Sample setrun.py module for AMRClaw¶
This sample setrun.py module is for two-dimensional acoustics, from the example in $CLAW/amrclaw/examples/acoustics_2d_radial.
"""
Module to set up run time parameters for Clawpack.
The values set in the function setrun are then written out to data files
that will be read in by the Fortran code.
"""
import os
import numpy as np
#------------------------------
def setrun(claw_pkg='amrclaw'):
#------------------------------
"""
Define the parameters used for running Clawpack.
INPUT:
claw_pkg expected to be "amrclaw" for this setrun.
OUTPUT:
rundata - object of class ClawRunData
"""
from clawpack.clawutil import clawdata
assert claw_pkg.lower() == 'amrclaw', "Expected claw_pkg = 'amrclaw'"
num_dim = 2
rundata = clawdata.ClawRunData(claw_pkg, num_dim)
#------------------------------------------------------------------
# Problem-specific parameters to be written to setprob.data:
#------------------------------------------------------------------
probdata = rundata.new_UserData(name='probdata',fname='setprob.data')
probdata.add_param('rho', 1., 'density of medium')
probdata.add_param('bulk', 4., 'bulk modulus')
#------------------------------------------------------------------
# Standard Clawpack parameters to be written to claw.data:
# (or to amrclaw.data for AMR)
#------------------------------------------------------------------
clawdata = rundata.clawdata # initialized when rundata instantiated
# Set single grid parameters first.
# See below for AMR parameters.
# ---------------
# Spatial domain:
# ---------------
# Number of space dimensions:
clawdata.num_dim = num_dim
# Lower and upper edge of computational domain:
clawdata.lower[0] = -1.000000e+00 # xlower
clawdata.upper[0] = 1.000000e+00 # xupper
clawdata.lower[1] = -1.000000e+00 # ylower
clawdata.upper[1] = 1.000000e+00 # yupper
# Number of grid cells:
clawdata.num_cells[0] = 50 # mx
clawdata.num_cells[1] = 50 # my
# ---------------
# Size of system:
# ---------------
# Number of equations in the system:
clawdata.num_eqn = 3
# Number of auxiliary variables in the aux array (initialized in setaux)
clawdata.num_aux = 0
# Index of aux array corresponding to capacity function, if there is one:
clawdata.capa_index = 0
# -------------
# Initial time:
# -------------
clawdata.t0 = 0.000000
# Restart from checkpoint file of a previous run?
# Note: If restarting, you must also change the Makefile to set:
# RESTART = True
# If restarting, t0 above should be from original run, and the
# restart_file 'fort.chkNNNNN' specified below should be in
# the OUTDIR indicated in Makefile.
clawdata.restart = False # True to restart from prior results
clawdata.restart_file = 'fort.chk00006' # File to use for restart data
# -------------
# Output times:
#--------------
# Specify at what times the results should be written to fort.q files.
# Note that the time integration stops after the final output time.
clawdata.output_style = 1
if clawdata.output_style==1:
# Output ntimes frames at equally spaced times up to tfinal:
# Can specify num_output_times = 0 for no output
clawdata.num_output_times = 20
clawdata.tfinal = 1.0
clawdata.output_t0 = True # output at initial (or restart) time?
elif clawdata.output_style == 2:
# Specify a list or numpy array of output times:
# Include t0 if you want output at the initial time.
clawdata.output_times = [0., 0.1]
elif clawdata.output_style == 3:
# Output every step_interval timesteps over total_steps timesteps:
clawdata.output_step_interval = 2
clawdata.total_steps = 4
clawdata.output_t0 = True # output at initial (or restart) time?
clawdata.output_format = 'ascii' # 'ascii', 'binary', 'netcdf'
clawdata.output_q_components = 'all' # could be list such as [True,True]
clawdata.output_aux_components = 'none' # could be list
clawdata.output_aux_onlyonce = True # output aux arrays only at t0
# ---------------------------------------------------
# Verbosity of messages to screen during integration:
# ---------------------------------------------------
# The current t, dt, and cfl will be printed every time step
# at AMR levels <= verbosity. Set verbosity = 0 for no printing.
# (E.g. verbosity == 2 means print only on levels 1 and 2.)
clawdata.verbosity = 0
# --------------
# Time stepping:
# --------------
# if dt_variable==True: variable time steps used based on cfl_desired,
# if dt_variable==False: fixed time steps dt = dt_initial always used.
clawdata.dt_variable = True
# Initial time step for variable dt.
# (If dt_variable==0 then dt=dt_initial for all steps)
clawdata.dt_initial = 1.00000e-02
# Max time step to be allowed if variable dt used:
clawdata.dt_max = 1.000000e+99
# Desired Courant number if variable dt used
clawdata.cfl_desired = 0.900000
# max Courant number to allow without retaking step with a smaller dt:
clawdata.cfl_max = 1.000000
# Maximum number of time steps to allow between output times:
clawdata.steps_max = 50000
# ------------------
# Method to be used:
# ------------------
# Order of accuracy: 1 => Godunov, 2 => Lax-Wendroff plus limiters
clawdata.order = 2
# Use dimensional splitting? (not yet available for AMR)
clawdata.dimensional_split = 'unsplit'
# For unsplit method, transverse_waves can be
# 0 or 'none' ==> donor cell (only normal solver used)
# 1 or 'increment' ==> corner transport of waves
# 2 or 'all' ==> corner transport of 2nd order corrections too
clawdata.transverse_waves = 2
# Number of waves in the Riemann solution:
clawdata.num_waves = 2
# List of limiters to use for each wave family:
# Required: len(limiter) == num_waves
# Some options:
# 0 or 'none' ==> no limiter (Lax-Wendroff)
# 1 or 'minmod' ==> minmod
# 2 or 'superbee' ==> superbee
# 3 or 'mc' ==> MC limiter
# 4 or 'vanleer' ==> van Leer
clawdata.limiter = ['mc','mc']
clawdata.use_fwaves = False # True ==> use f-wave version of algorithms
# Source terms splitting:
# src_split == 0 or 'none' ==> no source term (src routine never called)
# src_split == 1 or 'godunov' ==> Godunov (1st order) splitting used,
# src_split == 2 or 'strang' ==> Strang (2nd order) splitting used, not recommended.
clawdata.source_split = 0
# --------------------
# Boundary conditions:
# --------------------
# Number of ghost cells (usually 2)
clawdata.num_ghost = 2
# Choice of BCs at xlower and xupper:
# 0 or 'user' => user specified (must modify bcNamr.f to use this option)
# 1 or 'extrap' => extrapolation (non-reflecting outflow)
# 2 or 'periodic' => periodic (must specify this at both boundaries)
# 3 or 'wall' => solid wall for systems where q(2) is normal velocity
clawdata.bc_lower[0] = 'extrap' # at xlower
clawdata.bc_upper[0] = 'extrap' # at xupper
clawdata.bc_lower[1] = 'extrap' # at ylower
clawdata.bc_upper[1] = 'extrap' # at yupper
# ---------------
# Gauges:
# ---------------
rundata.gaugedata.gauges = []
# for gauges append lines of the form [gaugeno, x, y, t1, t2]
rundata.gaugedata.gauges.append([0, 0.0, 0.0, 0., 10.])
rundata.gaugedata.gauges.append([1, 0.7, 0.0, 0., 10.])
rundata.gaugedata.gauges.append([2, 0.7/np.sqrt(2.), 0.7/np.sqrt(2.), 0., 10.])
# --------------
# Checkpointing:
# --------------
# Specify when checkpoint files should be created that can be
# used to restart a computation.
clawdata.checkpt_style = 1
if clawdata.checkpt_style == 0:
# Do not checkpoint at all
pass
elif clawdata.checkpt_style == 1:
# Checkpoint only at tfinal.
pass
elif clawdata.checkpt_style == 2:
# Specify a list of checkpoint times.
clawdata.checkpt_times = [0.1,0.15]
elif clawdata.checkpt_style == 3:
# Checkpoint every checkpt_interval timesteps (on Level 1)
# and at the final time.
clawdata.checkpt_interval = 5
# ---------------
# AMR parameters:
# ---------------
amrdata = rundata.amrdata
# max number of refinement levels:
amrdata.amr_levels_max = 3
# List of refinement ratios at each level (length at least amr_level_max-1)
amrdata.refinement_ratios_x = [2, 2]
amrdata.refinement_ratios_y = [2, 2]
amrdata.refinement_ratios_t = [2, 2]
# Specify type of each aux variable in clawdata.auxtype.
# This must be a list of length num_aux, each element of which is one of:
# 'center', 'capacity', 'xleft', or 'yleft' (see documentation).
amrdata.aux_type = []
# Flag for refinement based on Richardson error estimater:
amrdata.flag_richardson = False # use Richardson?
amrdata.flag_richardson_tol = 0.001000e+00 # Richardson tolerance
# Flag for refinement using routine flag2refine:
amrdata.flag2refine = True # use this?
amrdata.flag2refine_tol = 0.2 # tolerance used in this routine
# User can modify flag2refine to change the criterion for flagging.
# Default: check maximum absolute difference of first component of q
# between a cell and each of its neighbors.
# steps to take on each level L between regriddings of level L+1:
amrdata.regrid_interval = 2
# width of buffer zone around flagged points:
# (typically the same as regrid_interval so waves don't escape):
amrdata.regrid_buffer_width = 2
# clustering alg. cutoff for (# flagged pts) / (total # of cells refined)
# (closer to 1.0 => more small grids may be needed to cover flagged cells)
amrdata.clustering_cutoff = 0.7
# print info about each regridding up to this level:
amrdata.verbosity_regrid = 0
# ---------------
# Regions:
# ---------------
rundata.regiondata.regions = []
# to specify regions of refinement append lines of the form
# [minlevel,maxlevel,t1,t2,x1,x2,y1,y2]
# ----- For developers -----
# Toggle debugging print statements:
amrdata.dprint = False # print domain flags
amrdata.eprint = False # print err est flags
amrdata.edebug = False # even more err est flags
amrdata.gprint = False # grid bisection/clustering
amrdata.nprint = False # proper nesting output
amrdata.pprint = False # proj. of tagged points
amrdata.rprint = False # print regridding summary
amrdata.sprint = False # space/memory output
amrdata.tprint = False # time step reporting each level
amrdata.uprint = False # update/upbnd reporting
return rundata
# end of function setrun
# ----------------------
if __name__ == '__main__':
# Set up run-time parameters and write all data files.
import sys
rundata = setrun(*sys.argv[1:])
rundata.write()


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Special Projects
This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.
MEAGAN HALL
09.07.17
Matt Starkey | FILE
As you scan through this week’s paper, you will notice a certain theme. Post staffers have been planning for a few months to make our first special edition of the 2017-18 academic year all about Ohio University’s new president, Duane Nellis.
Though Nellis officially started his term as president June 12, and The Post provided coverage of his first day and his speech at the Class of 2021 Convocation in August, we decided an entire edition dedicated to Nellis’ plans as president and who he is as a person would be a great way to begin the academic year.
Throughout the past couple of months, The Post staff had the opportunity to talk with Nellis, ask questions and learn more his past and his plans for the future. We asked him about his college days, his off-campus housing and how he will make himself available to students. To add additional context, one reporter interviewed former interim President David Descutner, who now serves as interim executive vice president and provost, to talk about his new role helping Nellis.
And just because we are putting out a special issue now does not mean our coverage about President Nellis and his administration will slow down anytime soon.
Creating a special edition is an exciting time for Post staffers. It gives the different sections within The Post a chance to work closely together and learn from one another.
Special editions are not a new concept to The Post. Every year, staffers start making plans weeks and months ahead of time so The Post can produce themed packages, like last year’s edition dedicated to the opioid epidemic and another edition that included 11 articles about different elements that make Athens wonderful.
As per usual, we’ll put out a Homecoming edition later this semester, and we have a few other ideas in the works for the rest of the academic year. If you are a student and feel as passionate about telling stories as we do, drop by our newsroom in Baker 325 and talk to us about joining The Post.
If you are a reader and feel strongly about special editions or topics that The Post could be covering more in-depth, send me an email and we can get a conversation started.
– Elizabeth Backo is a senior studying journalism and the editor-in-chief of The Post. Do you have suggestions for future special editions of The Post? Email her at<EMAIL_ADDRESS>or send her a tweet at @liz_backo.
Landing Page
This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.


The column values of existing rows can be changed using the UPDATE command. For example, the following sequence of commands shows the before and after results of giving everyone who is a manager a 10% raise:
SELECT ename, sal FROM emp WHERE job = 'MANAGER'; ename | sal -------+--------- JONES | 2975.00 BLAKE | 2850.00 CLARK | 2450.00 (3 rows) UPDATE emp SET sal = sal * 1.1 WHERE job = 'MANAGER'; SELECT ename, sal FROM emp WHERE job = 'MANAGER'; ename | sal -------+--------- JONES | 3272.50 BLAKE | 3135.00 CLARK | 2695.00 (3 rows)


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--- layout: page header-img: "img/red.jpg" description: "Welcome to another world" permalink: 404.html ---
Oops, this world is disintegrating...


When starting a new Berkeley DB project, much of the code that you must write is dedicated to defining the BDB environment: what databases it contains, the types of the databases, and so forth. Also, since records in BDB are just byte arrays, you must write code that assembles and interprets these byte arrays.
Much of this code can be written automatically (in C) by the db_sql_codegen utility. To use it, you first specify the schema of your Berkeley DB environment in SQL Data Definition Language (DDL). Then you invoke the db_sql_codegen command, giving the DDL as input. db_sql_codegen reads the DDL, and writes C code that implements a storage-layer API suggested by the DDL.
The generated API includes a general-purpose initialization function, which sets up the environment and the databases (creating them if they don't already exist). It also includes C structure declarations for each record type, and numerous specialized functions for storing and retrieving those records.
db_sql_codegen can also produce a simple test program that exercises the generated API. This program is useful as an example of how to use the API. It contains calls to all of the interface functions, along with commentary explaining what the code is doing.
Once the storage layer API is produced, your application may use it as is, or you may customize it as much as you like by editing the generated source code. Be warned, however: db_sql_codegen is a one-way process; there is no way to automatically incorporate customizations into newly generated code, if you decide to run db_sql_codegen again.
To learn more about db_sql_codegen, please consult the db_sql_codegen utility manual page in the Berkeley DB C API Reference Guide.


What does “Changing Thoughts” mean?
- First you identify thoughts you had just before feeling bad
- Then you work on changing that thought to something that is more helpful
How can “Changing Thoughts” help?
- Changing your thoughts about tinnitus can help you change the way you feel
When can I use “Changing Thoughts”?
-
Any time you feel tension or stress when you think about your tinnitus
Thoughts Affect Feelings
What you think affects how you feel.  The thoughts you have about your guests being late will affect how you feel (your emotions). If you think “it’s rude to be late,” then you might get angry. If you think “it’s nice to have extra time to clean up,” then you may feel relieved. If you think “they could have been in an accident,” then you might become worried. In this example, the event is the same, but your thoughts about the event are different. Sometimes the way you feel is caused by the thoughts about the event, not the event itself.
What thoughts do you have about your tinnitus? When you think about tinnitus, how do you feel?
Feelings Affect Health
What you think affects how you feel, and your feelings affect your health. Stress and negative emotions can lead to many health problems. In times of stress our brains release hormones into our bodies. These hormones increase heart rate, blood pressure, and muscle tension. The changes caused by these hormones help us react to emergencies. However, if our brains release these hormones too often it is bad for our health. People who are stressed for a long time may be more likely to get colds, heart disease, and other health problems. This is why it is so important to learn how to change your thoughts that cause negative feelings.
CORRECTING THOUGHT ERRORS
So how can you control negative feelings? Your thoughts determine the feelings you experience. You may not be able to change events, or tinnitus. However, the way you think about an event is under your control. Change your thoughts, and your feelings will change too. Below is a description for how to change your thoughts, step-by-step.
Changing Thoughts – Step-by-Step:
Step 1: Describe Situation. Identify what was going on when you started feeling bad—what happened? Sometimes it is hard to remember the situation that made you feel bad until later. If this is the case for you, go to the second step and come back to this step later.
Step 2: Describe Thoughts. Now try to write down a thought you had just before you started feeling bad or upset. What was the first thought that came into your mind? You may have had many thoughts just before you started feeling bad. If you had more than one thought, pick the one that made you feel the worst.
Step 3: List Feelings. Write down any bad or upsetting feelings you are having. For example, sad, angry, jealous, or disappointed.
Step 4: Identify Thought Errors: Our thoughts often have some truth to them, but some have many more errors. Sometimes people get in the habit of having thoughts that are not helpful. Thoughts that are not helpful or unhealthy are called “thought errors.” You may feel bad or get upset out of habit and find out later that the reason you got upset was your own “thought error.”
All people make thought errors from time to time. Many people make thought errors that cause them to feel sad or upset. If you are aware of the most common thought errors, you can catch yourself and correct your thinking. Read the list of thought errors below. Think about which ones are familiar to you.
Ten Common Thought Errors:
- All-or-nothing thinking: When you see things in only two categories such as right or wrong.
- Over-simplifying: When you see one bad event as a pattern that never changes.
- Focusing on wrong details: When you pick out a single detail and focus on it. You don’t think about other more positive details.
- Jumping to conclusions: When you come to believe something is true without enough information to know if it really is true. You might assume that you know what someone else is thinking or assume things will turn out badly.
- Assuming the worst: When you think something is much worse than it really is.
- Emotional thoughts: When you think that your emotions show the way things really are. You might think, “I feel it, so it must be true.”
- “Should” statements: When you say “should” and “shouldn’t” to try to get yourself to do hard tasks. These statements tend to make you feel guilty. Also included are statements with the words “must” and “ought.”
- Labeling: Attaching a bad label to yourself or others.
- Making Things Personal: You see yourself as the cause of some negative event when you are not responsible. You ignore other details.
- Blaming: You blame others for your problems. You may also blame yourself for other people’s problems.
Can you identify any of the 10 thought errors from the list? You can have more than one thought error in one thought.
Step 6: New positive thought. Write down a new thought about the event that is more helpful. This step requires a lot of practice. With practice it will become more natural to create new positive thoughts. Sometimes it helps to say statements that apply to many things. For example, “I am whole and complete,” or “I love and accept myself.”
New positive thoughts should be:
- brief
- easy to remember
- thoughts you believe are true
- thoughts that apply to your life
- helpful
Step 7: Feelings when you think the new thought. As you practice, pay attention to how you feel when you have positive thoughts instead of negative thoughts. Do you notice your tinnitus as much? Are your muscles relaxed?
Step 8: Picture yourself in the future. Look at the negative thought from Step 2 again. Think of a time in the future when you might have that thought again. Picture yourself thinking the positive thought from Step 6 instead.


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2nd,3rd floor, Lolge corner, Above Apple Salon, Near ICICI Bank, Karve Nagar, Pune -<PHONE_NUMBER>050535 /<PHONE_NUMBER>


FLIPPER 1996 A film review by Timothy Voon Copyright 1997 Timothy Voon
Cast: Paul Hogan, Elijah Wood, Chelsea Field, Jonathan Banks, Isaac Hayes, Jessica Wesson, Jason Fuchs Director: Alan Shapiro Screenplay: Alan Shapiro
"I've lost all of my baby fat, but I'm still cute, so love me anyway.' It must be hard for any child star (Wood) to enter that difficult period of our lives known as adolescence. The teenage years are hard enough as it is without the unnecessary attentions of fame and fortune, too easily endowed on anyone who can help ‘Hollywood' earn a quick buck.
Turn on 'Water Music' meditate on dolphins, and watch your blood pressure drop to safe limits. Out with the anti-hypertensive medication, and in with 'Flipper'. This dolphin befriends a lonely, angry young boy, detects bins of chemical wastes underwater, and cons children out of free fish and pocket money. Now where can I find myself a pet fish like this? (Ooops, I mean mammal).
Where we live the sun always shines, the surf is always blue, the natives are always friendly, and Uncle Porter's (Hogan) speciality is 'toast ala welding torch'. Ahhh, sounds like a good place to retire. Fresh air, the sound of the sea, endless summer, and don't forget the sun tan lotion.
So come join 'Flipper' in yet another adventure where he battles the bad guys of the sea. The environmentally unfriendly fisherman who shoots dolphins and poisons the ocean floors; the giant hammerhead shark who has an acquired taste for seagulls and little boys. At the end of the day I didn't really enjoy the movie, but it did remind me of a much needed holiday.
Comment: Not 'Waterworld', but certainly more intelligent. Feel Scale: 0% Goldfish / *DOLPHIN* / White Shark 100%
Timothy Voon e-mail<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


|
Processor Counter Monitor
|
Basic core counter state. More...
#include <cpucounters.h>
Public Member Functions | |
| BasicCounterState (const BasicCounterState &)=default | |
| BasicCounterState (BasicCounterState &&)=default | |
| BasicCounterState & | operator= (BasicCounterState &&)=default |
| BasicCounterState & | operator+= (const BasicCounterState &o) |
| void | readAndAggregate (std::shared_ptr< SafeMsrHandle >) |
| void | readAndAggregateTSC (std::shared_ptr< SafeMsrHandle >) |
| int32 | getThermalHeadroom () const |
| Returns current thermal headroom below TjMax. | |
Protected Member Functions | |
| uint64 * | getEventsPtr () |
| const uint64 * | getEventsPtr () const |
| uint64 & | Event (size_t i) |
| const uint64 & | Event (size_t i) const |
Friends | |
| class | PCM |
| class | JSONPrinter |
| template<class CounterStateType > | |
| double | getExecUsage (const CounterStateType &before, const CounterStateType &after) |
| Computes average number of retired instructions per time intervall. More... | |
| template<class CounterStateType > | |
| double | getIPC (const CounterStateType &before, const CounterStateType &after) |
| Computes average number of retired instructions per core cycle (IPC) More... | |
| template<class CounterStateType > | |
| double | getAverageFrequency (const CounterStateType &before, const CounterStateType &after) |
| Computes average core frequency also taking Intel Turbo Boost technology into account. More... | |
| template<class CounterStateType > | |
| double | getActiveAverageFrequency (const CounterStateType &before, const CounterStateType &after) |
| Computes average core frequency when not in powersaving C0-state (also taking Intel Turbo Boost technology into account) More... | |
| template<class CounterStateType > | |
| double | getRelativeFrequency (const CounterStateType &before, const CounterStateType &after) |
| Computes average core frequency also taking Intel Turbo Boost technology into account. More... | |
| template<class CounterStateType > | |
| double | getActiveRelativeFrequency (const CounterStateType &before, const CounterStateType &after) |
| Computes average core frequency when not in powersaving C0-state (also taking Intel Turbo Boost technology into account) More... | |
| template<class CounterStateType > | |
| double | getL2CacheHitRatio (const CounterStateType &before, const CounterStateType &after) |
| Computes L2 cache hit ratio. More... | |
| template<class CounterStateType > | |
| double | getL3CacheHitRatio (const CounterStateType &before, const CounterStateType &after) |
| Computes L3 cache hit ratio. More... | |
| template<class CounterStateType > | |
| uint64 | getL3CacheMisses (const CounterStateType &before, const CounterStateType &after) |
| Computes number of L3 cache misses. More... | |
| template<class CounterStateType > | |
| uint64 | getL2CacheMisses (const CounterStateType &before, const CounterStateType &after) |
| Computes number of L2 cache misses. More... | |
| template<class CounterStateType > | |
| uint64 | getL2CacheHits (const CounterStateType &before, const CounterStateType &after) |
| Computes number of L2 cache hits. More... | |
| template<class CounterStateType > | |
| uint64 | getL3CacheHitsNoSnoop (const CounterStateType &before, const CounterStateType &after) |
| Computes number of L3 cache hits where no snooping in sibling L2 caches had to be done. More... | |
| template<class CounterStateType > | |
| uint64 | getL3CacheHitsSnoop (const CounterStateType &before, const CounterStateType &after) |
| Computes number of L3 cache hits where snooping in sibling L2 caches had to be done. More... | |
| template<class CounterStateType > | |
| uint64 | getL3CacheHits (const CounterStateType &before, const CounterStateType &after) |
| Computes total number of L3 cache hits. More... | |
| template<class CounterStateType > | |
| uint64 | getL3CacheOccupancy (const CounterStateType &now) |
| Computes L3 Cache Occupancy. More... | |
| template<class CounterStateType > | |
| uint64 | getLocalMemoryBW (const CounterStateType &before, const CounterStateType &after) |
| Computes Local Memory Bandwidth. More... | |
| template<class CounterStateType > | |
| uint64 | getRemoteMemoryBW (const CounterStateType &before, const CounterStateType &after) |
| Computes Remote Memory Bandwidth. More... | |
| template<class CounterStateType > | |
| uint64 | getCycles (const CounterStateType &before, const CounterStateType &after) |
| Computes the number core clock cycles when signal on a specific core is running (not halted) More... | |
| template<class CounterStateType > | |
| uint64 | getInstructionsRetired (const CounterStateType &before, const CounterStateType &after) |
| Computes the number of retired instructions. More... | |
| template<class CounterStateType > | |
| uint64 | getCycles (const CounterStateType &now) |
| Computes the number executed core clock cycles. More... | |
| template<class CounterStateType > | |
| uint64 | getInstructionsRetired (const CounterStateType &now) |
| Computes the number of retired instructions. More... | |
| template<class CounterStateType > | |
| uint64 | getNumberOfCustomEvents (int32 eventCounterNr, const CounterStateType &before, const CounterStateType &after) |
| Returns the number of occured custom core events. More... | |
| template<class CounterStateType > | |
| uint64 | getInvariantTSC (const CounterStateType &before, const CounterStateType &after) |
| Computes number of invariant time stamp counter ticks. More... | |
| template<class CounterStateType > | |
| uint64 | getRefCycles (const CounterStateType &before, const CounterStateType &after) |
| Computes the number of reference clock cycles while clock signal on the core is running. More... | |
| template<class CounterStateType > | |
| double | getCoreCStateResidency (int state, const CounterStateType &before, const CounterStateType &after) |
| Computes residency in the core C-state. More... | |
| template<class CounterStateType > | |
| uint64 | getCoreCStateResidency (int state, const CounterStateType &now) |
| Reads raw residency counter for the core C-state. More... | |
| template<class CounterStateType > | |
| uint64 | getSMICount (const CounterStateType &before, const CounterStateType &after) |
| Returns the number of occured system management interrupts. More... | |
Basic core counter state.
Intended only for derivation, but not for the direct use
|
friend |
Computes average core frequency when not in powersaving C0-state (also taking Intel Turbo Boost technology into account)
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes average core frequency when not in powersaving C0-state (also taking Intel Turbo Boost technology into account)
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes average core frequency also taking Intel Turbo Boost technology into account.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes residency in the core C-state.
| state | C-state |
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Reads raw residency counter for the core C-state.
| state | C-state # |
| now | CPU counter state |
|
friend |
Computes the number core clock cycles when signal on a specific core is running (not halted)
Returns number of used cycles (halted cyles are not counted). The counter does not advance in the following conditions:
The performance counter for this event counts across performance state transitions using different core clock frequencies
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes the number executed core clock cycles.
Returns number of used cycles (halted cyles are not counted).
| now | Current CPU counter state |
|
friend |
Computes average number of retired instructions per time intervall.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes the number of retired instructions.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes the number of retired instructions.
| now | Current CPU counter state |
|
friend |
Computes number of invariant time stamp counter ticks.
This counter counts irrespectively of C-, P- or T-states
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes average number of retired instructions per core cycle (IPC)
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes L2 cache hit ratio.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes number of L2 cache hits.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes number of L2 cache misses.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes L3 cache hit ratio.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes total number of L3 cache hits.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes number of L3 cache hits where no snooping in sibling L2 caches had to be done.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes number of L3 cache hits where snooping in sibling L2 caches had to be done.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes number of L3 cache misses.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes L3 Cache Occupancy.
|
friend |
Computes Local Memory Bandwidth.
|
friend |
Returns the number of occured custom core events.
Read number of events programmed with the CUSTOM_CORE_EVENTS
| eventCounterNr | Event/counter number (value from 0 to 3) |
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes the number of reference clock cycles while clock signal on the core is running.
The reference clock operates at a fixed frequency, irrespective of core frequency changes due to performance state transitions. See Intel(r) Software Developer's Manual for more details
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes average core frequency also taking Intel Turbo Boost technology into account.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |
|
friend |
Computes Remote Memory Bandwidth.
|
friend |
Returns the number of occured system management interrupts.
| before | CPU counter state before the experiment |
| after | CPU counter state after the experiment |


adH0cc AstroNotes
To identify well suited Type IIP supernovae, adH0cc has to spectroscopically classify numerous transients. The outcomes of these classifications are reported in AstroNotes of the IAU Supernova Working Group. All adH0cc AstroNotes can be found here:


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Please remember that minimum donation with Helperbit is 0.001 bitcoin, please take it in mind when you top up your wallet.


Welcome to the Solar System!
Welcome to the Solar System!
The Solar System[a] is the gravitationally bound system comprising the Sun and the objects that orbit it, either directly or indirectly. Of those objects that orbit the Sun directly, the largest eight are the planets,[c] with the remainder being significantly smaller objects, such as dwarf planets and small Solar System bodies. Of the objects that orbit the Sun indirectly, the moons, two are larger than the smallest planet, Mercury.
For many thousands of years, humanity, with a few exceptions, did not recognize or understand the concept of the Solar System. Most people up to the Late Middle Ages–Renaissance believed Earth to be stationary at the centre of the universe and categorically different from the divine or ethereal objects that moved through the sky. Although the Greek philosopher Aristarchus of Samos had speculated on a heliocentric reordering of the cosmos, Nicolaus Copernicus was the first to develop a mathematically predictive heliocentric system.
In the 17th century, Galileo Galilei, Johannes Kepler, and Isaac Newton developed an understanding of physics that led to the gradual acceptance of the idea that Earth moves around the Sun and that the planets are governed by the same physical laws that governed Earth. The invention of the telescope led to the discovery of further planets and moons. Improvements in the telescope and the use of unmanned spacecraft have enabled the investigation of geological phenomena, such as mountains, craters, seasonal meteorological phenomena, such as clouds, dust storms and ice caps on the other planets.
The distance from Earth to the Sun is 1 astronomical unit (150,000,000 km), or AU. For comparison, the radius of the Sun is 0.0047 AU (700,000 km). Thus, the Sun occupies 0.00001% (10−5 %) of the volume of a sphere with a radius the size of Earth's orbit, whereas Earth's volume is roughly one millionth (10−6) that of the Sun. Jupiter, the largest planet, is 5.2 astronomical units (780,000,000 km) from the Sun and has a radius of 71,000 km (0.00047 AU), whereas the most distant planet, Neptune, is 30 AU (4.5x109 km) from the Sun.
The inner Solar System is the region comprising the terrestrial planets and the asteroid belt. Composed mainly of silicates and metals, the objects of the inner Solar System are relatively close to the Sun; the radius of this entire region is less than the distance between the orbits of Jupiter and Saturn. This region is also within the frost line, which is a little less than 5 AU (about 700 million km) from the Sun.


write a scene about: _________________
in the genre: _________________
movie subject generator created for teaching on the scripted design elective module in the situated design course at the master institute of visual cultures, akv st joost Copyright © 2019-20 ollie palmer Site distributed at github under an MIT license (use, adapt, modify as you like) :)


Base:
property
Debugger
System.Management.Automation.Runspaces.Runspace.Debugger
3 references to Debugger
System.Management.Automation (3)
engine\hostifaces\LocalConnection.cs (1)
1018
Debugger debugger =
Debugger
;
engine\hostifaces\LocalPipeline.cs (2)
850
!((LocalRunspace.
Debugger
!= null) && (LocalRunspace.
Debugger
.InBreakpoint)))


Yidgha Phonetic Keyman Keyboard
Introduction
This keyboard was developed for keyboarding the Yidgha language in the Arabic Script.


THC Files
Latest data reviews and more...
Latest data reviews and more...
Cannabutter is the essential ingredient for any weed bake. Don’t be intimidated, it is extremely easy to make. Here’s what you need to make basic cannabutter. Ingredients: A cup or less of water 1 lb. of butter, unsalted 1 oz. of high quality lightly ground weed bud or trim The basic process is to put your decarboxylated weed and butter in a pot with a little water just to make sure the butter doesn’t burn, and heat on a low simmer for 3 to 6 hours. Long as the butter doesn’t burn, the longer you heat, the more cannabinoids you get in the butter. Some prefer to heat in a crockpot to 6 hours. Let it cool enough to handle, strain out the plant material and discard (the thc is now inside the butter!), then put it in the fridge overnight for it to harden. Next, remove the hardened butter from the residual water and you have your main ingredient, your precious canna butter infused with powerful cannabinoids
Here’s what you’ll need: Light corn syrup, ¾ cup Cup of water Powdered sugar, 1/8 cup 2 cups of sugar Hash oil, (canna oil) Teaspoon of peppermint flavoring Green food coloring, 2 drops Equipment: Saucepan Scissors thermometer pan, greased with canna butter Weeds or star candy molds Lollipop sticks Baking dish


lib/casson.js:1
Casson is a Object Data Model with asynchronous CQL binary protocol library to interact with Cassandra.
Pool, Table and Select, Insert, and Update are the core set of classes for Casson.
Casson
KeySpace
Defined in lib/casson.js:59
lib/casson.js:59
P
Defined in lib/casson.js:23
lib/casson.js:23
Short alias for Pool
Pool
Defined in lib/casson.js:17
lib/casson.js:17
Q
Defined in lib/casson.js:51
lib/casson.js:51
Short alias for Query
Query
Defined in lib/casson.js:45
lib/casson.js:45
T
Defined in lib/casson.js:37
lib/casson.js:37
Short alias for Table
Table
Defined in lib/casson.js:31
lib/casson.js:31
Uuid
Defined in lib/casson.js:66
lib/casson.js:66


Your node must be HTTPS in order to connect to it via TrustCoinWallet.com. You can [download the TrustCoinWallet repo & run it locally](https://github.com/kvhnuke/etherwallet/releases/latest) to connect to your local node. Or, get free SSL certificate via [LetsEncrypt](https://letsencrypt.org/).
귀하의 주소는 귀하의 계정 번호 또는 귀하의 공개 키로도 알려져 있습니다. 그것은 사람들과 공유하여 그들이 당신에게 이더 나 토큰을 보낼 수 있습니다. 다채로운 주소 아이콘을 찾으십시오. 어딘가에 주소를 입력 할 때마다 종이 지갑과 일치하는지 확인하십시오.
Your Address:
This Keystore / JSON file matches the format used by Mist & Geth so you can easily import it in the future. It is the recommended file to download and back up.
This is the unencrypted text version of your private key, meaning no password is necessary. If someone were to find your unencrypted private key, they could access your wallet without a password. For this reason, encrypted versions are typically recommended.
Private Key (unencrypted)
ProTip: Click print and save this as a PDF, even if you do not own a printer!
You arrived via a link that has the address, amount, gas or data fields filled in for you. You can change any information before sending. Unlock your wallet to get started.
Unlock your wallet to send ETH or Tokens directly from this page.
Warning! You are not connected to an ETH node.
Please use the node switcher in the top-right corner to switch to an ETH node. We do not support swapping ETC or Testnet ETH.
This page allows you to download different versions of private keys and re-print your paper wallet. You may want to do this in order to [import your account into Geth/Mist](http://ethereum.stackexchange.com/questions/465/how-to-import-a-plain-private-key-into-geth/). If you want to check your balance, we recommend using a blockchain explorer like etherscan.io.
귀하의 주소는 귀하의 계정 번호 또는 귀하의 공개 키로도 알려져 있습니다. 그것은 사람들과 공유하여 그들이 당신에게 이더 나 토큰을 보낼 수 있습니다. 다채로운 주소 아이콘을 찾으십시오. 어딘가에 주소를 입력 할 때마다 종이 지갑과 일치하는지 확인하십시오.
Your Address:
This Keystore / JSON file matches the format used by Mist & Geth so you can easily import it in the future. It is the recommended file to download and back up.
This is the unencrypted text version of your private key, meaning no password is necessary. If someone were to find your unencrypted private key, they could access your wallet without a password. For this reason, encrypted versions are typically recommended.
Private Key (unencrypted)
ProTip: Click print and save this as a PDF, even if you do not own a printer!
**You need your Keystore File + password or Private Key** to access this wallet in the future. Please save & back it up externally! There is no way to recover a wallet if you do not save it. Read the [help page](https://www.TrustCoinWallet.com/#help) for instructions.
TrustCoinWallet.com은 귀하의 지갑파일과 비밀키를 보관하지 않습니다. 계정에 액세스하거나, 키를 복구하거나, 비밀번호를 재설정하거나, 트랜잭션을 되돌릴 수 없습니다. 열쇠를 보호하고 올바른 URL에 있는지 항상 확인하십시오.
귀하의 보안은 귀하의 책임입니다.
Always backup your keys: TrustCoinWallet.com & TrustCoinWallet CX are not "web wallets". You do not create an account or give us your funds to hold onto. No data leaves your computer / your browser. We make it easy for you to create, save, and access your information and interact with the blockchain.
We are not responsible for any loss: Ethereum, TrustCoinWallet.com & TrustCoinWallet CX, and some of the underlying Javascript libraries we use are under active development. While we have thoroughly tested & tens of thousands of wallets have been successfully created by people all over the globe, there is always the possibility something unexpected happens that causes your funds to be lost. Please do not invest more than you are willing to lose, and please be careful.
Translations of TrustCoinWallet: The community has done an amazing job translating TrustCoinWallet into a variety of languages. However, TrustCoinWallet can only verify the validity and accuracy of the information provided in English and, because of this, the English version of our website is the official text.
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.


Today we will work with Python packages for spatial raster analysis. Python has some dedicated packages to handle rasters:
Make a directory structure for this tutorial:
cd ~/Documents/
mkdir PythonRaster #or give the directory a name to your liking
cd ./PythonRaster
mkdir data
mkdir output
Like in the previous tutorials, we will create a conda environment
with a .yaml
file:
name: raster
dependencies:
- python
- numpy
- matplotlib
- spyder
- owslib
- gdal
- geopandas
- rasterio
- rasterstats
- affine
- osmnx
After creation, activate the environment, open Spyder, create a script in the root directory, and start coding.
A Web Coverage Service (WCS) loads raster data in a similar way as Web Feature Services (WFS) load vector data. Web Coverage Services are a standard by the Open Geospatial Consortium and allow the downloading of geospatial raster data with multiple types of format encoding: GeoTIFF, netCDF, JPEG2000 etc. A Web Map Service [WMS] also exists for rasters; it allows downloading of images but without the data values.
Today we will work with elevation rasters. More specifically, we will have a look at the WCS of the AHN dataset. AHN stands for “Actueel Hoogtebestand Nederland” and is a Digital Elevation Model [DEM] that covers the Netherlands. Access the web coverage service to have a look at the contents:
from owslib.wcs import WebCoverageService
# Access the WCS by proving the url and optional arguments
wcs = WebCoverageService('http://geodata.nationaalgeoregister.nl/ahn3/wcs?service=WCS', version='1.0.0')
# Print to check the contents of the WCS
print(list(wcs.contents))
Running the last line of code shows that the Web Coverage Service of
the AHN3 contains four rasters: 0.5m DSM, 0.5m DTM, 5m DSM, and 5m DTM.
Raster data of AHN has the projected coordinate system
RD_New (EPSG: 28992)
.
We can also check what types of operations are available for this WCS:
# Get all operations and print the name of each of them
print([op.name for op in wcs.operations])
You will see that the Web Coverage Service allows accessing the data (GetCoverage), the metadata (DescribeCoverage), and the capabilities (GetCapabilities). These are all standard protocols defined by the OGC.
Several functions are available to access specific metadata of each individual raster, for example:
# Take the 0.5m DSM as an example
cvg = wcs.contents['ahn3_05m_dsm']
# Print supported reference systems, the bounding box defined in WGS 84 coordinates, and supported file formats
print(cvg.supportedCRS)
print(cvg.boundingBoxWGS84)
print(cvg.supportedFormats)
Let us have a look at the data itself. As we do not want to overload the web service, we download once and store the data locally.
Download the Digital Surface Model [DSM], which is the the ‘ahn3_05m_dsm’ version, and Digital Terrain Model [DTM], which is the ‘ahn3_05m_dtm’ version, to a local file. The difference between a DEM, DSM and DTM is explained on the GIS StackExchange.
import os
# Define a bounding box in the available crs (see before) by picking a point and drawing a 1x1 km box around it
x, y = 174100, 444100
bbox = (x - 500, y - 500, x + 500, y + 500)
# Request the DSM data from the WCS
response = wcs.getCoverage(identifier='ahn3_05m_dsm', bbox=bbox, format='GEOTIFF_FLOAT32',
crs='urn:ogc:def:crs:EPSG::28992', resx=0.5, resy=0.5)
# Write the data to a local file in the 'data' directory
with open('data/AHN3_05m_DSM.tif', 'wb') as file:
file.write(response.read())
# Do the same for the DTM
response = wcs.getCoverage(identifier='ahn3_05m_dtm', bbox=bbox, format='GEOTIFF_FLOAT32',
crs='urn:ogc:def:crs:EPSG::28992', resx=0.5, resy=0.5)
with open('data/AHN3_05m_DTM.tif', 'wb') as file:
file.write(response.read())
Before continuing, please check if this step was successful.
GDAL handles raster and vector geospatial data formats with Python, Java, R and C APIs. When opening a raster file in gdal, the object has a hierarchical structure starting at the Dataset level. A Dataset has a Geotransform (metadata) and can contain one or more Bands. Each Band has a Data array and potentially Overviews.
Let us open the file we just saved. You will see you first get the dataset, and need to access the band (even though there is only one), before the data array can be accessed.
from osgeo import gdal
# Open dataset, gdal automatically selects the correct driver
ds = gdal.Open("data/AHN3_05m_DSM.tif" )
# Get the band (band number 1)
band = ds.GetRasterBand(1)
# Get the data array
data = band.ReadAsArray()
print(data)
# Delete objects to close the file
ds = None
Question 1: Why do we set ds to None at the end of your script? What may happen if you do not do that?
The GDAL Python API is not the best documented Python module. Therefore, Rasterio is explained as an alternative raster data handling module.
Rasterio
reads and writes multiple raster formats based on GDAL, provides raster
processing functions based on NumPy arrays and GeoJSON, and integrates
Matplotlib in the module rasterio.plot
for
visualization.
The rest of the tutorial below is a complete route of handling a raster dataset. We will use the DEMs from a the WCS for our study area, handle it with Rasterio, calculate new information (CHM), overlay it with vector data representing buildings and visualize it.
Let us read in the raster data we just stored from the WCS with
Rasterio and plot it with rasterio.plot
:
import rasterio
from rasterio.plot import show
import matplotlib.pyplot as plt
# Open the two rasters
dsm = rasterio.open("data/AHN3_05m_DSM.tif", driver="GTiff")
dtm = rasterio.open("data/AHN3_05m_DTM.tif", driver="GTiff")
# Metadata functions from Rasterio
print(dsm.meta)
print(dtm.meta)
# Plot with rasterio.plot, which provides Matplotlib functionality
plt.figure(figsize=(5, 5), dpi=300) # adjust size and resolution
show(dsm, title='Digital Surface Model', cmap='gist_ncar')
Question 2: Adjust the code above to take a look at the DTM. Note the gaps that appear. What are these gaps?
The metadata shows the driver (GDAL’s way of knowing how to function with a specific file format), datatype, nodata value, width of raster in number of cells, height of raster in number of cells, number of raster bands in the dataset, coordinate reference system, and transformation values.
In the back-end, raster layers in Rasterio are stored as NumPy
arrays, which appear when the data are read with the method
.read()
:
# Rasterio object
print(type(dsm))
# Read, show object type and data
dsm_data = dsm.read(1)
print(type(dsm_data))
print(dsm_data)
A Canopy Height Model (CHM) gives an indication of the height of trees and/or buildings. It can be created by subtracting a Digital Terrain Model from a Digital Surface Model. In the resulting raster, each cell value represents the height above the underlying surface topography.
import numpy as np
# Access the data from the two rasters
dsm_data = dsm.read()
dtm_data = dtm.read()
# Set our nodata to np.nan (this is important for later)
dsm_data[dsm_data == dsm.nodata] = np.nan
dtm_data[dtm_data == dtm.nodata] = np.nan
Earlier, we noticed that the DTM included gaps. Let’s first fill
these gaps using the fillnodata()
function from
rasterio.fill
. For more information, see the documentation.
from rasterio.fill import fillnodata
# Create a mask to specify which pixels to fill (0=fill, 1=do not fill)
dtm_mask = dtm_data.copy()
dtm_mask[~np.isnan(dtm_data)] = 1
dtm_mask[np.isnan(dtm_data)] = 0
# Fill missing values
dtm_data = fillnodata(dtm_data, mask=dtm_mask)
Now, let’s can create our CHM:
# Subtract the NumPy arrays
chm = dsm_data - dtm_data
# Check the resulting array
print(chm)
# Copy metadata of one of the rasters (does not matter which one)
kwargs = dsm.meta
# Save the chm as a raster
with rasterio.open('data/AHN3_05m_CHM.tif', 'w', **kwargs) as file:
file.write(chm.astype(rasterio.float32))
Question 3: Where is the CHM the highest in the study area? Is it what you expected?
We have now applied the basic concepts of creating a Canopy Height Model!
Using our CHM, let’s determine the average heights of the buildings
in our study area. The first step is to download building data from the
BAG Web Feature Service that we also used in the vector tutorial. Note
that we make use of the bbox
from an earlier codeblock for
this.
import geopandas as gpd
from requests import Request
# Extract only buildings on and around WUR campus
url = 'https://service.pdok.nl/lv/bag/wfs/v2_0'
layer = 'bag:pand' # see wfs.contents
bb = ','.join(map(str, bbox)) # string of bbox needed for the request url
# Specify the parameters for fetching the data
params = dict(service='WFS', version="1.1.0", request='GetFeature',
typeName=layer, outputFormat='json',
srsname='urn:ogc:def:crs:EPSG::28992', bbox=bb)
# Parse the URL with parameters
q = Request('GET', url, params=params).prepare().url
# Read data from URL
buildings_gdf = gpd.read_file(q)
Question 4: What happens in the line “bb = ‘,’.join(map(str, bbox))”? Look up how
map()
works in Python if you do not know.
The next step is to perform zonal statistics to get the average
height value per building polygon. We will do this with the module
Rasterstats, which can use a GeoDataFrame
and a
.tif
file for this task. Here, we make it output a GeoJSON.
import rasterstats as rs
# Apply the zonal statistics function with gdf and tif as input
chm_buildings = rs.zonal_stats(buildings_gdf, "data/AHN3_05m_CHM.tif", prefix='CHM_', geojson_out=True)
# Convert GeoJSON to GeoDataFrame
buildings_gdf = gpd.GeoDataFrame.from_features(chm_buildings)
# Check the added attributes with a prefix 'CHM_'
print(buildings_gdf['CHM_mean'])
A quick visualization shows us the heights derived from the raster data on the map:
# Create one plot with figure size 10 by 10
fig, ax = plt.subplots(1, figsize=(10, 10))
# Customize figure with title, legend, and facecolour
ax.set_title('Heights above ground (m) of buildings on the WUR campus')
buildings_gdf.plot(ax=ax, column='CHM_mean', k=6,
cmap=plt.cm.viridis, linewidth=1, edgecolor='black', legend=True)
ax.set_facecolor("lightgray")
# Make sure to get an equal scale in the x and y direction
plt.axis('equal')
# Visualize figure
plt.show()
Question 5: Why do we want an equal scale in the x and y direction for this figure?
Note that this tutorial only scratches the surface of the
possibilities of Rasterio. It can do most if not all things you did in
R
in the Vector - Raster tutorial. Rasterio for example
also allows you to do masking,
reprojecting,
and resampling.
As you’ve seen before, to store the NumPy array as a raster file, Rasterio needs the accompanying metadata. It is possible to use the metadata of an existing raster (which we did before), but it is also possible to create it from scratch.
To create metadata from scratch, the CRS can be defined with a function from Rasterio and the transformation can be defined using Affine. Affine is a Python module that facilitates affine transformations, i.e. scaling, rotating, mirroring or skewing of images/rasters/arrays.
Rasterio can write most raster formats from GDAL. The developers recommend using GeoTiff driver for writing as it is the best-tested and best-supported format.
import affine
# Specify the components of the crs (we know them from the DSM)
kwargs = {'driver': 'GTiff',
'dtype': 'float32',
'nodata': np.nan,
'width': 2000,
'height': 2000,
'count': 1,
'crs': rasterio.crs.CRS({'init': 'epsg:28992'}),
'transform': affine.Affine(0.5, 0.0, 173600.0, 0.0, -0.5, 444600.0)}
# Write the raster file
with rasterio.open('data/AHN3_05m_CHM_affine.tif', 'w', **kwargs) as file:
file.write(chm.astype(rasterio.float32))
Raster data can be visualized by passing NumPy arrays to Matplotlib directly or by making use of a method in Rasterio that accesses Matplotlib for you. Using Matplotlib directly allows more flexibility, such as tweaking the legend, axis and labels, and is more suitable for professional purposes. The visualization using Rasterio requires less code and can give a quick idea of your raster data. We show both approaches below. Let’s first make a visualization of the DSM using Matplotlib:
# Create one plot with figure size 10 by 10
fig, ax = plt.subplots(figsize=(10, 10), dpi=200)
# imshow() is the main raster plotting method in Matplotlib
# Again, ensure an equal scale in the x and y direction
dsmplot = ax.imshow(dsm_data[0], cmap='Oranges', extent=bbox, aspect='equal')
# Title (do not do this for a scientific report, use a caption instead)
ax.set_title("Digital Surface Model - WUR Campus", fontsize=14)
# Add a legend (colourbar) with label
cbar = fig.colorbar(dsmplot, fraction=0.035, pad=0.01)
cbar.ax.get_yaxis().labelpad = 15
cbar.ax.set_ylabel('Height (m)', rotation=90)
# Hide the axes
ax.set_axis_off()
plt.show()
If you do not like the orange colourmap of Matplotlib, it is also possible to pick another colourmap.
The second approach with Rasterio only requires one line of code to make a plot. By creating subplots, the figures can be combined (this can be done with Matplotlib directly as well).
# Figure with three subplots, unpack directly
fig, (axdsm, axdtm, axchm) = plt.subplots(1, 3, figsize=(15, 7), dpi=200)
# Populate the three subplots with raster data
show(dsm_data, ax=axdsm, title='DSM')
show(dtm_data, ax=axdtm, title='Filled DTM')
show(chm, ax=axchm, title='CHM')
plt.show()
Rasterio can also create simple histograms by calling functions of Matplotlib.
from rasterio.plot import show_hist
# Figure with three subplots, unpack directly
fig, (axdsm, axdtm, axchm) = plt.subplots(1, 3, figsize=(15, 7), dpi=200)
# Populate the three subplots with histograms
show_hist(dsm_data, ax=axdsm, bins=100, lw=0.0, stacked=False, alpha=0.3, title="Histogram DSM")
show_hist(dtm_data, ax=axdtm, bins=100, lw=0.0, stacked=False, alpha=0.3, title="Histogram filled DTM")
show_hist(chm, ax=axchm, bins=100, lw=0.0, stacked=False, alpha=0.3, title="Histogram CHM")
# Build legends and show
axdsm.legend(['DSM'])
axdtm.legend(['Filled DTM'])
axchm.legend(['CHM'])
plt.show()
Question 6: What is represented on the x and y axis? The default axis labels are DN (x) and Frequency (y); if you were to change them, what labels would you pick to better reflect the content of the plots?


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The CXR Classification Engine will classify a Chest X-Ray image as either Frontal or Lateral.
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mgcrea.ngStrap.affix
The subnavigation on the left is a live demo of the affix plugin.
Affix require the helpers.dimensions and helpers.debounce modules to be loaded.

Append a bs-affix
attribute to any element to enable the plugin.
You can affix inside a custom container with the bs-affix-target
attribute added to any parent element.
$affix
serviceAvailable for programmatic use (mainly inside a directive as it requires an element).
var affix = $affix(element, options);
Options can be passed via data-attributes on the directive or as an object hash to configure the service. For data attributes, append the option name to data-
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| Name | type | default | description |
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| offsetTop | number | 0 | Pixels to offset from top of screen when calculating position of scroll. |
| offsetBottom | number | 0 | Pixels to offset from bottom of screen when calculating position of scroll. |
| offsetParent | number | HTML string | DOMElement | If provided, the affix will be positioned relative to a parent element. If a numeric value is present, the affix will try to find a parent that is N parents "up" in the DOM tree. If a non-numeric value is provided, the value is treated as an HTML string or DOMElement and constructed via angular.element(element). | |
| offsetUnpin | number | 0 | Pixels to offset from unpin position when calculating position of scroll. |
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The concurrent mark sweep collector, also known as the concurrent collector or CMS, is targeted at applications that are sensitive to garbage collection pauses. It performs most garbage collection activity concurrently, i.e., while the application threads are running, to keep garbage collection-induced pauses short. The key performance enhancements made to the CMS collector in JDK 6 are outlined below. See the documents referenced below for more detailed information on these changes, the CMS collector, and garbage collection in HotSpot.
Note that these features only apply when the CMS collector is in
use; the option -XX:+UseConcMarkSweepGC
selects the
CMS collector.
The System.gc()
and
Runtime.getRuntime().gc()
methods instruct the JVM to
run the garbage collector to recycle unused objects. The HotSpot
implementation of these methods currently stops all application
threads to collect the entire heap, which can result in a lengthy
pause particularly when the heap is large. This works against the
goal of the CMS collector to keep pauses short.
In JDK 6, the CMS collector can optionally perform these
collections concurrently, to avoid a lengthy pause in response to a
System.gc()
or Runtime.getRuntime().gc()
call. To enable this feature, add the option
-XX:+ExplicitGCInvokesConcurrent
java
command line.
Several changes were made that increase the default size of the young generation when the CMS collector is used:
The primary effect of these changes is to improve application performance by reducing garbage collection overhead. However, because the default young generation size is larger, applications may also see larger young generation pause times and a larger memory footprint. If necessary, please see the documents referenced below for more details on generations, survivor spaces and the options available for adjusting their sizes.
The CMS collector now uses multiple threads to perform the concurrent marking task in parallel on platforms with multiple processors. This reduces the duration of the concurrent marking cycle, allowing the collector to support applications with larger numbers of threads and higher object allocation rates, particularly on large multiprocessor machines. Prior releases used only a single thread for concurrent marking, limiting the collector's ability to keep up with applications with very high object allocation rates.
The HotSpot memory management whitepaper provides more detail about the CMS collector and the other collectors available in HotSpot, as well as descriptions of the generations, survivor spaces and other memory management concepts.
The HotSpot documentation page contains links to Garbage Collection Tuning Guides, specific to each release, which include guidelines for choosing the garbage collector that best suits your application and techniques that can help reduce garbage collection overhead.


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Implementation of the Keras API meant to be a high-level API for TensorFlow.
Detailed documentation and user guides are available at keras.io.
activations
module: Built-in activation functions.
applications
module: Keras Applications are canned architectures with pre-trained weights.
backend
module: Keras backend API.
callbacks
module: Callbacks: utilities called at certain points during model training.
constraints
module: Constraints: functions that impose constraints on weight values.
datasets
module: Keras built-in datasets.
estimator
module: Keras estimator API.
experimental
module: Public API for tf.keras.experimental namespace.
initializers
module: Keras initializer serialization / deserialization.
layers
module: Keras layers API.
losses
module: Built-in loss functions.
metrics
module: Built-in metrics.
mixed_precision
module: Public API for tf.keras.mixed_precision namespace.
models
module: Code for model cloning, plus model-related API entries.
optimizers
module: Built-in optimizer classes.
preprocessing
module: Keras data preprocessing utils.
regularizers
module: Built-in regularizers.
utils
module: Keras utilities.
wrappers
module: Wrappers for Keras models, providing compatibility with other frameworks.
class Model
: Model
groups layers into an object with training and inference features.
class Sequential
: Linear stack of layers.
Input(...)
: Input()
is used to instantiate a Keras tensor.


closed
by None at 2020-09-28 14:02:05.closed
by None at 2020-09-28 14:02:22.closed
by None at 2020-09-28 14:02:34.closed
by None at 2020-09-28 14:02:51.added_to_project
by Trey Pendragon at 2020-09-28 16:43:39.head_ref_force_pushed
by Trey Pendragon at 2020-09-28 16:56:39.head_ref_force_pushed
by Trey Pendragon at 2020-09-28 16:59:36.referenced
by Christina Chortaria at 2020-09-28 20:06:26.merged
by Christina Chortaria at 2020-09-28 20:06:26.closed
by Christina Chortaria at 2020-09-28 20:06:26.moved_columns_in_project
by Christina Chortaria at 2020-09-28 20:06:26.head_ref_force_pushed
by Trey Pendragon at 2020-09-28 22:25:57.ready_for_review
by Trey Pendragon at 2020-09-28 22:26:40.This builds upon the work supported by https://github.com/pulibrary/dspace-cli/blob/master/library/pulibs.rb
88435/dsp01h415pd575 was listed as the Collection which was created, but this does not resolve.
labeled
by James R. Griffin III at 2020-09-28 12:58:48.labeled
by James R. Griffin III at 2020-09-28 12:58:48.assigned
by James R. Griffin III at 2020-09-28 12:58:48.assigned
by Christina Chortaria at 2020-09-28 13:41:08.moved_columns_in_project
by Christina Chortaria at 2020-09-28 13:41:13.labeled
by Trey Pendragon at 2020-09-28 15:46:34.head_ref_deleted
by Christina Chortaria at 2020-09-28 19:44:01.labeled
by Kevin McElwee at 2020-09-28 16:01:05.added_to_project
by Christina Chortaria at 2020-09-28 13:03:37.moved_columns_in_project
by Christina Chortaria at 2020-09-28 13:28:41.labeled
by Christina Chortaria at 2020-09-28 15:44:51.renamed
by Christina Chortaria at 2020-09-28 15:45:29.renamed
by Christina Chortaria at 2020-09-28 15:45:44.unlabeled
by Christina Chortaria at 2020-09-28 15:48:28.Coverage remained the same at 81.285% when pulling 48f19ac3404508dea9a3abc1cb8c5d3274c49b74 on 806-alma-traject into 0d1283e5c70bd27b0ce9d5df0cd0ed658edbeabe on master.
head_ref_force_pushed
by Christina Chortaria at 2020-09-28 17:45:45.assigned
by Christina Chortaria at 2020-09-28 19:34:41.assigned
by None at 2020-09-28 19:34:59.review_requested
by Kevin Reiss at 2020-09-28 12:05:45.referenced
by Kevin Reiss at 2020-09-28 15:04:50.merged
by Kevin Reiss at 2020-09-28 15:04:50.closed
by Kevin Reiss at 2020-09-28 15:04:50.Coverage remained the same at 96.578% when pulling d6947ab2d476b80ea6e85abf93674789766b2961 on remove_v8 into 5aebc88fd145374ace6e4e973b053ea51ea30e88 on main.
deployed
by Trey Pendragon at 2020-09-28 16:34:45.ready_for_review
by Trey Pendragon at 2020-09-28 16:44:31.added_to_project
by Trey Pendragon at 2020-09-28 16:44:38.head_ref_force_pushed
by Trey Pendragon at 2020-09-28 16:50:45.deployed
by Trey Pendragon at 2020-09-28 16:50:55.convert_to_draft
by Trey Pendragon at 2020-09-28 16:53:48.deployed
by Trey Pendragon at 2020-09-28 18:02:29.referenced
by Kevin Reiss at 2020-09-28 18:25:55.merged
by Kevin Reiss at 2020-09-28 18:25:55.closed
by Kevin Reiss at 2020-09-28 18:25:55.head_ref_deleted
by Kevin Reiss at 2020-09-28 18:25:59.referenced
by None at 2020-09-28 12:56:15.merged
by None at 2020-09-28 12:56:15.closed
by None at 2020-09-28 12:56:15.head_ref_deleted
by None at 2020-09-28 12:56:16.referenced
by Francis Kayiwa at 2020-09-28 18:12:52.merged
by Francis Kayiwa at 2020-09-28 18:12:52.closed
by Francis Kayiwa at 2020-09-28 18:12:52.head_ref_deleted
by Francis Kayiwa at 2020-09-28 18:12:53.assigned
by Francis Kayiwa at 2020-09-28 18:38:26.We're going to ingest MC016 so we can look at this. The real request seems to be that you can request child components from the parent component, but we may not need to do this with the request cart.
moved_columns_in_project
by Eliot Jordan at 2020-09-28 16:48:37.assigned
by Eliot Jordan at 2020-09-28 16:48:45.Coverage remained the same at 100.0% when pulling c1be889b6de10238cfd60d0496772f8870215a9b on 890-lando into 2007f33e0bbbd2a46633896e46c350a6694b4731 on master.
deployed
by Trey Pendragon at 2020-09-28 20:46:25.head_ref_force_pushed
by Eliot Jordan at 2020-09-28 21:23:18.head_ref_force_pushed
by Eliot Jordan at 2020-09-28 21:35:17.referenced
by None at 2020-09-28 11:56:06.referenced
by None at 2020-09-28 12:21:56.assigned
by None at 2020-09-28 12:23:02.referenced
by None at 2020-09-28 12:48:58.referenced
by None at 2020-09-28 12:54:53.closed
by Kevin Reiss at 2020-09-28 14:28:36.referenced
by None at 2020-09-28 13:11:02.referenced
by None at 2020-09-28 13:12:59.closed
by Kevin Reiss at 2020-09-28 14:28:37.head_ref_force_pushed
by None at 2020-09-28 12:21:56.renamed
by None at 2020-09-28 12:22:32.head_ref_force_pushed
by None at 2020-09-28 12:48:58.head_ref_force_pushed
by None at 2020-09-28 12:54:53.head_ref_force_pushed
by None at 2020-09-28 13:12:59.referenced
by Kevin Reiss at 2020-09-28 14:28:35.merged
by Kevin Reiss at 2020-09-28 14:28:35.closed
by Kevin Reiss at 2020-09-28 14:28:36.head_ref_deleted
by Kevin Reiss at 2020-09-28 14:28:40.referenced
by None at 2020-09-28 17:58:29.merged
by None at 2020-09-28 17:58:29.closed
by None at 2020-09-28 17:58:29.head_ref_deleted
by None at 2020-09-28 17:58:33.referenced
by None at 2020-09-28 15:44:40.closed
by Kevin Reiss at 2020-09-28 20:51:15.referenced
by Kevin Reiss at 2020-09-28 20:51:15.merged
by Kevin Reiss at 2020-09-28 20:51:15.closed
by Kevin Reiss at 2020-09-28 20:51:16.head_ref_deleted
by Kevin Reiss at 2020-09-28 20:51:21.


Frame:
/
Resolution:
Fps:
Show:
Video
Motion
Features
Filters
Features:
Min-Max Features/Filters
Motion Map


Welcome
Universal WebView is an Xcode 8 project that allows you to wrap your website in a super simple iOS app. If you are a web developer who wants to release an iOS app, this should help you cut some corners when it comes to learning Swift. It is ideal for single page web app with on-screen/no navigation. For example, a game, interactive page, web based slideshow, etc.
Try Your Website
Click on the button bellow to test your website. Just search your web via Google and check it out.
Video
WebView App supports HTML5 videos, YouTube, Vimeo, JW Player etc. You can play any video you want.
Geolocation
WebView App supportst HTML5 geolocation. You just need to accept necessery permissions when prompted in the app. As of Chrome 50, the Geolocation API will only work on secure contexts such as HTTPS. If your site is hosted on an non-secure origin (such as HTTP) the requests to get the users location will no longer function.
Click on the button bellow to test if your website can get user location. Just search your web via Google and check it out.


Download the APK
See the source code
Learn how to build your own native payment app
Uninstall BobPay Web Payment App
Install BobPay Web Payment App
Learn how to build your own web based payment app.


If you know the name of your doctor then enter it above, otherwise enter you ailment and we'll recommend a list of doctors for you


The template helpers serve to relieve the templates from including the same inline code again and again. It’s a set of standardized methods for working with forms (FormHelper), dates (DateHelper), texts (TextHelper), and Active Records (ActiveRecordHelper) that’s available to all templates by default.
It’s also really easy to make your own helpers and it’s much encouraged to keep the template files free from complicated logic. It’s even encouraged to bundle common compositions of methods from other helpers (often the common helpers) as they’re used by the specific application.
module MyHelper def hello_world() "hello world" end end
MyHelper can now be included in a controller, like this:
class MyController < ActionController::Base helper :my_helper end
…and, same as above, used in any template rendered from MyController, like this:
Let’s hear what the helper has to say: <%= hello_world %>
Declare a helper:
helper :foo
requires ‘foo_helper’ and includes FooHelper in the template class.
helper FooHelper
includes FooHelper in the template class.
helper { def foo() "#{bar} is the very best" end }
evaluates the block in the template class, adding method foo.
helper(:three, BlindHelper) { def mice() 'mice' end }
does all three.
[ show source ]
# File vendor/rails/actionpack/lib/action_controller/helpers.rb, line 60 60: def helper(*args, &block) 61: args.flatten.each do |arg| 62: case arg 63: when Module 64: add_template_helper(arg) 65: when String, Symbol 66: file_name = arg.to_s.underscore + '_helper' 67: class_name = file_name.camelize 68: 69: begin 70: require_dependency(file_name) 71: rescue LoadError => load_error 72: requiree = / -- (.*?)(\.rb)?$/.match(load_error).to_a[1] 73: msg = (requiree == file_name) ? "Missing helper file helpers/#{file_name}.rb" : "Can't load file: #{requiree}" 74: raise LoadError.new(msg).copy_blame!(load_error) 75: end 76: 77: add_template_helper(class_name.constantize) 78: else 79: raise ArgumentError, 'helper expects String, Symbol, or Module argument' 80: end 81: end 82: 83: # Evaluate block in template class if given. 84: master_helper_module.module_eval(&block) if block_given? 85: end
Declare a controller attribute as a helper. For example,
helper_attr :name attr_accessor :name
makes the name and name= controller methods available in the view. The is a convenience wrapper for helper_method.
[ show source ]
# File vendor/rails/actionpack/lib/action_controller/helpers.rb, line 107 107: def helper_attr(*attrs) 108: attrs.flatten.each { |attr| helper_method(attr, "#{attr}=") } 109: end
Declare a controller method as a helper. For example,
helper_method :link_to def link_to(name, options) ... end
makes the link_to controller method available in the view.
[ show source ]
# File vendor/rails/actionpack/lib/action_controller/helpers.rb, line 91 91: def helper_method(*methods) 92: methods.flatten.each do |method| 93: master_helper_module.module_eval "def \#{method}(*args, &block)\ncontroller.send(%(\#{method}), *args, &block)\nend\n" 94: end 95: end


Prefered creative, advertising, sales and marketing consultancy
To provide high quality service that combine creativity with value pricing, while establishing a successful relationship with our customers and stakeholders.
To constantly strive to meet or exceed our customer needs and
expectations of service and price, improve customer experience locally and globally and selection.
To this end, we will perform periodic reviews of the marketplace to improve our offerings.
To be the most passionately
referred agency in the advertising, marketing consultancy in the commercial industry, while striving to provide our clients with the optimum service they deserve. Service is the heart of the business.


jignesh@Enigma:~$ cd jignesh/About_Me
jignesh@Enigma:~$ cat jignesh.txt
"Hey there, nice to see you here."
This is jignesh.Welcome
I am a computer engineer, you can checkout my resume here.
You can visit my LinkedIn, here: LinkedIn
You can also contact me through email, feel free to send me an email at<EMAIL_ADDRESS>Cheers!


The project loads two similar models that were trained to produce one of six labels given a sentence. The models were generated with the same program but with a different split of the data between training and validation. The average of their confidences that each label is correct is used to answer questions.
The models were the result of five hyperparameter searches. Each search explored twenty variants and then replaced the current model with the best one found if it is better. See the search and replace block to see how this was implemented.
The project splits the data into training, validation, and test datasets in such a way that they each contain the same number of each category of question. See the create training and validation data block.
The project turns sentences into lists of 512 numbers using the features of sentence block. This relies upon the universal sentence encoder.
Or replace the data with your own.


We are about to step into the world of Modern Cryptology. But first, it is neccesary to undertsand Xor Encryption and logic gates.
Xor was an operation patented in 1919 for use in the Vernam cipher, which combines the binary of plaintext with teh binary bits of a key.
Xor is a type of logic gate, which means it returns true or false, with 1 being true and 0 being false. Other logic gates include and and or, the pattern of these gates is displayed in the chart below (and *, or +, and xor^).
In Xor encryption, a string of characters is converted to their binary value using an ascii table. Every letter and symbol in language has a number equivalent. These numbers are called ascii. They are shown on the ascii table below:
The table below shows the decimal value, which we can use to get the binary value. Decimal numbers are base 10, they are what we are taught to count in, there are 10 basic numbers:0, 1, 2, 3...9. Binary numbers are in base 2, so they only have 1s and 0s. Each binary number has 8 bits (so 8 spots for a one or a zero)
We are going to convert 30 from decimal to binary to show you how it is done.
Click to move on!
Click to go back!


set-op.hpp File Reference
(Revision: 10359)
Go to the source code of this file.
Namespaces | |
| namespace | Gecode |
Gecode toplevel namespace | |
| namespace | Gecode::Int |
Finite domain integers. | |
| namespace | Gecode::Int::Sequence |
Sequence propagators | |
Enumerations | |
| enum | Gecode::Int::Sequence::TakesStatus { Gecode::Int::Sequence::TS_NO, Gecode::Int::Sequence::TS_YES, Gecode::Int::Sequence::TS_MAYBE } |
Status of whether a view takes a value from a set. More... | |
Functions | |
| template<class View > | |
| TakesStatus | Gecode::Int::Sequence::takes (const View &x, int s) |
| Return whether view x takes value s. | |
| template<class View > | |
| TakesStatus | Gecode::Int::Sequence::takes (const View &x, const IntSet &s) |
| Return whether view x takes value from s. | |
| template<class View > | |
| bool | Gecode::Int::Sequence::includes (const View &x, int s) |
| Test whether all values of view x are included in s. | |
| template<class View > | |
| bool | Gecode::Int::Sequence::includes (const View &x, const IntSet &s) |
| Test whether all values of view x are included in s. | |
| template<class View > | |
| bool | Gecode::Int::Sequence::excludes (const View &x, int s) |
| Test whether all values of view x are excluded from s. | |
| template<class View > | |
| bool | Gecode::Int::Sequence::excludes (const View &x, const IntSet &s) |
| Test whether all values of view x are excluded from s. | |
| template<class View > | |
| bool | Gecode::Int::Sequence::undecided (const View &x, int s) |
| Test whether no decision on inclusion or exclusion of values of view x in s can be made. | |
| template<class View > | |
| bool | Gecode::Int::Sequence::undecided (const View &x, const IntSet &s) |
| Test whether no decision on inclusion or exclusion of values of view x in s can be made. | |
| template<class View > | |
| ModEvent | Gecode::Int::Sequence::include (Space &home, View &x, int s) |
| Prune view x to only include values from s. | |
| template<class View > | |
| ModEvent | Gecode::Int::Sequence::include (Space &home, View &x, const IntSet &s) |
| Prune view x to only include values from s. | |
| template<class View > | |
| ModEvent | Gecode::Int::Sequence::exclude (Space &home, View &x, int s) |
| Prune view x to exclude all values from s. | |
| template<class View > | |
| ModEvent | Gecode::Int::Sequence::exclude (Space &home, View &x, const IntSet &s) |
| Prune view x to exclude all values from s. |