assessment - Demonstrate Mastery
- Take Practice Quiz 4.1
- Do not take the acutal quiz until you have scored 70 or higher on Practice Quiz.
- Take Schoology Quiz 4.1
Language Objective: SWBAT justify their conclusions, communicate them to others,
and respond to the arguments of others using the core vocabulary terms of this lesson.
Core Vocabulary
- polynomial
- polynomial function
- end behavior
Prior Vocabulary
- monomial
- linear function
- quadratic function
- leading coefficient
- degree
- constant term
- standard form


Welcome to the Metalworks Help files. These are actually HTML files which you are viewing with the Swing JEditorPane component. Pretty cool, eh? Were this an actual program, these files would contain helpful tips and instructions. Since this is not a real program, but only a demo, there is a random smattering of information contained within. This is simply indended to show what is possible. Click on the links below for more information.


HTML is a hypertext
Markup Language. The basic language of HTML is ASCII Code.The start tag and end tag
This is only text oriented language


We can define a baselayer type of imageOverlay, so we will not be interacting with map tiles, but a single image, which can be panned and zoomed inside the leaflet map container.
You can see a standalone example of this code here.


| [18] | Tapajit Dey, Willem Jiang, and Brian Fitzgerald. Knights and gold stars: A tale of innersource incentivization. IEEE Software, pages 2--12, 2022. [ bib | DOI ] |
| [17] | Ahmed Samir Imam Mahmoud, Tapajit Dey, Alexander Nolte, Audris Mockus, and James D Herbsleb. One-off events? an empirical study of hackathon code creation and reuse. Empirical Software Engineering, 27(7):1--49, 2022. [ bib | Print (Open Access)] |
| [16] | Tapajit Dey, Andrey Karnauch, and Audris Mockus. Representation of developer expertise in open source software. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), pages 995--1007, 2021. [ bib | DOI | Pre-Print Slides | Talk Video ] |
| [15] | Ahmed Imam, Tapajit Dey, Alexander Nolte, Audris Mockus, and James D. Herbsleb. The secret life of hackathon code where does it come from and where does it go? In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), pages 68--79, 2021. [ bib | DOI | Pre-Print] |
| [14] | Ahmed Imam and Tapajit Dey. Tracking hackathon code creation and reuse. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), pages 615--617, 2021. [ bib | DOI | Pre-Print] |
| [13] | Yuxing Ma, Tapajit Dey, Chris Bogart, Sadika Amreen, Marat Valiev, Adam Tutko, David Kennard, Russell Zaretzki, and Audris Mockus. World of code: enabling a research workflow for mining and analyzing the universe of open source vcs data. Empirical Software Engineering, 26(2):1--42, 2021. [ bib | https://doi.org/10.1007/s10664-020-09905-9 | Pre-Print] |
| [12] | Tapajit Dey and Audris Mockus. Effect of technical and social factors on pull request quality for the npm ecosystem. In Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), ESEM '20, New York, NY, USA, 2020. Association for Computing Machinery. [ bib | https://doi.org/10.1145/3382494.3410685 | Pre-Print | Slides | Talk Video ] |
| [11] | Tapajit Dey, Sara Mousavi, Eduardo Ponce, Tanner Fry, Bogdan Vasilescu, Anna Filippova, and Audris Mockus. Detecting and characterizing bots that commit code. In Proceedings of the 17th International Conference on Mining Software Repositories, MSR '20, page 209–219, New York, NY, USA, 2020. Association for Computing Machinery. [ bib | https://doi.org/10.1145/3379597.3387478 | Pre-Print | Slides | Talk Video ] |
| [10] | Tapajit Dey, Bogdan Vasilescu, and Audris Mockus. An exploratory study of bot commits. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW'20, page 61–65, New York, NY, USA, 2020. Association for Computing Machinery. [ bib | https://doi.org/10.1145/3387940.3391502 | Pre-Print | Slides | Talk Video ] |
| [9] | Tanner Fry, Tapajit Dey, Andrey Karnauch, and Audris Mockus. A dataset and an approach for identity resolution of 38 million author ids extracted from 2b git commits. In Proceedings of the 17th International Conference on Mining Software Repositories, MSR '20, page 518–522, New York, NY, USA, 2020. Association for Computing Machinery. [ bib | https://doi.org/10.1145/3379597.3387500 | Pre-Print ] |
| [8] | Andrey Krutauz, Tapajit Dey, Peter C. Rigby, and Audris Mockus. Do code review measures explain the incidence of post-release defects? Empirical Software Engineering, 25(5):3323--3356, Sep 2020. [ bib | https://doi.org/10.1007/s10664-020-09837-4 | Pre-Print] |
| [7] | Tapajit Dey and Audris Mockus. Deriving a usage-independent software quality metric. Empirical Software Engineering, 25(2):1596--1641, Mar 2020. [ bib | https://doi.org/10.1007/s10664-019-09791-w | Pre-Print] |
| [6] | Tapajit Dey, Yuxing Ma, and Audris Mockus. Patterns of effort contribution and demand and user classification based on participation patterns in npm ecosystem. In Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE'19, page 36–45, New York, NY, USA, 2019. Association for Computing Machinery. [ bib | https://doi.org/10.1145/3345629.3345634 | Pre-Print| Slides | ] |
| [5] | Sadika Amreen, Bogdan Bichescu, Randy Bradley, Tapajit Dey, Yuxing Ma, Audris Mockus, Sara Mousavi, and Russell Zaretzki. A Methodology for Measuring FLOSS Ecosystems, pages 1--29. Springer Singapore, Singapore, 2019. [ bib | https://doi.org/10.1007/978-981-13-7099-1_1 ] |
| [4] | Tapajit Dey and Audris Mockus. Modeling relationship between post-release faults and usage in mobile software. In Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE'18, page 56–65, New York, NY, USA, 2018. Association for Computing Machinery. [ bib | https://doi.org/10.1145/3273934.3273941 | Pre-Print| Slides | ] |
| [3] | Tapajit Dey and Audris Mockus. Are software dependency supply chain metrics useful in predicting change of popularity of npm packages? In Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE'18, page 66–69, New York, NY, USA, 2018. Association for Computing Machinery. [ bib | https://doi.org/10.1145/3273934.3273942 | Pre-Print| Slides | ] |
| [2] | Tapajit Dey, Jacob Logan Massengill, and Audris Mockus. Analysis of popularity of game mods: A case study. In Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts, CHI PLAY Companion '16, page 133–139, New York, NY, USA, 2016. Association for Computing Machinery. [ bib | https://doi.org/10.1145/2968120.2987724 | Pre-Print] |
| [1] | Yuxing Ma, Tapajit Dey, and Audris Mockus. Modularizing global variable in climate simulation software: Position paper. In Proceedings of the International Workshop on Software Engineering for Science, SE4Science '16, page 8–11, New York, NY, USA, 2016. Association for Computing Machinery. [ bib | https://doi.org/10.1145/2897676.2897681 | Pre-Print] |


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