How to use the Blink_1Hz program in a clock program - irq

I want to use the RPi Pico to build a clock.
In the documentation I found "blink_1hz.py" and I want to use the
1 second interrupt as counter for my clock.
The original program prints the system time every second correctly.
I only replaced the call to the lambda function in the irq by my own seconds counter function.
My problem is:
My version the of program prints only once instead of ten times, and does not increment t
I looked all over the internet but there is little specific information about the interrupts used by the StateMachines.
All suggestions are welcome
Here is my code:
# Example using PIO to blink an LED and raise an IRQ at 1Hz.
import time
from machine import Pin
import rp2
#rp2.asm_pio(set_init=rp2.PIO.OUT_LOW)
def blink_1hz():
# Cycles: 1 + 1 + 6 + 32 * (30 + 1) = 1000
irq(rel(0))
set(pins, 1)
set(x, 31) [5]
label("delay_high")
nop() [29]
jmp(x_dec, "delay_high")
# Cycles: 1 + 7 + 32 * (30 + 1) = 1000
set(pins, 0)
set(x, 31) [6]
label("delay_low")
nop() [29]
jmp(x_dec, "delay_low")
def secs():
global t
t = t+1
print("secs",t)
t = 0
# Create the StateMachine with the blink_1hz program, outputting on Pin(25).
sm = rp2.StateMachine(0, blink_1hz, freq=2000, set_base=Pin(25))
# Set the IRQ handler to print the millisecond timestamp.
sm.irq(handler = secs()) # prints secs only once
#sm.irq(lambda p: print( time.ticks_ms())) # original, prints ticks every second
# Start the StateMachine.
sm.active(1)
time.sleep(10)
# Stop the StateMachine
sm.active(0)
p rint("main",t)

Related

When using the frame skipping wrapper for OpenAI Gym, what is the purpose of the np.max line?

I'm implementing the following wrapper used commonly in OpenAI's Gym for Frame Skipping. It can be found in dqn/atari_wrappers.py
I'm very confused about the following line:
max_frame = np.max(np.stack(self._obs_buffer), axis=0)
I have added comments throughout the code for the parts I understand and to aid anyone who may be able to help.
np.stack(self._obs_buffer) stacks the two states in _obs_buffer.
np.max returns the maximum along axis 0.
But what I don't understand is why we're doing this or what it's really doing.
class MaxAndSkipEnv(gym.Wrapper):
"""Return only every 4th frame"""
def __init__(self, env=None, skip=4):
super(MaxAndSkipEnv, self).__init__(env)
# Initialise a double ended queue that can store a maximum of two states
self._obs_buffer = deque(maxlen=2)
# _skip = 4
self._skip = skip
def _step(self, action):
total_reward = 0.0
done = None
for _ in range(self._skip):
# Take a step
obs, reward, done, info = self.env.step(action)
# Append the new state to the double ended queue buffer
self._obs_buffer.append(obs)
# Update the total reward by summing the (reward obtained from the step taken) + (the current
# total reward)
total_reward += reward
# If the game ends, break the for loop
if done:
break
max_frame = np.max(np.stack(self._obs_buffer), axis=0)
return max_frame, total_reward, done, info
At the end of the for loop the self._obs_buffer holds the last two frames.
Those two frames are then max-pooled over, resulting in an observation, that contains some temporal information.

Collecting Tweets using max_id is not working as expected

Iam currently doing a tweet search using Twitter Api. However, taking the tweet id is not working for me.
Here is my code:
searchQuery = '#BLM' # this is what we're searching for
searchQuery = searchQuery + "-filter:retweets"
Geocode="39.8, -95.583068847656, 2500km"
maxTweets = 1000000 # Some arbitrary large number
tweetsPerQry = 100 # this is the max the API permits
fName = 'tweetsBLM.json' # We'll store the tweets in a json file.
sinceId = None
#max_id = -1 # initial search
max_id=1278836959926980609 # the last id of previous search
tweetCount = 0
print("Downloading max {0} tweets".format(maxTweets))
with open(fName, 'w') as f:
while tweetCount < maxTweets:
try:
if (max_id <= 0):
if (not sinceId):
new_tweets = api.search(q=searchQuery,lang="en", geocode=Geocode,
count=tweetsPerQry)
else:
new_tweets = api.search(q=searchQuery,lang="en",geocode=Geocode,
count=tweetsPerQry,
since_id=sinceId )
else:
if (not sinceId):
new_tweets = api.search(q=searchQuery, lang="en", geocode=Geocode,
count=tweetsPerQry,
max_id=str(max_id - 1) )
else:
new_tweets = api.search(q=searchQuery, lang="en", geocode=Geocode,
count=tweetsPerQry,
max_id=str(max_id - 1),
since_id=sinceId)
if not new_tweets:
print("No more tweets found")
break
for tweet in new_tweets:
f.write(jsonpickle.encode(tweet._json, unpicklable=False) +
'\n')
tweetCount += len(new_tweets)
print("Downloaded {0} tweets".format(tweetCount))
max_id = new_tweets[-1].id
except tweepy.TweepError as e:
# Just exit if any error
print("some error : " + str(e))
print('exception raised, waiting 15 minutes')
print('(until:', dt.datetime.now() + dt.timedelta(minutes=15), ')')
time.sleep(15*60)
break
print ("Downloaded {0} tweets, Saved to {1}".format(tweetCount, fName))
This code works perfectly fine. I initially run it and got about 40 000 tweets. Then i took the id of the last tweet of previous/initial search to go back in time. However, i was disappointed to see that there were no tweets anymore. I can not believe that for a second. I must be going wrong somewhere because #BLM has been very active in the last 2/3 months.
Any help is very welcome. Thank you
I may have found the answer. Using Twitter API, it is not possible to get older tweets (7 days old or more). Using max_id to get around this is not possible either.
The only way is to stream and wait for more than 7 days.
Finally, there is also this link that look for older tweets
https://pypi.org/project/GetOldTweets3/ it is an extension of the original Jefferson Henrique's work

Q values overshoot in Double Deep Q Learning

I am trying to teach the agent to play ATARI Space Invaders video game, but my Q values overshoot.
I have clipped positive rewards to 1 (agent also receives -1 for losing a life), so the maximum expected return should be around 36 (maybe I am wrong about this). I have also implemented the Huber loss.
I have noticed that when my Q values start overshooting, the agent stops improving (the reward stops increasing).
Code can be found here
Plots can be found here
Note:
I have binarized frames, so that I can use bigger replay buffer (my replay buffer size is 300 000 which is 3 times smaller than in original paper)
EDIT:
I have binarized frames so I can use 1 bit(instead of 8 bits) to store one pixel of the image in the replay buffer, using numpy.packbits function. In that way I can use 8 times bigger replay buffer. I have checked if image is distorted after packing it with packbits, and it is NOT. So sampling from replay buffer works fine. This is the main loop of the code (maybe the problem is in there):
frame_count = 0
LIFE_CHECKPOINT = 3
for episode in range(EPISODE,EPISODES):
# reset the environment and init variables
frames, _, _ = space_invaders.resetEnv(NUM_OF_FRAMES)
state = stackFrames(frames)
done = False
episode_reward = 0
episode_reward_clipped = 0
frames_buffer = frames # contains preprocessed frames (not stacked)
while not done:
if (episode % REPORT_EPISODE_FREQ == 0):
space_invaders.render()
# select an action from behaviour policy
action, Q_value, is_greedy_action = self.EGreedyPolicy(Q, state, epsilon, len(ACTIONS))
# perform action in the environment
observation, reward, done, info = space_invaders.step(action)
episode_reward += reward # update episode reward
reward, LIFE_CHECKPOINT = self.getCustomReward(reward, info, LIFE_CHECKPOINT)
episode_reward_clipped += reward
frame = preprocessFrame(observation, RESOLUTION)
# pop first frame from the buffer, and add new at the end (s1=[f1,f2,f3,f4], s2=[f2,f3,f4,f5])
frames_buffer.append(frame)
frames_buffer.pop(0)
new_state = stackFrames(frames_buffer)
# add (s,a,r,s') tuple to the replay buffer
replay_buffer.add(packState(state), action, reward, packState(new_state), done)
state = new_state # new state becomes current state
frame_count += 1
if (replay_buffer.size() > MIN_OBSERVATIONS): # if there is enough data in replay buffer
Q_values.append(Q_value)
if (frame_count % TRAINING_FREQUENCY == 0):
batch = replay_buffer.sample(BATCH_SIZE)
loss = Q.train_network(batch, BATCH_SIZE, GAMMA, len(ACTIONS))
losses.append(loss)
num_of_weight_updates += 1
if (epsilon > EPSILON_END):
epsilon = self.decayEpsilon(epsilon, EPSILON_START, EPSILON_END, FINAL_EXPLORATION_STATE)
if (num_of_weight_updates % TARGET_NETWORK_UPDATE_FREQ == 0) and (num_of_weight_updates != 0): # update weights of target network
Q.update_target_network()
print("Target_network is updated!")
episode_rewards.append(episode_reward)
I have also checked the Q.train_network and Q.update_target_network functions and they work fine.
I was wondering if problem can be in hyper parameters:
ACTIONS = {"NOOP":0,"FIRE":1,"RIGHT":2,"LEFT":3,"RIGHTFIRE":4,"LEFTFIRE":5}
NUM_OF_FRAMES = 4 # number of frames that make 1 state
EPISODES = 10000 # number of episodes
BUFFER_SIZE = 300000 # size of the replay buffer(can not put bigger size, RAM)
MIN_OBSERVATIONS = 30000
RESOLUTION = 84 # resolution of frames
BATCH_SIZE = 32
EPSILON_START = 1 # starting value for the exploration probability
EPSILON_END = 0.1
FINAL_EXPLORATION_STATE = 300000 # final frame for which epsilon is decayed
GAMMA = 0.99 # discount factor
TARGET_NETWORK_UPDATE_FREQ = 10000
REPORT_EPISODE_FREQ = 100
TRAINING_FREQUENCY = 4
OPTIMIZER = RMSprop(lr=0.00025,rho=0.95,epsilon=0.01)

Shapefile with overlapping polygons: calculate average values

I have a very big polygon shapefile with hundreds of features, often overlapping each other. Each of these features has a value stored in the attribute table. I simply need to calculate the average values in the areas where they overlap.
I can imagine that this task requires several intricate steps: I was wondering if there is a straightforward methodology.
I’m open to every kind of suggestion, I can use ArcMap, QGis, arcpy scripts, PostGis, GDAL… I just need ideas. Thanks!
You should use the Union tool from ArcGIS. It will create new polygons where the polygons overlap. In order to keep the attributes from both polygons, add your polygon shapefile twice as input and use ALL as join_attributes parameter.This creates also polygons intersecting with themselves, you can select and delete them easily as they have the same FIDs. Then just add a new field to the attribute table and calculate it based on the two original value fields from the input polygons.
This can be done in a script or directly with the toolbox's tools.
After few attempts, I found a solution by rasterising all the features singularly and then performing cell statistics in order to calculate the average.
See below the script I wrote, please do not hesitate to comment and improve it!
Thanks!
#This script processes a shapefile of snow persistence (area of interest: Afghanistan).
#the input shapefile represents a month of snow cover and contains several features.
#each feature represents a particular day and a particular snow persistence (low,medium,high,nodata)
#these features are polygons multiparts, often overlapping.
#a feature of a particular day can overlap a feature of another one, but features of the same day and with
#different snow persistence can not overlap each other.
#(potentially, each shapefile contains 31*4 feature).
#the script takes the features singularly and exports each feature in a temporary shapefile
#which contains only one feature.
#Then, each feature is converted to raster, and after
#a logical conditional expression gives a value to the pixel according the intensity (high=3,medium=2,low=1,nodata=skipped).
#Finally, all these rasters are summed and divided by the number of days, in order to
#calculate an average value.
#The result is a raster with the average snow persistence in a particular month.
#This output raster ranges from 0 (no snow) to 3 (persistent snow for the whole month)
#and values outside this range should be considered as small errors in pixel overlapping.
#This script needs a particular folder structure. The folder C:\TEMP\Afgh_snow_cover contains 3 subfolders
#input, temp and outputs. The script takes care automatically of the cleaning of temporary data
import arcpy, numpy, os
from arcpy.sa import *
from arcpy import env
#function for finding unique values of a field in a FC
def unique_values_in_table(table, field):
data = arcpy.da.TableToNumPyArray(table, [field])
return numpy.unique(data[field])
#check extensions
try:
if arcpy.CheckExtension("Spatial") == "Available":
arcpy.CheckOutExtension("Spatial")
else:
# Raise a custom exception
#
raise LicenseError
except LicenseError:
print "spatial Analyst license is unavailable"
except:
print arcpy.GetMessages(2)
finally:
# Check in the 3D Analyst extension
#
arcpy.CheckInExtension("Spatial")
# parameters and environment
temp_folder = r"C:\TEMP\Afgh_snow_cover\temp_rasters"
output_folder = r"C:\TEMP\Afgh_snow_cover\output_rasters"
env.workspace = temp_folder
unique_field = "FID"
field_Date = "DATE"
field_Type = "Type"
cellSize = 0.02
fc = r"C:\TEMP\Afgh_snow_cover\input_shapefiles\snow_cover_Dec2007.shp"
stat_output_name = fc[-11:-4] + ".tif"
#print stat_output_name
arcpy.env.extent = "MAXOF"
#find all the uniquesID of the FC
uniqueIDs = unique_values_in_table(fc, "FID")
#make layer for selecting
arcpy.MakeFeatureLayer_management (fc, "lyr")
#uniqueIDs = uniqueIDs[-5:]
totFeatures = len(uniqueIDs)
#for each feature, get the date and the type of snow persistence(type can be high, medium, low and nodata)
for i in uniqueIDs:
SC = arcpy.SearchCursor(fc)
for row in SC:
if row.getValue(unique_field) == i:
datestring = row.getValue(field_Date)
typestring = row.getValue(field_Type)
month = str(datestring.month)
day = str(datestring.day)
year = str(datestring.year)
#format month and year string
if len(month) == 1:
month = '0' + month
if len(day) == 1:
day = '0' + day
#convert snow persistence to numerical value
if typestring == 'high':
typestring2 = 3
if typestring == 'medium':
typestring2 = 2
if typestring == 'low':
typestring2 = 1
if typestring == 'nodata':
typestring2 = 0
#skip the NoData features, and repeat the following for each feature (a feature is a day and a persistence value)
if typestring2 > 0:
#create expression for selecting the feature
expression = ' "FID" = ' + str(i) + ' '
#select the feature
arcpy.SelectLayerByAttribute_management("lyr", "NEW_SELECTION", expression)
#create
#outFeatureClass = os.path.join(temp_folder, ("M_Y_" + str(i)))
#create faeture class name, writing the snow persistence value at the end of the name
outFeatureClass = "Afg_" + str(year) + str(month) + str(day) + "_" + str(typestring2) + '.shp'
#export the feature
arcpy.FeatureClassToFeatureClass_conversion("lyr", temp_folder, outFeatureClass)
print "exported FID " + str(i) + " \ " + str(totFeatures)
#create name of the raster and convert the newly created feature to raster
outRaster = outFeatureClass[4:-4] + ".tif"
arcpy.FeatureToRaster_conversion(outFeatureClass, field_Type, outRaster, cellSize)
#remove the temporary fc
arcpy.Delete_management(outFeatureClass)
del SC, row
#now many rasters are created, representing the snow persistence types of each day.
#list all the rasters created
rasterList = arcpy.ListRasters("*", "All")
print rasterList
#now the rasters have values 1 and 0. the following loop will
#perform CON expressions in order to assign the value of snow persistence
for i in rasterList:
print i + ":"
inRaster = Raster(i)
#set the value of snow persistence, stored in the raster name
value_to_set = i[-5]
inTrueRaster = int(value_to_set)
inFalseConstant = 0
whereClause = "Value > 0"
# Check out the ArcGIS Spatial Analyst extension license
arcpy.CheckOutExtension("Spatial")
print 'Executing CON expression and deleting input'
# Execute Con , in order to assign to each pixel the value of snow persistence
print str(inTrueRaster)
try:
outCon = Con(inRaster, inTrueRaster, inFalseConstant, whereClause)
except:
print 'CON expression failed (probably empty raster!)'
nameoutput = i[:-4] + "_c.tif"
outCon.save(nameoutput)
#delete the temp rasters with values 0 and 1
arcpy.Delete_management(i)
#list the raster with values of snow persistence
rasterList = arcpy.ListRasters("*_c.tif", "All")
#sum the rasters
print "Caclulating SUM"
outCellStats = CellStatistics(rasterList, "SUM", "DATA")
#calculate the number of days (num of rasters/3)
print "Calculating day ratio"
num_of_rasters = len(rasterList)
print 'Num of rasters : ' + str(num_of_rasters)
num_of_days = num_of_rasters / 3
print 'Num of days : ' + str(num_of_days)
#in order to store decimal values, multiplicate the raster by 1000 before dividing
outCellStats = outCellStats * 1000 / num_of_days
#save the output raster
print "saving output " + stat_output_name
stat_output_name = os.path.join(output_folder,stat_output_name)
outCellStats.save(stat_output_name)
#delete the remaining temporary rasters
print "deleting CON rasters"
for i in rasterList:
print "deleting " + i
arcpy.Delete_management(i)
arcpy.Delete_management("lyr")
Could you rasterize your polygons into multiple layers, each pixel could contain your attribute value. Then merge the layers by averaging the attribute values?

How to profile methods in Scala?

What is a standard way of profiling Scala method calls?
What I need are hooks around a method, using which I can use to start and stop Timers.
In Java I use aspect programming, aspectJ, to define the methods to be profiled and inject bytecode to achieve the same.
Is there a more natural way in Scala, where I can define a bunch of functions to be called before and after a function without losing any static typing in the process?
Do you want to do this without changing the code that you want to measure timings for? If you don't mind changing the code, then you could do something like this:
def time[R](block: => R): R = {
val t0 = System.nanoTime()
val result = block // call-by-name
val t1 = System.nanoTime()
println("Elapsed time: " + (t1 - t0) + "ns")
result
}
// Now wrap your method calls, for example change this...
val result = 1 to 1000 sum
// ... into this
val result = time { 1 to 1000 sum }
In addition to Jesper's answer, you can automatically wrap method invocations in the REPL:
scala> def time[R](block: => R): R = {
| val t0 = System.nanoTime()
| val result = block
| println("Elapsed time: " + (System.nanoTime - t0) + "ns")
| result
| }
time: [R](block: => R)R
Now - let's wrap anything in this
scala> :wrap time
wrap: no such command. Type :help for help.
OK - we need to be in power mode
scala> :power
** Power User mode enabled - BEEP BOOP SPIZ **
** :phase has been set to 'typer'. **
** scala.tools.nsc._ has been imported **
** global._ and definitions._ also imported **
** Try :help, vals.<tab>, power.<tab> **
Wrap away
scala> :wrap time
Set wrapper to 'time'
scala> BigDecimal("1.456")
Elapsed time: 950874ns
Elapsed time: 870589ns
Elapsed time: 902654ns
Elapsed time: 898372ns
Elapsed time: 1690250ns
res0: scala.math.BigDecimal = 1.456
I have no idea why that printed stuff out 5 times
Update as of 2.12.2:
scala> :pa
// Entering paste mode (ctrl-D to finish)
package wrappers { object wrap { def apply[A](a: => A): A = { println("running...") ; a } }}
// Exiting paste mode, now interpreting.
scala> $intp.setExecutionWrapper("wrappers.wrap")
scala> 42
running...
res2: Int = 42
This what I use:
import System.nanoTime
def profile[R](code: => R, t: Long = nanoTime) = (code, nanoTime - t)
// usage:
val (result, time) = profile {
/* block of code to be profiled*/
}
val (result2, time2) = profile methodToBeProfiled(foo)
There are three benchmarking libraries for Scala that you can avail of.
Since the URLs on the linked site are likely to change, I am pasting the relevant content below.
SPerformance - Performance Testing framework aimed at automagically comparing performance tests and working inside Simple Build Tool.
scala-benchmarking-template - SBT template project for creating Scala (micro-)benchmarks based on Caliper.
Metrics - Capturing JVM- and application-level metrics. So you know what's going on
testing.Benchmark might be useful.
scala> def testMethod {Thread.sleep(100)}
testMethod: Unit
scala> object Test extends testing.Benchmark {
| def run = testMethod
| }
defined module Test
scala> Test.main(Array("5"))
$line16.$read$$iw$$iw$Test$ 100 100 100 100 100
I use a technique that's easy to move around in code blocks. The crux is that the same exact line starts and ends the timer - so it is really a simple copy and paste. The other nice thing is that you get to define what the timing means to you as a string, all in that same line.
Example usage:
Timelog("timer name/description")
//code to time
Timelog("timer name/description")
The code:
object Timelog {
val timers = scala.collection.mutable.Map.empty[String, Long]
//
// Usage: call once to start the timer, and once to stop it, using the same timer name parameter
//
def timer(timerName:String) = {
if (timers contains timerName) {
val output = s"$timerName took ${(System.nanoTime() - timers(timerName)) / 1000 / 1000} milliseconds"
println(output) // or log, or send off to some performance db for analytics
}
else timers(timerName) = System.nanoTime()
}
Pros:
no need to wrap code as a block or manipulate within lines
can easily move the start and end of the timer among code lines when being exploratory
Cons:
less shiny for utterly functional code
obviously this object leaks map entries if you do not "close" timers,
e.g. if your code doesn't get to the second invocation for a given timer start.
ScalaMeter is a nice library to perform benchmarking in Scala
Below is a simple example
import org.scalameter._
def sumSegment(i: Long, j: Long): Long = (i to j) sum
val (a, b) = (1, 1000000000)
val execution_time = measure { sumSegment(a, b) }
If you execute above code snippet in Scala Worksheet you get the running time in milliseconds
execution_time: org.scalameter.Quantity[Double] = 0.260325 ms
The recommended approach to benchmarking Scala code is via sbt-jmh
"Trust no one, bench everything." - sbt plugin for JMH (Java
Microbenchmark Harness)
This approach is taken by many of the major Scala projects, for example,
Scala programming language itself
Dotty (Scala 3)
cats library for functional programming
Metals language server for IDEs
Simple wrapper timer based on System.nanoTime is not a reliable method of benchmarking:
System.nanoTime is as bad as String.intern now: you can use it,
but use it wisely. The latency, granularity, and scalability effects
introduced by timers may and will affect your measurements if done
without proper rigor. This is one of the many reasons why
System.nanoTime should be abstracted from the users by benchmarking
frameworks
Furthermore, considerations such as JIT warmup, garbage collection, system-wide events, etc. might introduce unpredictability into measurements:
Tons of effects need to be mitigated, including warmup, dead code
elimination, forking, etc. Luckily, JMH already takes care of many
things, and has bindings for both Java and Scala.
Based on Travis Brown's answer here is an example of how to setup JMH benchmark for Scala
Add jmh to project/plugins.sbt
addSbtPlugin("pl.project13.scala" % "sbt-jmh" % "0.3.7")
Enable jmh plugin in build.sbt
enablePlugins(JmhPlugin)
Add to src/main/scala/bench/VectorAppendVsListPreppendAndReverse.scala
package bench
import org.openjdk.jmh.annotations._
#State(Scope.Benchmark)
#BenchmarkMode(Array(Mode.AverageTime))
class VectorAppendVsListPreppendAndReverse {
val size = 1_000_000
val input = 1 to size
#Benchmark def vectorAppend: Vector[Int] =
input.foldLeft(Vector.empty[Int])({ case (acc, next) => acc.appended(next)})
#Benchmark def listPrependAndReverse: List[Int] =
input.foldLeft(List.empty[Int])({ case (acc, next) => acc.prepended(next)}).reverse
}
Execute benchmark with
sbt "jmh:run -i 10 -wi 10 -f 2 -t 1 bench.VectorAppendVsListPreppendAndReverse"
The results are
Benchmark Mode Cnt Score Error Units
VectorAppendVsListPreppendAndReverse.listPrependAndReverse avgt 20 0.024 ± 0.001 s/op
VectorAppendVsListPreppendAndReverse.vectorAppend avgt 20 0.130 ± 0.003 s/op
which seems to indicate prepending to a List and then reversing it at the end is order of magnitude faster than keep appending to a Vector.
I took the solution from Jesper and added some aggregation to it on multiple run of the same code
def time[R](block: => R) = {
def print_result(s: String, ns: Long) = {
val formatter = java.text.NumberFormat.getIntegerInstance
println("%-16s".format(s) + formatter.format(ns) + " ns")
}
var t0 = System.nanoTime()
var result = block // call-by-name
var t1 = System.nanoTime()
print_result("First Run", (t1 - t0))
var lst = for (i <- 1 to 10) yield {
t0 = System.nanoTime()
result = block // call-by-name
t1 = System.nanoTime()
print_result("Run #" + i, (t1 - t0))
(t1 - t0).toLong
}
print_result("Max", lst.max)
print_result("Min", lst.min)
print_result("Avg", (lst.sum / lst.length))
}
Suppose you want to time two functions counter_new and counter_old, the following is the usage:
scala> time {counter_new(lst)}
First Run 2,963,261,456 ns
Run #1 1,486,928,576 ns
Run #2 1,321,499,030 ns
Run #3 1,461,277,950 ns
Run #4 1,299,298,316 ns
Run #5 1,459,163,587 ns
Run #6 1,318,305,378 ns
Run #7 1,473,063,405 ns
Run #8 1,482,330,042 ns
Run #9 1,318,320,459 ns
Run #10 1,453,722,468 ns
Max 1,486,928,576 ns
Min 1,299,298,316 ns
Avg 1,407,390,921 ns
scala> time {counter_old(lst)}
First Run 444,795,051 ns
Run #1 1,455,528,106 ns
Run #2 586,305,699 ns
Run #3 2,085,802,554 ns
Run #4 579,028,408 ns
Run #5 582,701,806 ns
Run #6 403,933,518 ns
Run #7 562,429,973 ns
Run #8 572,927,876 ns
Run #9 570,280,691 ns
Run #10 580,869,246 ns
Max 2,085,802,554 ns
Min 403,933,518 ns
Avg 797,980,787 ns
Hopefully this is helpful
I like the simplicity of #wrick's answer, but also wanted:
the profiler handles looping (for consistency and convenience)
more accurate timing (using nanoTime)
time per iteration (not total time of all iterations)
just return ns/iteration - not a tuple
This is achieved here:
def profile[R] (repeat :Int)(code: => R, t: Long = System.nanoTime) = {
(1 to repeat).foreach(i => code)
(System.nanoTime - t)/repeat
}
For even more accuracy, a simple modification allows a JVM Hotspot warmup loop (not timed) for timing small snippets:
def profile[R] (repeat :Int)(code: => R) = {
(1 to 10000).foreach(i => code) // warmup
val start = System.nanoTime
(1 to repeat).foreach(i => code)
(System.nanoTime - start)/repeat
}
You can use System.currentTimeMillis:
def time[R](block: => R): R = {
val t0 = System.currentTimeMillis()
val result = block // call-by-name
val t1 = System.currentTimeMillis()
println("Elapsed time: " + (t1 - t0) + "ms")
result
}
Usage:
time{
//execute somethings here, like methods, or some codes.
}
nanoTime will show you ns, so it will hard to see. So I suggest that you can use currentTimeMillis instead of it.
While standing on the shoulders of giants...
A solid 3rd-party library would be more ideal, but if you need something quick and std-library based, following variant provides:
Repetitions
Last result wins for multiple repetitions
Total time and average time for multiple repetitions
Removes the need for time/instant provider as a param
.
import scala.concurrent.duration._
import scala.language.{postfixOps, implicitConversions}
package object profile {
def profile[R](code: => R): R = profileR(1)(code)
def profileR[R](repeat: Int)(code: => R): R = {
require(repeat > 0, "Profile: at least 1 repetition required")
val start = Deadline.now
val result = (1 until repeat).foldLeft(code) { (_: R, _: Int) => code }
val end = Deadline.now
val elapsed = ((end - start) / repeat)
if (repeat > 1) {
println(s"Elapsed time: $elapsed averaged over $repeat repetitions; Total elapsed time")
val totalElapsed = (end - start)
println(s"Total elapsed time: $totalElapsed")
}
else println(s"Elapsed time: $elapsed")
result
}
}
Also worth noting you can use the Duration.toCoarsest method to convert to the biggest time unit possible, although I am not sure how friendly this is with minor time difference between runs e.g.
Welcome to Scala version 2.11.7 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_60).
Type in expressions to have them evaluated.
Type :help for more information.
scala> import scala.concurrent.duration._
import scala.concurrent.duration._
scala> import scala.language.{postfixOps, implicitConversions}
import scala.language.{postfixOps, implicitConversions}
scala> 1000.millis
res0: scala.concurrent.duration.FiniteDuration = 1000 milliseconds
scala> 1000.millis.toCoarsest
res1: scala.concurrent.duration.Duration = 1 second
scala> 1001.millis.toCoarsest
res2: scala.concurrent.duration.Duration = 1001 milliseconds
scala>
adding on => method with name & seconds
profile[R](block: => R,methodName : String): R = {
val n = System.nanoTime()
val result = block
val n1 = System.nanoTime()
println(s"Elapsed time: ${TimeUnit.MILLISECONDS.convert(n1 - n,TimeUnit.NANOSECONDS)}ms - MethodName: ${methodName}")
result
}