--- title: "Nufflytics: Analyzing Blood Bowl matches from FUMBBL using Python" author: "Gertjan Verhoeven" date: '2022-03-20' summary: This blogpost is about **Blood Bowl**, a boardgame I started playing last year. The idea of this blog post is to showcase some possible analyses that can be done on the FUMBBL match data I've compiled. slug: blood-bowl-nufflytics draft: no categories: - Data science - Blood Bowl tags: - Python - Pandas baseurl: https://gsverhoeven.github.io header: image: headers/bloodbowl_dataset.png preview: no ---
This blogpost is about Blood Bowl, a strategic boardgame invented in the late 80’s, that I finally started playing last year. Blood bowl is a game of Fantasy Football, where fantasy team races (think “Orcs”, or “Elves”) are pitted against each other. Interestingly, the various teams (there are over 20 different ones) require different play styles, and not all team races are equally strong. On tournaments, this gives rise to various compensation schemes to make all teams “viable” for competition. There exists a lively tournament scene, with thousands of matches played each year.
The idea of this blog post is to showcase some possible analyses that can be done on the FUMBBL match data I’ve compiled. The idea is to make Blood Bowl data analysis (also know as Nufflytics, a term coined by Blood Bowler “Schlice” in reference to Nuffle, the god of Blood Bowl) easier and more accessible to others. I took inspiration from various sources, detailed at the end of this post. So lets dive in the world of Blood Bowl stats nerdery.
Since the previous blog post on FUMBBL data, I decided to make a separate Github repository fumbbl_datasets that contains the Python code to fetch and construct the FUMBBL datasets. You can either download the latest datasets manually, or clone the entire repo to your local drive, depending on your expertise and preferences.
The datasets are available both in CSV and HDF5. CSV would be the format of choice for Excel analysis, whereas the HDF5 format is suitable for scripted languages such as Python or R. Here we use Python, with the libraries Pandas
and plotnine
for data analysis and visualization. The code below assumes the datasets are locally stored at the location contained in the path_to_datasets
variable:
import pandas as pd
import numpy as np
import plotnine as p9
# point this to the location of the HDF5 datasets
path_to_datasets = '../../../../fumbbl_datasets/'
# FUMBBL matches
target = 'datasets/v0.2/df_matches.h5'
df_matches = pd.read_hdf(path_to_datasets + target)
# FUMBBL matches by team
target = 'datasets/v0.2/df_mbt.h5'
df_mbt = pd.read_hdf(path_to_datasets + target)
# FUMBBL inducements
target = 'datasets/v0.2/inducements.h5'
inducements = pd.read_hdf(path_to_datasets + target)
Let’s see what we’ve got! The pandas DataFrame df_matches
contains records for all matches played on FUMBBL between august 2020 and march 2022.
Since we have a proper datetime
type variable for each week (week_date
), we can use pandas
and plotnine
to plot the weekly game volume as a time series.
The introduction of the new Competitive division with BB2020 rules is marked by a vertical red line. I labeled the larger leagues as well a recent tournament I took part in myself.
res = (df_matches
.loc[(df_matches['week_date'] >= '2020-08-01' ) & (df_matches['week_date'] < '2022-11-25')]
.groupby(['week_date', 'week_number', 'division_name'])
.agg(
n_games = ('match_id', "count")
)
.reset_index()) # this adds the "group by" variables back as columns of res
(p9.ggplot(data = res, mapping = p9.aes(x = 'week_date', y = 'n_games', color = 'division_name'))
+ p9.geom_point()
+ p9.geom_line()
+ p9.expand_limits(y=[0,2000])
+ p9.geom_vline(xintercept = '2021-09-01', color = "red")
+ p9.theme(figure_size = (10, 5))
+ p9.ggtitle("Weekly game volume on FUMBBL august 2020 - march 2022"))
<ggplot: (-9223363302253724542)>
To check the dataset, I compared this plot with the plot of weekly game volumes that FUMBBL itself provides at https://fumbbl.com/p/stats. Both plots looked identical at the time of writing, so it seems that we have a complete dataset for the given period.
The effect of starting the new BB2020 Competitive division is clearly visible, with the weekly game volume almost doubling in september 2021. The first online NAF tournament using BB2020 rules is also visible, running for 6 weeks in October / November 2021.
We can also look at the percentage of matches that involve star players. I used the various plot aesthetics like symbol shape and size to encode the game volume and ruleset (BB2016 or BB2020 based).
divisions = ['Blackbox', 'Competitive', 'Online NAF Tournaments', 'Ranked', 'Regular_league']
res = (df_matches
.query("division_name in @divisions")
.groupby(['division_name', 'league', 'ruleset', 'ruleset_version', 'week_date'])
.agg(
n_games = ('match_id', 'count'),
perc_sp = ('has_sp', 'mean')
)
.reset_index()
.sort_values("n_games", ascending=False)
)
(p9.ggplot(data = res.query("n_games > 30"), mapping = p9.aes(x = 'week_date', y = 'perc_sp*100',
group = 'factor(division_name)', color = 'factor(division_name)'))
+ p9.geom_point(p9.aes(shape = 'factor(ruleset_version)', size = 'n_games'))
+ p9.expand_limits(y=[0,1])
+ p9.scale_size_area()
+ p9.geom_vline(xintercept = '2021-09-01', color = "red")
+ p9.ggtitle("Star player usage over time, by division/league")
+ p9.theme(figure_size = (10, 6))
+ p9.ylab("% matches with at least one Star Player"))
<ggplot: (-9223363302246313763)>
In above graph, the various online NAF Tournaments are clearly distinguished. Amorical Cup 2020 in summer 2020, Eur’Open Online in Nov/dec 2020, SteelBowl in Feb 2021, and LitBowl in May 2021 were all using BB2016 rules.
Through Googling and using the Wayback Machine, I was able to find the rulepacks of these tournaments. LitBowl featured “big budgets” (up to 1440K) and a requirement of only 10 regular players before inducement, this likely explains the large amount of Star Players in that tournament.
In contrast, in the GBFU tournament, the first online NAF tournament using the BB2020 rules, only some 15% of matches involved at least one star player.
For the main divisions on FUMBBL, ELO style coach ratings are available that are updated after each game. The coach rankings are explained on this help page.
According to the ELO ranking system, a coach rating difference of 40 should result in 85% wins for the higher ranked coach. Coaches of equal rating should have a win rate of 0.5 (with draws weighted at half point).
The range of coach rankings observed for a particular game tells us something about the relationship between skill and luck. If a game is pure luck, we will never observe large differences in coach rating, since the outcome will be determined by a coin flip, independent of coach skill.
On FUMBBL, coach ratings vary roughly between 125 and 175. What do we expect if a coach with a rating of 175 plays a coach of rating 145? Well, the rating difference is 30. According to the formula (assuming equal team strength and equal races), the expected win probability is 1/(1 + 10^0.75) = 85%, and the probability of loss is 15%.
Since our CR we obtained from the FUMBBL match result page is an overall coach rating (i.e. it ignores division), we can simply pool all matches from divisions where coach rating is tracked.
The match data contains a Coach Ranking Difference bin (category) that we can each to calculate the Win/draw/loss percentages for each category.
Let’s see what the actual percentages are:
main_divisions = ['Blackbox', 'Ranked', 'Competitive']
res = (df_matches[df_matches['division_name'].isin(main_divisions)]
.groupby(['cr_bin', 'team1_win'])
.agg(
n_games = ('cr_bin', "count"),
)
.reset_index()) # this adds the group by variable (now index) as a column
# add total games played within each bin
res['n_games_bin'] = res.groupby('cr_bin').n_games.transform('sum')
res['perc'] = res['n_games']/res['n_games_bin']
(p9.ggplot(res, p9.aes(x = 'factor(cr_bin)', y = 'perc', fill = 'factor(team1_win)'))
+ p9.geom_bar(position = "fill", stat = "identity")
+ p9.theme(axis_text_x= p9.element_text(rotation=90, hjust=1))
+ p9.ggtitle('probability of win/draw/loss as a function of Coach Rating difference')
)
<ggplot: (-9223363302350291624)>
(Note that I made the bins for large CR differences (greater than 10) wider to get more games per bin.)
From above graphs, we can conclude that the coach ratings work as expected, with large coach rating differences indeed showing high win rates for the higher ranked coach. From this we can infer that a highly skilled coach will win 9 times out of ten agains a below average coach. We call Blood Bowl a Strategy game for a reason!
With Blood Bowl 2020 also came a large change to passing the ball. Passing is no longer linked to the Agility statistics, but now has its own Passing (PA) stat. Overall, passing became riskier, and high agility teams do not automatically have good passing stats. For example, only a High Elf thrower has a PA of 2+, whereas the rest of the players have a PA of 4+ or higher. On the Dark Elf team, the player with the best PA stat is the runner, with a PA of 3+, without a built in re-roll. So we can expect quite some changes in the number of completions per match. For more detail I refer to a nice post by king_ghidra at Blood Bowl Strategies.
Let’s have a look!
divisions = ['Ranked', 'Blackbox', 'Competitive']
tv_bins = ['1.1M', '1.4M', '1.7M']
res = (df_mbt[df_mbt['division_name'].isin(divisions)]
.loc[df_mbt['tv_bin'].isin(tv_bins)]
.query("mirror_match == 0 & has_sp == 0 & tv_bin in @tv_bins & division_name in @divisions")
.groupby(['division_name', 'ruleset_version', 'week_date', 'tv_bin'])
.agg(
avg_comp = ('home_comp', "mean"),
avg_pass = ('home_pass', "mean"),
avg_foul = ('home_foul', "mean"),
avg_block = ('home_block', "mean"),
avg_cas = ('home_cas', "mean"),
avg_rcv_cas = ('away_cas', "mean"),
n_games = ('race_name', "count")
)
.sort_values( 'n_games', ascending = False)
.reset_index()) # this adds the group by variables (now index) as a column
res = res.dropna()
(p9.ggplot(data = res.query('n_games > 10'),
mapping = p9.aes(x = 'week_date', y = 'avg_comp',
size = 'n_games', color = 'factor(division_name)', shape = 'factor(tv_bin)'))
+ p9.geom_point()
+ p9.scale_size_area()
+ p9.geom_vline(xintercept = '2021-09-01', color = "red")
+ p9.theme(figure_size = (10, 5))
+ p9.ggtitle("average completions per game BB2020 vs BB2016"))
/home/gertjan/venvs/requests_env/lib/python3.6/site-packages/plotnine/scales/scale_shape.py:85: PlotnineWarning: Using shapes for an ordinal variable is not advised.
<ggplot: (-9223363302350371093)>
divisions = ['Ranked', 'Blackbox', 'Competitive']
tv_bins = ['1.1M', '1.4M', '1.7M']
res = (df_mbt[df_mbt['division_name'].isin(divisions)]
.loc[df_mbt['tv_bin'].isin(tv_bins)]
.query("mirror_match == 0 & has_sp == 0 & tv_bin in @tv_bins & division_name in @divisions")
.groupby(['ruleset_version', 'race_name', 'tv_bin'])
.agg(
avg_comp = ('home_comp', "mean"),
avg_pass = ('home_pass', "mean"),
avg_foul = ('home_foul', "mean"),
n_games = ('race_name', "count")
)
.sort_values( 'n_games', ascending = False)
.reset_index()) # this adds the group by variables (now index) as a column
res = res.dropna()
(p9.ggplot(data = res.query('n_games > 10 & tv_bin == "1.1M"'),
mapping = p9.aes(y = 'reorder(race_name, avg_comp)', x = 'avg_comp',
size = 'n_games', group = 'factor(ruleset_version)',
color = 'factor(ruleset_version)'))
+ p9.geom_point()
+ p9.scale_size_area()
+ p9.ggtitle("average completions per game BB2016 vs BB2020 at 1.1M TV"))
<ggplot: (-9223363302350232432)>
Across the board we see a decrease in average completions per match. Note that this is for low team values, at around 1.1M, between 950K and 1250K.
Observations that stand out:
Also for fouling Blood Bowl 2020 brought some changes to the rules. The Sneaky Git skill became better, allowing a player to continue moving after the foul has been committed. The Black Orcs were added as a new team, that show fouling potential: they have access to cheap bribes, the Grab skill to set up a foul, and cheap goblin bruisers to quickly move around the pitch. And there was of course the swarming for the Underworld and Snotling teams, that provides a continuous supply of disposable players to foul with. For more detail I refer to a nice post by king_ghidra at Blood Bowl Strategies.
Let’s see how the stats were affected!
divisions = ['Ranked', 'Blackbox', 'Competitive']
tv_bins = ['1.1M', '1.2M', '1.3M', '1.4M']
res = (df_mbt[df_mbt['division_name'].isin(divisions)]
.loc[df_mbt['tv_bin2'].isin(tv_bins)]
.query("mirror_match == 0 & has_sp == 0 & tv_bin2 in @tv_bins & division_name in @divisions")
.groupby(['ruleset_version', 'race_name', 'tv_bin2'])
.agg(
avg_comp = ('home_comp', "mean"),
avg_pass = ('home_pass', "mean"),
avg_foul = ('home_foul', "mean"),
n_games = ('race_name', "count")
)
.sort_values( 'n_games', ascending = False)
.reset_index()) # this adds the group by variables (now index) as a column
res = res.dropna()
(p9.ggplot(data = res.query('n_games > 10'),
mapping = p9.aes(y = 'reorder(race_name, avg_foul)', x = 'avg_foul',
size = 'n_games', color = 'factor(ruleset_version)',
shape = 'factor(tv_bin2)'))
+ p9.geom_point()
+ p9.scale_size_area()
+ p9.ggtitle("average number of fouls per game BB2016 vs BB2020")
+ p9.ylab(""))
<ggplot:<PHONE_NUMBER>775)>
From this plot, we can see that BB2020 indeed shows increased fouling across the board. As expected, the Black Orcs are high up in the fouling charts, and we see large increases in fouling for Underworld, Goblins, Halflings and Snotlings. We can also see that as teams develop, fouling typically increases, possible related to developing a specialized fouling player with the sneaky git skill. For humans access to cheap agile halfling hopefulls with access to sneaky git increased fouling opportunity. In Orc teams goblins can forfill this role.
Last but not least, a win rate analysis. In a tournament setting, elaborate tiering systems are in place to compensate for differences in race strength, skills are selected from skill packs, and player casualties are forgotten with each match played with a “resurrected” fresh team. Furthermore, opponents are randomly assigned and must be played. Contrast this with the FUMBBL Competitive division: Here teams start with 1M gold and without any extra skills. Teams must be developed, like in a league, and opponents can be strategically chosen based on which race they play, their coach rating etc.
With all this in mind, I tried the impossible: to compare relative team strength as expected by the tournament tiers, with the observed win rates in the Competitive Division. With the Eurobowl 2022 in Malta coming up, I decided to approximate the conditions of that rulepack. Teams are created using 1.15M gold, as well as roughly 36 SPP worth of skills. This translates to 6 primary skills worth 20K, giving us a total team value of 1270K, say around 1.3M.
To correct for differences in coaching ability, I restricted the match selection for matches where coach ratings are not too different (< 10), and above 150. I excluded matches involving Star players, and mirror matches (I.e. Orcs vs Orcs). To distinguish the relatively small % differences in win rate, we need to have a bandwidth around 1.3M to get sufficient statistics for each team. I included confidence intervals to visualize the statistical uncertainty for the win rates.
# Function for computing confidence intervals
from statsmodels.stats.proportion import proportion_confint
divisions = ['Competitive']
tv_bins = ['1.2M', '1.3M', '1.4M']
res = (df_mbt[df_mbt['division_name'].isin(divisions)]
.loc[df_mbt['tv_bin2'].isin(tv_bins)]
.query('mirror_match == 0 & has_sp == 0 & CR_diff < 10 & coach_CR > 150')
.groupby(['race_name', 'ruleset_version', 'Malta_2022'])
.agg(
perc_win = ('wins', "mean"),
n_wins = ('wins', "sum"),
n_games = ('race_name', "count")
)
.query('n_games > 0')
.reset_index()) # this adds the group by variable (now index) as a column
res['lower_CI'], res['upper_CI'] = proportion_confint(
count = round(res['n_wins']).astype(int),
nobs = res['n_games'],
alpha = 0.05
)
(p9.ggplot(data = res.query('n_games > 30'),
mapping = p9.aes(x = 'reorder(race_name, -Malta_2022)', y = 'perc_win',
size = 'n_games', color = 'factor(Malta_2022)'))
+ p9.geom_linerange(p9.aes(ymin = 'lower_CI', ymax = 'upper_CI'), size = 1)
+ p9.geom_point()
+ p9.scale_size_area()
+ p9.coord_flip()
+ p9.geom_hline(yintercept = 0.5)
+ p9.ggtitle("FUMBBL BB2020 win rates around 1.3M"))
<ggplot: (-9223363302334269892)>
First off, I think the most important lesson here is that it is really difficult to compare win rates across such different settings.
But what info can we squeeze from this plot nevertheless:
It seems that Amazon and Underworld have higher FUMBBL win rates than expected based on their Malta 2022 tier.
What else do we got:
The analyses above hopefully give you some idea what can be with the rich FUMMBL data available.
One last application: The data can also be used to search for matches based on highly particular search criteria: for example, if you are interested in Snotling matches that induce Morg N Thorg and play against a skilled Elf coach. The match_id
can then be used to watch the replay on FUMBBL.
Finally, some acknowledgements. While writing this blog, I drew inspiration from several sources:


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EY MENA is a professional services network. I worked there as Senior Data Analyst supporting Assurance practices throughout the MENA region.
SQL for analytics in the real world with real world use cases. This course would be designed according to your needs. Have a session with me to understand your learning requirements and get started!
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May 26
Wherefore God also hath highly exalted him, and given him a name which is above every name: That at the name of Jesus every knee should bow, of things in heaven, and things in earth, and things under the earth.
Philippians 2:9-10
Once you make up your mind to keep the commands of Jesus and begin to allow the Word to dwell in you richly, the Name of Jesus will become far more powerful to you. It will become more than just a word. It will become a force that will cause every circumstance and every demon that tries to stand in your way to bow its knee to your command.
I tell you, the Name of Jesus works. There is far greater power in it than any of us have yet realized. My faith is so set on the authority of the Name of Jesus that there are times I just say, "In the Name" and the power of God comes on the scene.
In fact, I've discovered that the Name of Jesus--just the Name alone-- is effective when spoken by a Word-abiding believer. Revelation 19:13 says the Name of Jesus is the Word of God! So, when an evil spirit is trying to bring sickness, poverty, depression, or any other garbage into my household, I don't have to quote every scripture I know to stop him. I can just point my finger at him and say, "Jesus!" That's like throwing the whole Word of God in his face at one time!
Discover for yourself what the exalted Name can do. Begin to speak it with confidence and authority. There is power in the Name of Jesus.
Scripture Reading: Acts 3:1-16


High Priest Orven:
Good work! Now they shall rest in peace!
I give you blessings in the name of Einhasad. May we never have to deal with such a desecration again!
Now I must pray. Come back later.


I'm not sure about the name however. Secondary weapons are the handguns, doesn't seem fitting seeing as none of the requirements are directly classed as weapons. Also, it might come an assignments specially for handguns/secondary in the future, who knows.
IMHO, it should be called something more in the ways of:
- When all else fails
- Last resort
You get the idea :P
Post edited 1 x times, last by
T.I.A
Topicstarter
T.I.A
My Englisch is not so good the name "When all else fails" sounds great!
it is hard i know but is fun! i have a blast with it :) campers are doomed too get a eodbot finishing off ^^
I hope that it will come it is a assignment that you will not have in a second like "allrounder" and that sounds fun too do
for the allrounder i needed also a couple of weeks to do it.
Thx for responding
Meaurk
Global Moderator
I might be outdated, but since when is it possible to kill someone with a defibrillator? I never succeeded.
Topicstarter
T.I.A
I have 133 kills, you aim on his shoulders in his back and then keep pushing the buttom not push 1 time hold it when you push thats the key :)
ORFK
Global Moderator
damt DONT tell this everybody!
thats a secret! xD
otherwise its not important where you aim, you just have to know how you have to push/click ;-)
i like all the youTube Videos where small childerns try to describe you have to "shoot" with the defibrillator in their head and the hole video its not working xD
Topicstarter
T.I.A
xXJumpyXx wrote:
Seems like a good assignment.
I'm not sure about the name however. Secondary weapons are the handguns, doesn't seem fitting seeing as none of the requirements are directly classed as weapons. Also, it might come an assignments specially for HANDGUNS/secondary in the future, who knows.
Dont forget we dont have all handguns that battlelog shows us, we dont have the "M1911 S-TAC"
So if you do something like get ... with every handgun that does not work because we dont have this one :)
Topicstarter
T.I.A
Oh Come on, When are new assinments coming??
I hoped with the 3.3.0 version ):
I'm waiting, please answer
BrainFooLong
Administrator
T.I.A wrote:
Oh Come on, When are new assinments coming??
I hoped with the 3.3.0 version ):
I'm waiting, please answer
Chill, we never said a specific date for new assignments. Maybe in next release.

I hoped with the 3.3.0 version ):
I'm waiting, please answer
Chill, we never said a specific date for new assignments. Maybe in next release.
Yeah i know, but its bin more than a MONTH i think 2 MONTHS that we have new assignments...
and there are plenty sujestions
Meaurk
Global Moderator
We're still faster then DICE. :D
Topicstarter
T.I.A
Meaurk wrote:
We're still faster then DICE. :D
Thats a fact ^^
shooter-jonathan
Hey, I like to finish as much assignments as possible but 100 Kills with EOD Bot...
imho that´s far too much!
Can´t you change it to 10, 20 or max 50?

Maybe other AssignmentFans share my opinion??
Post edited 1 x times, last by
BrainFooLong
Administrator
BrainFooLong
Administrator
shooter-jonathan wrote:
Hey, I like to finish as much assignments as possible but 100 Kills with EOD Bot...
imho that´s far too much!
Can´t you change it to 10, 20 or max 50?

Maybe other AssignmentFans share my opinion??
It should be challenging and take a while. And 100 you can reach in a few hours on metro for example.
This is one of the easiest assignments imho.


We’re a studio in Poland with a team of great developers
who build first-rate mobile apps.
---
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---
We’re a studio in Poland with a team of great developers
who build first-rate mobile apps.
Michał Łukasiewicz is an idea guy. Starts 5 new things weekly. Workaholic. When he is on vacation, he works only part-time so that his employees never need to work overtime.
Daniel Makurat always thinks differently. His ideas often sound odd at first, but turn out to be really Bright in the end. Works mostly in iOS, but Ruby scars are still visible.
Piotr Mionskowski is a real Full Stack Developer, probably the only one who touched every possible technology and the default go-to person in case of problems. TDD fan that always asks ’Why?’.
Mateusz Klimczak - Trello guy, has everything planned ahead. Does the job of ten corporate line managers at once while running 3 iOS projects in the meantime.
Adam Bar is the web enthusiast. He is trying to convince everyone we wouldn't need mobile apps some day. A fan of order and structure.
Eliasz Sawicki is a world-class conference speaker to be. Eager to experiment with the new stuff. Mostly working in iOS, but not afraid of JavaScript fatigue.
Kamil Wysocki works in iOS. Has highly developed usability intuition. He is always trying to cover all cases while creating new stuff.
Agnieszka Olszewska joined the team to do big things in Big Data. Privately keen on big mountains. Always smiling.
Andrzej Zabost is an Android enthusiast and Kotlin fan. In spare time he crushes his enemies in video games.
Paweł Papkiewicz is a real DIY guy with wide experience of web development. A few codes and he will create a digital bike for you.
Ula Stankiewicz is an English and Social Media lover. She makes our brand bright. Can’t imagine her life without bike and chocolate.
Piotr Łupiński joined our team to do amazing stuff on Android. Deeply in love with design. In free time he enjoys architecture photography and keeping fit.


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The Singapore government has a well-deserved reputation for its long-term, forward looking approach to land use planning. In recent years however, infrastructure growth had struggled to catch up with rapid population growth. Why did Singapore’s system of long-term planning fall short?
How has Singapore’s ‘global city’ strategies affected equality outcomes in the country, and how should Singapore’s policy-makers should adjust these long-held strategies to safeguard equality in the country?
When a couple bought a shophouse that formerly accommodated a bookstore they used to visit, they seized the chance to create something special, marrying elements from the past and present.
1923 was a significant year for Singapore. It was the year the Singapore-Johor Causeway was completed, the year broadcastinging started in the Republic, and the year the country’s Prime Minister Lee Kuan Yew was born. It was also the year No. 5 Chatsworth Park was built.


The case takes place on Colorado, foot of the Rocky Mountains, located in a small town called Mizzurna Falls (a town with less than 2000 population).
Date: December 25th, Christmas morning AM 6:13 of the city's located in the suburbs of the woods at Barlow. The girl (Cathy Flannery) was found covered with blood with scars that people suppose that she was attacked by bears.
Cathy and Emma Rowland attend at the same high school, since last night Emma dissapeared, her mother Sarah Rowland had tried to search her.
The State police are above two incidents of relevance in the investigation started:
- This is the second city in widening the synthetic drug EPOC.
- The city since ancient times have the legend of the "Land of the Dead ritual journey"
Our hero, Matthew Williams had to find Emma and discover what is really happening in Mizzurna Falls.
Manufacturer's description:
One town reeproduced in whole 3D Polygon graphics, colorful action adventure game unfolds. Events do not always occur in the same time, or how to solve the mystery up to you! Beginning with a mysterious death case accomplished 16-year-old girl in rural America, to find Emma went missing, the case goes to step in Matthew is a hero.
Features:
- Third person perspective
- 3D graphics
- Mystery & terror themes.
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Color and Screen Calabration
Take a look into my thoughts on Design Workflow and the need to meet industry standards for correcting color output for Graphic Design
Learn MoreTake a look into my thoughts on Design Workflow and the need to meet industry standards for correcting color output for Graphic Design
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Defaulted to 5, set to higher if deploying particularly large/slow images that may take longer than 5 minutes to download and start


Well, I figured I'd write this down in here too while I'm here and high both at once.
I started smoking the wacky tobaccy the summer after freshman year.
I remember Maggie filled up a bong with stems she had left over and told me to smoke it, showed me how and said that it wouldn't get me high but it would put thc in my system so i'd get really baked the real time i do it.
but as i was walking home from her house, I began to feel as though the street was rising and I was walking upwards.
Now i feel like im floating and letting my body come down a bit from the air so that i can type on the keyboard. or something real nice like that.
I notice that even though i ponder things when im not high, that's it's nothing unusual for me, its easier for me to do it right now, and my thoughts flow easily, they just click and connect like a puzzle.
i guess the brain power must shift from all the parts of the brain to specific spots....like from the part that governs memory to the part that governs thought processes. I dunno.
Sometimes I actualy become aroused when I smoke weed, which is odd. When one thinks of marijuana one does not think of being horny.
I get the munchies bad. I eat everything in the house. Like I can't get full and my stomach is rumbling. I think if you have cancer or aids or some other chronic (no pun intended) disease that depletes your hunger and makes you underweight, this can be a good thing, along with the pain relief.

So when I smoke, I feel relaxed and light, and thinking about my fear just doesn't process in my mind, because it's too filled with the goodness of the leafy greens^^
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Microsoft PowerPoint is a presentation program and is a part of Microsoft Office. It is widely used by marketing people, educators, students, trainers etc to make Presentations/Slide Shows.
PowerPoint presentations consist of a number of individual pages or "slides". The "slide" is analogous to a page of presentation. Slides may contain text, graphics, movies, and other objects, which may be arranged freely on the slide. In PowerPoint, we have the facility of "Template" or "Slide Master" to have consistent style in complete presentation.
The presentation can be printed, displayed live on a computer or navigated through, at the command of the presenter. For larger audiences, the computer display is often projected using a video projector. Slides can also form the basis of web-casts. Speaker notes and handouts can be generated corresponding to each slide in Powerpoint.
Data from Microsoft Excel sheet and / or Microsoft Word document can be easily imported to the presentation. One or more presentations can be merged also.
Difference between PowerPoint Presentation and Word Document
Microsoft PowerPoint is used to make presentation whereas Microsoft Word is used to create document. In the presentation, you need to prepare slides and users are not required to read long pages. Presentations are interactive and interesting as compared to reading documents. The extension of word processing document is. DOC where as that of a presentation file is. PPT.


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Fremont Settlement Patterns and Demography. In Clear Creek Canyon Archaeological Project: Results and Synthesis, edited by Joel C Janetski, Richard K Talbot, Deborah E Newman, Lane D Richens, and James D Wilde, pp. 201–230. Museum of Peoples and Cultures Occasional Papers No. 7. Brigham Young University, Provo, Utah.
2000a
Fremont Farmers: The Search for Context. In The Archaeology of Regional Interaction: Religion, Warfare, and Exchange Across the American Southwest and Beyond, edited by Michelle Hegmon, pp. 275–293. University Press of Colorado, Boulder.
Talbot, Richard K, Shane A Baker, and Lane D Richens
2004
The Right Place - Fremont and Early Pioneer Archaeology in Salt Lake City. Museum of Peoples and Cultures Technical Series No. 03-07. Brigham Young University, Provo, Utah.
Talbot, Richard K, Lane D Richens, James D Wilde, Joel C Janetski, and Deborah E Newman
1999
Excavations of Icicle Bench, Radford Roost, and Lott’s Farm, Clear Creek Canyon, Central Utah. Museum of Peoples and Cultures Occasional Papers No. 4. Brigham Young University, Provo, Utah.
2000
Excavations at Five Finger Ridge, Clear Creek Canyon, Central Utah.
Thomas, David Hurst
1981
How to Classify the Projectile Points from Monitor Valley, Nevada. Journal of California and Great Basin Anthropology 3(1):7–43.
1986
Points on Points: A Reply to Flenniken and Raymond. American Antiquity 51:619–627.
Thulman, David K
2012
Discriminating Paleoindian Point Types from Florida Using Landmark Geometric Morphometrics. Journal of Archaeological Science 39(5):1599–1607. DOI:10.1016/j.jas.2012.01.004.
2019
A Typology of Florida Fluted Points Using Landmark-Based Geometric Morphometrics. PaleoAmerica 5(2):181–190.
Towner, Ronald H, and Miranda Warburton
1990
Projectile Point Rejuvenation: A Technological Analysis. Journal of Field Archaeology 17(3):311–321. DOI:10.1179/009346990791548231.
Voegelin, Erminie Wheeler
1938
Tübatulabal Ethnography. Anthropological Records. University of California Press, Berkeley.
Waguespack, Nicole M, Todd A Surovell, Allen Denoyer, Alice Dallow, Adam Savage, Jamie Hyneman, and Dan Tapster
2009
Making a Point: Wood- Versus Stone-Tipped Projectiles. Antiquity 83(321):786–800. DOI:10.1017/S0003598X00098999.
Weder, Dennis G
1980
Lithic Artifacts and Debitage. In Cowboy Cave, edited by Jesse Jennings, pp. 39–48. University of Utah Anthropological Papers No. 104. University of Utah Press, Salt Lake City.
Wenger, Etienne
1998
Communities of Practice: Learning, Meaning, and Identity. Vol. 9. Cambridge University Press, Cambridge.
Whittaker, John C
1994
Flintknapping: Making and Understanding Stone Tools. University of Texas Press, Austin.
Whittaker, John C, and William D Bryce
2017
Hunting Technologies. In The Oxford Handbook of Southwest Archaeology, edited by Barbara Mills and Severin Fowles, pp. 627–644. Oxford University Press, Oxford.
Whittaker, John C, and Kathryn A Kamp
2016
Flint from the Ancestors: Ritualized Use of Stone Tools in the Prehistoric Southwest. In Archaeological Variability and Interpretation in Global Perspective, edited by Alan P Sullivan III and Deborah Irene Olszewski, pp. 267–289. University Press of Colorado, Boulder.
Wiessner, Polly
1983
Style and Social Information in Kalahari San Projectile Points. American Antiquity 48(2):253–276. DOI:10.2307/280450.
Wilde, James D, and Reed A Soper
1999
Baker Village: Report of Excavations, 1990-1994. Museum of Peoples and Cultures Technical Series No. 99-12. Brigham Young University, Provo, Utah.
Wilke, Philip J, and J Jeffrey Flenniken
1991
Missing the Point: Rebuttal to Bettinger, O’Connell, and Thomas. American Anthropologist 93:172–73.
Woods, Aaron R
2004
Spotten Cave Re-Visited: A Re-Analysis of the Projectile Point Assemblage. Utah Archaeology 17:14–31.
2009
Distribution, Function, and Value of Parowan Valley Projectile Points. Unpublished PhD thesis, Department of Anthropology, Brigham Young University, Provo, Utah.
Woods, James C
1988
Projectile Point Fracture Patterns and Inferences about Tool Function. Idaho Archaeologist 11(1):3–6.
Wormington, Hannah Marie
1955
A Reappraisal of the Fremont Culture: With a Summary of the Archaeology of the Northern Periphery. Proceedings, No. 1. Denver Museum of Natural History, Denver.
Young, D Craig, Kimberley Carpenter, Daron Duke, Eric Wohlgemuth, and Teresa Wriston
2008
Mosquito Willies (42To137): Prehistoric Data Recovery in Locus 3, Tooele County, Utah. Manuscript on file Hill Air Force Base Environmental Management Restoration Branch.


| Pending User Flowspace requests: {{num_user_reqs}} |
| Pending Admin Flowspace requests: {{num_admin_reqs}} |
| Pending Promote to Admin requests: {{num_admin_promotes}} |
Owned Flowspaces:
| {{fs}} |
Administrative Rules:
| Experiment | Opted FlowSpace |
|---|---|
| {{fs.opt.experiment.project_name}} : {{fs.opt.experiment.slice_name}} | {{fs}} |


User Credits
Overview
The first thing to point out is that User Credit is OPTIONAL - you do not need to implement user credits unless you want to.
What is a User Credit?
User Credit is a number, representing a currency amount, associated with a registered user. You can sell your customer user credits OUTSIDE of ABPro then enter their number of credits into ABPro. Now when they go to make a booking, the cost of the booking is deducted from their credits.
Why use User Credit?
A few reasons:
- If you want to sell your customer the ability to make multiple bookings without paying each time. For example, selling a package of 5 appointments.
- Quantity discounting. You can sell a package of 10 appointments for less than the cost of 10 individual appointments. This could be done for example by selling $100 of credits for $75.
- Support additional payment gateways, for example sell credits through Virtumart or an existing gateway, then just add the credits into ABPro.
Limitations
- ABPro is not a banking system and Soft Ventures will not be liable for any discrepancies.
Limited activity logging will be maintained but NOT at an auditing level one would find in financial system software.
- PayPal must be enabled to turn on the financial parts of the screens.
- Credits are purchased outside of ABPro (PayPal buy now or Virtumart, etc.) and entered manually by Admin.
- A resource can have only one rate, that is you cannot offer a different rate for customers paying by PayPal vs paying by credits. Discounting would be handled outside ABPro when credits are purchased (ex: sell $100 worth of credits for $75)
- No partial cancel, ie: no cancel 1 seat of 3, cancel all and reorder.
- If a user needs to pay part via PayPal (his credit balance was not enough) and he bails out of PayPal without paying, he would need to contact admin to get his credits restored manually.
- The credit system only applies to the public booking screens and Front-Desk booking screen. Admin manually setting a booking to 'accepted' will have no effect on the user's credit balance. If admin sets a booking to 'canceled', and the booking was paid all or in part by user credit, the credit is re-instated. If part was paid by PayPal, that will be added to the user's credit balance - no PayPal refunds are ever initiated by ABPro.
- Not compatible with manual booking acceptance (Auto-Accept = No)
Toolbar Icons
Remove: Deletes one or more user credit accounts.
Edit: Opens the User Credit Edit screen to allow you to view and modify the credit balance and/or view account activity.
New: Opens the New User Credit screen to allow you to add a new user credit account.
Help: Opens this help screen.
List Area
ID: Unique ID for the category.
User ID: Joomla User ID of the user.
Name: User's name.
User's Credit Balance: Shows the current balance for the user.
Click on the User ID link to open the edit screen.


The Gini coefficient is a measure of inequality. The coeeficient can change in
response to policy decisions, national and international events, and
economic factors.
Line charts that visualize those changes for many states at a time
are challenging to read. The goal of this project is to create a
tool for displaying changes in the Gini coefficient between any two (consecutive or non-consecutive)
years.
This example shows the changes in Gini coefficients from 2010 to 2014.
Note that only a subset of nations have Gini values recorded in both years; the others
are not shown.


Username:
First name:
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Update profile
Update profile picture
Update profile as admin
Update profile picture
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Homepage


| Method | Defined by | ||
|---|---|---|---|
|
Dates()
Constructor, throws an error if called, as this is an abstract class.
| Dates | ||
|
addDays(d:Date, x:int):Date
[static]
Adds days to a date instance.
| Dates | ||
|
addHours(d:Date, x:int):Date
[static]
Adds hours to a date instance.
| Dates | ||
|
addMinutes(d:Date, x:int):Date
[static]
Adds minutes to a date instance.
| Dates | ||
|
addMonths(d:Date, x:int):Date
[static]
Adds months to a date instance.
| Dates | ||
|
addSeconds(d:Date, x:int):Date
[static]
Adds seconds to a date instance.
| Dates | ||
|
addYears(d:Date, x:int):Date
[static]
Adds years to a date instance.
| Dates | ||
|
roundTime(t:Date, span:int, roundUp:Boolean = false):Date
[static]
Rounds a date according to a particular time span.
| Dates | ||
|
timeSpan(t:Date, s:Date):int
[static]
Given two dates, returns a measure of the time span between them.
| Dates | ||
|
timeStep(span:int):Number
[static]
Returns the number of milliseconds needed to step one time step
forward according to the given time span measure.
| Dates | ||
|
toUTC(d:Date):Date
[static]
Given a date, returns a date instance of the same time in Univeral
Coordinated Time (UTC).
| Dates |
| Constant | Defined by | ||
|---|---|---|---|
| DAYS : int = -2 [static] Constant indicating a time span on the order of days.
| Dates | ||
| HOURS : int = -3 [static] Constant indicating a time span on the order of hours.
| Dates | ||
| MILLISECONDS : int = -6 [static] Constant indicating a time span on the order of milliseconds.
| Dates | ||
| MINUTES : int = -4 [static] Constant indicating a time span on the order of minutes.
| Dates | ||
| MONTHS : int = -1 [static] Constant indicating a time span on the order of months.
| Dates | ||
| MS_DAY : Number = 86400000 [static] Number of milliseconds in a day.
| Dates | ||
| MS_HOUR : Number = 3600000 [static] Number of milliseconds in an hours.
| Dates | ||
| MS_MIN : Number = 60000 [static] Number of milliseconds in a minute.
| Dates | ||
| SECONDS : int = -5 [static] Constant indicating a time span on the order of seconds.
| Dates | ||
| WEEKS : int = -10 [static] Constant indicating a time span on the order of weeks.
| Dates | ||
| YEARS : int = 0 [static] Constant indicating a time span of one or more years.
| Dates |
| Dates | () | constructor |
public function Dates()
Constructor, throws an error if called, as this is an abstract class.
| addDays | () | method |
public static function addDays(d:Date, x:int):Date
Adds days to a date instance.
Parametersd:Date — the date
|
|
x:int — the number of days to add (can be negative to subtract)
|
Date — a new Date representing the new date and time
|
| addHours | () | method |
public static function addHours(d:Date, x:int):Date
Adds hours to a date instance.
Parametersd:Date — the date
|
|
x:int — the number of hours to add (can be negative to subtract)
|
Date — a new Date representing the new date and time
|
| addMinutes | () | method |
public static function addMinutes(d:Date, x:int):Date
Adds minutes to a date instance.
Parametersd:Date — the date
|
|
x:int — the number of minutes to add (can be negative to subtract)
|
Date — a new Date representing the new date and time
|
| addMonths | () | method |
public static function addMonths(d:Date, x:int):Date
Adds months to a date instance.
Parametersd:Date — the date
|
|
x:int — the number of months to add (can be negative to subtract)
|
Date — a new Date representing the new date and time
|
| addSeconds | () | method |
public static function addSeconds(d:Date, x:int):Date
Adds seconds to a date instance.
Parametersd:Date — the date
|
|
x:int — the number of seconds to add (can be negative to subtract)
|
Date — a new Date representing the new date and time
|
| addYears | () | method |
public static function addYears(d:Date, x:int):Date
Adds years to a date instance.
Parametersd:Date — the date
|
|
x:int — the number of years to add (can be negative to subtract)
|
Date — a new Date representing the new date and time
|
| roundTime | () | method |
public static function roundTime(t:Date, span:int, roundUp:Boolean = false):Date
Rounds a date according to a particular time span. Date values are rounded to the minimum date/time value of the time span (the first day in a year, month, or week, or the beginning of a day, hours, minute, second, etc).
Parameterst:Date — the date to round
|
|
span:int — the time span to which the date should be rounded, legal
values are YEARS, MONTHS, WEEKS, DAYS, HOURS, MINUTES, SECONDS, or
MILLISECONDS
|
|
roundUp:Boolean (default = false ) — if true, the date will be rounded up to nearest value,
otherwise it will be rounded down (the default)
|
Date — a new Date representing the rounded date and time.
|
| timeSpan | () | method |
public static function timeSpan(t:Date, s:Date):int
Given two dates, returns a measure of the time span between them.
Parameterst:Date — the first date to compare
|
|
s:Date — the second date to compare
|
int — an integer value indicating the time span between dates. If
the return value is positive, it represents the number of years
between dates. Otherwise, the return value is one of MONTHS, DAYS,
HOURS, MINUTES, SECONDS, or MILLISECONDS.
|
| timeStep | () | method |
public static function timeStep(span:int):Number
Returns the number of milliseconds needed to step one time step forward according to the given time span measure.
Parametersspan:int — the time span for which to return a time step value.
Legal values are any positive numbers (representing years) or DAYS,
HOURS, MINUTES, SECONDS, and MILLISECONDS. Note that the MONTHS
time span is not supported and will result in a zero return value.
|
Number — the number of milliseconds needed to more one time step
ahead according to the input time span. For years (positive
integer input), this step is the nearest power of ten less than the
input value.
|
| toUTC | () | method |
public static function toUTC(d:Date):Date
Given a date, returns a date instance of the same time in Univeral Coordinated Time (UTC).
Parametersd:Date — the date to convert
|
Date |
| DAYS | constant |
public static const DAYS:int = -2
Constant indicating a time span on the order of days.
| HOURS | constant |
public static const HOURS:int = -3
Constant indicating a time span on the order of hours.
| MILLISECONDS | constant |
public static const MILLISECONDS:int = -6
Constant indicating a time span on the order of milliseconds.
| MINUTES | constant |
public static const MINUTES:int = -4
Constant indicating a time span on the order of minutes.
| MONTHS | constant |
public static const MONTHS:int = -1
Constant indicating a time span on the order of months.
| MS_DAY | constant |
public static const MS_DAY:Number = 86400000
Number of milliseconds in a day.
| MS_HOUR | constant |
public static const MS_HOUR:Number = 3600000
Number of milliseconds in an hours.
| MS_MIN | constant |
public static const MS_MIN:Number = 60000
Number of milliseconds in a minute.
| SECONDS | constant |
public static const SECONDS:int = -5
Constant indicating a time span on the order of seconds.
| WEEKS | constant |
public static const WEEKS:int = -10
Constant indicating a time span on the order of weeks.
| YEARS | constant |
public static const YEARS:int = 0
Constant indicating a time span of one or more years.


Integrating factor
If the differential equation is in the form of
\[
\frac{dy}{dt} + ay = g(t)
\]
where \( a \) is a constant and \( g(t) \) is a constant or an expression containing \( t \) but not \( y \).
For example \( \frac{dy}{dt} + 5y = 1 - t \). Quindi \( g(t) \) è una funzione nella sola variable \(t\).
Then, Leibniz discovered that the if the differential equation is multiplied by a certain function \( \mu(t) \), then
the equation is converted into one that is immediately integrable by using the product rule for derivatives. The function
\( \mu(t) \) is called integrating factor and our main task in this section is to determine how to find it for
a given equation.


id mileage i x 5297.0 6299.0 0.00000 1.0000 5297.0 10479. 0.00000 2.0000 5297.0 15201. 0.00000 3.0000Note that this is from the output of the program. The data are saved directly to the
.dta
file. The first reading of Bus 5297 had an odometer of
6299, which is the second category (x=1
).
Bus 5297 engine was replaced once, and farther down in the data this looks like:
5297.0 1.5256e+005 0.00000 30.000 5297.0 1.5510e+005 1.0000 31.000 5297.0 4770.0 0.00000 0.00000The bus is replaced when
x=31
and the next odometer reading, after
subtracting the reading at replacement (which is in the header at row r2mi
,
the sixth row), is 47770 miles more, which means x = 0
.
| Variables | ||
| ext | const | |
| files | const | |
| groups | const | |
| Mxjmps | const | |
| mxmiles | const | |
| Nbins | const | |
| outdir | const | |
| SVnames | const | |
| Functions | ||
| main | ||
| read | ||
| Enumerations | ||
| Anonymous enum 1 | Read Rust's data into a Stata data file that can be read into niqlow This is a general version of the simpler readall.ox file which reads only group 4 buses This version fixes an error that used the purchase month instead of the first month Mileage jumps that are out of bounds are corrected in this version using a recursive adjustment The fix in mileage jumps reconciles the one difference between my code and A & M 2002 in group 4 This file is part of niqlow. | |
| Anonymous enum 2 | ||
| Anonymous enum 3 |


Sara Kane
Welcome to my portfolio!
This website is a portfolio for my first week of projects at Epicodus.
Projects
Wikipedia Rebuild
- Technologies used: Bootstrap, HTML, CSS
- Rebuild of Wikipedia's frontpage
- Created to practice Bootstrap
Bootstrap Vacation
- Technologies used: Bootstrap, HTML, CSS
- Website about a place I'd like to go on vacation
- Created to practice Bootstrap
Week-in-Review
- Technologies used: Bootstrap, HTML, CSS
- Summary of concepts learned during the first week at Epicodus
- Created to practice Bootstrap
Animal Shelter
- Technologies used: HTML, CSS
- Fake animal shelter website
- Created to practice styling with stylesheets
Resort Website
- Technologies used: HTML, CSS
- Fake resort website
- Created to practice branching and merging in git
My First Webpage
- Technologies used: HTML, CSS
- HTML, CSS and GitHub pages practice
Cookie Recipe
- Technologies used: HTML
- Practice correcting indentation and spacing of HTML code
Recipe Blog
- Technologies used: Markdown
- Practice using Markdown
Hello World
- Practice using Git
About Me
Background:
Education:
Bachelor of Science in Computer Science Portland State University
Job Experience:
Level 3 Computer TechnicianCompuCom Systems - under contract to Intel
Current interests, hobbies, and skills:
Hobbies and interests:
- Reading
- Board games
- Web Development
Skills:
- HTML and CSS
- MS Office
- Communication Skills
- Critical Thinking
- Teamwork


| <xml> ::= | <element> | <escape> |
| <xml> ::= | text - {text {'<' | '&'} text} |
| <xml> ::= | '<!--' {text - {text '-->' text}} '-->' |
Description
This is all the forms that are allowed inside an XML <element>.
Parsing
Text here is represented as a xdom:string tree node with type "xml".
An escaped entity is represented as a xdom:name tree node with kind "xml".
A XML comment is represented as a xdom:string tree node with kind "comment".
Execution
Execution of XML text results in a rendering of that text. Three special XML characters are escaped: < becomes <, > becomes >, and & becomes &. Names are replaced with the string value of the name. These name strings are also escaped for the three special XML characters.


Ramon strengthens into severe tropical storm ahead of landfall
Severe Tropical Storm Ramon (Kalmaegi) has maximum winds of 100 km/h and gustiness of up to 125 km/h as of 5 pm on Monday, November 18
by Acor ArceoSubscribe
Email Address
Newsletters
- Glenda Gloria's Take this Tuesday
- Huddle by Miriam Grace Go
- The Newsbreak Agenda by Chay Hofileña
- Sometimes, all you need is video by Lilibeth Frondoso


New Journal Submission
Artical Type:
*
Main Subject Area of Artical:
*
Algorithms
Artificial Intelligence
Circular Economy
Computer Vision
E-Commerce
Hospitality, Travel, and Tourism Management
Intelligent Automation
Marketing
Mobile Apps
Mobile Payment Systems
Sensor Technology
Virtual Reality
Web Technologies
Full Title of Journal:
*
Author:
*
Email:
*
Corresponding Author(s):
*
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*
This is transparent
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Task
Add
All Task
Total: {{allCount()}}
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{{task.description}}
Total: {{allCount()-contarCompletas()}}
Task Completed
Total: {{contarCompletas()}}


| Microsoft Typography | ...on the Web | ...embedding | Microsoft WEFT 3 | |||
| WEFT 3 release notes | |||
|
The page analysis process is initiated by clicking the 'Analyze pages!' button located at the bottom right of the screen. Please note that the analysis may take a long time. If practical you may prefer to analyze a local hard disk copy of your site, as this will be faster.
Microsoft WEFT uses the installed version of Microsoft Internet Explorer to analyze the font usage of your pages. Each Web page listed is individually loaded into Internet Explorer for analysis. The characters used on each page are identified in terms of their font, style and Unicode value. For this reason it is important that all your first choice fonts are installed prior to analysis. It is also important to examine the pages using Internet Explorer, to ensure that text is properly displayed in the intended font.
Options on this screen Skip analysis: Selecting this option will result in WEFT skipping the page analysis process. Back: Use this button to return to the 'Add Web pages' screen. Next: Click here to start the page analysis and move on to the 'Fonts to embed' screen. Cancel: Clicking this button will exit the WEFT wizard and return you to the main WEFT screen.
|
|||
| WEFT 3 release notes | |||
| Microsoft Typography | ...on the Web | ...embedding | Microsoft WEFT 3 | |||


I find solutions to customer problems using graphic design, web development and technical support. I create website graphics, popups, landing pages and anything that can help bring their ideas to life. My technical support ability allows me to fix issues that might be going on with a website’s code or third-party integration. I pocess a grasp and understanding in design, modern web technologies, responsive frameworks, social media, browser compatibility and limitations, SEO best practices, security and an open mind for keeping up with the always-changing technologies. I enjoy using multiple mobile & computer operating systems to help with compatibility and consistency throughout. I'm a creative-minded individual capable of coming up with new concepts and ideas to solve problems while collaborating with a team or individual.


I am happy to get a huge update of the BirdWatch application out of the way. The changes are much more than what I would normally want to work on for a single article, but then again there is enough interesting stuff going on in this new version for multiple blog articles to come. Initially this application was only meant to be an exercise in streaming information to web clients. But in the meantime I have noticed that this application can be useful and interesting beyond being a mere learning exercise. Let me explain what it has evolved to:
BirdWatch is an open-source real-time tweet search engine for a defined area of interest. I am running a public instance of this for software engineering related tweets. The application subscribes to all Tweets containing at least one out of a set of terms (such as AngularJS, Java, JavaScript, MongoDB, Python, Scala, …). The application receives all those tweets immediately through the Twitter Streaming API. The limitation here is that the delivery is capped to one percent of all Tweets. This is plenty for a well defined area of interest, considering that Twitter processes more than 400 million tweets per day.
Here is how it looks like:
As you can see above, there is now a search bar which allows to narrow down the selection tweets the application reasons about. Any search result is bookmarkable, making it easy not only to revisit the search at a later time but also to pass around the link. A link represents the result for the query at the time of opening the link, not at the time of bookmarking it. The application will then load a user-selectable amount of recent tweets matching the search criteria plus any new match for as long as the browser window is open.
Here are some examples.
You’re a Java developer and you’re looking for a new job? Try this search: java (job OR hiring)
You want to limit your job search to London? No problem. Here you go: java (job OR hiring) london
You’re interested in tweets about Python the language but not about Monty Python: python -monty
Well, you get the idea. AND is the standard operator, it is applied unless OR is specifically used between two terms.
You can download the application and run it for a different area of your choosing. For myself I’ve been running an instance that listens to tweets related to U.S. politics, an area I have been interested in ever since I lived in Washington DC in 2009/2010. Usually I have found that a quick look at the application before watching video podcasts of my favorite political shows would show me what is then talked about in the shows. I might run the application publicly for this interest area at some point as well, but for that I want to have more sophisticated analytics and charts in place. I have plenty of ideas in that area, but for now I want to get this out instead of working on the project by myself for another couple of weeks. I am also looking for contributions here, particularly in the area of great looking and useful charts.
So how can this be improved in the future? A stream of Tweets filtered by topic area is a rich data set; the current charts really only scratch the surface of what is possible here. I would like to visualize the most often mentioned links so that I can see at one glance which newspaper articles and other links are most talked about. This analysis could even be weighed by how many followers the person tweeting about a link has. After all, more followers mean that more people will be exposed to the link. Also there could be a visualization about how tweets develop over time. What are the characteristics of people retweeting something? Is it picked up by someone with many followers and then later on by many people with few followers? The other way around? Get involved, there are plenty of options on how to work with the data.
You can find a brief overview of the technology in the project description on Github.
In the future I will probably write some articles further explaining different parts of the application. Let me know what you are particularly interested in.
Alright, this is it for today. Until next time. Matthias


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We believe in coming up with original ideas and turning them into digital work that is both innovative and measurable. Taking on thought-provoking projects that challenge us creatively and make us go the extra mile is what we consider a way of life.
find out moreWe are relentless in moving boundaries and carry out this spirited attitude into digital solutions. Digital solutions that engage, inspire and make you think. Make you curious.
discoverWe're obsessed with growth both artistic and organic. But what fun is learning about a new technique if you can't share it with an audience? Here we go into a little more detail about current web standards to help others trying to get a better grasp on the wide world of start-ups.
read articlesFor projects, ideas, collaborations or anything else don’t hesitate to get in touch with us. Or just say hello.
get in touch


Loops
Problem 3. Min/Max of sequence
Write a script that finds the max and min number from a sequence of numbers.
Note: separate numbers with comma.
Sequence of numbers:
Go
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You may add tooltips to both the trigger and actions... Note: you can easily open a dialog with a Fab action.


The Ford C-4 is a strong 3 speed automatic transmission used from 1964 until 1981 in Ford F-series trucks and Broncos. It is 17 inches long and has 11 bolts holding on the pan. Transmission 1st 2nd 3rd 4th 5th Rev C-4 2.46 1.46 1.00 NA
The Ford C-4 is a strong 3 speed automatic transmission used from 1964 until 1981 in Ford F-series trucks and Broncos. It is 17 inches long and has 11 bolts holding on the pan.


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TEENAGE MUTANT NINJA TURTLES A film review by David M. Jones Copyright 1990 David M. Jones
This is a somewhat rambling review of the TEENAGE MUTANT NINJA TURTLES movie. Be warned that though there are no particularly grievous spoilers in this article, there may be enough to remove a little of the joy of seeing the movie from an unspoiled perspective. The most important thing to learn from this article is that the movie is well worth seeing, at least if you like the comic book. Now that you know that, maybe you want to hit 'n' now.
Last night I was privileged to attend the world premiere of TEENAGE MUTANT NINJA TURTLES, hosted by MIT's Lecture Series Committee. I was tempted, upon getting out of the movie at about 2:45 AM, to rush directly to my terminal and send a review off, but luckily the call of my bed was too great, so I went home to sleep instead, I say "luckily" because this extra time has allowed me to give the movie the consideration it deserves. Here, then, are some of my impressions of the movie. I've tried to keep spoilers to a minimum, and to warn you when they are coming. However, I haven't felt the need to be too diligent in this regard, since anyone familiar with the comic books won't find the plot too surprising.
Overall, I was quite impressed with the movie. I found it to be a reasonably faithful adaptation of the comic book. In particular, they left the characters largely intact. Raphael's uncontrolled rage and loner tendencies, Leonardo's leadership (particularly his concern for the team as a team, as well as individuals), and Casey's sometimes warped quixotism are all apparent in the movie. There are differences, of course. April is reworked into a gutsy and intelligent investigative reporter, but I can't help but think the changes are for the better. Then, too, I didn't get much of a sense of Micheangelo and Donatello as individuals. In fact, now that I think of it, I'd say their main function was as comic relief to offset the heavier interaction between the Raphael-Leonardo and April-Casey pairs. For example, with one questionable exception, there is no indication of Donatello's considerable technical prowess.
Unfortunately, one character doesn't come off very well at all, namely Splinter. The movie industry has made yet another attempt to portray "inscrutable oriental wisdom," and they've failed yet again. In fact, I'm tempted to say that Splinter comes across as a bit of a buffoon. Part of this was due to technical failings. For example, the only parts of the movie I can remember cringing at were the flashback scenes describing Splinter's life with his Master and his discovery of the turtles. The stop-action animation (if that's the term I want) was ill-suited to the ostensibly serious tone of Splinter's narrative. In particular, the scenes showing him learning the martial arts by mimicking his master from his cage were absurd. All in all, the movie would be improved if these scenes were replaced by straight narrative without the visual aides. However, a bigger part of the problem was the approach they took to the character. Two symptoms of the problem come to mind: (1) Splinter had a goatee, and (2) he spoke with a stereotypical Japanese accent. Given the inherent problems in making a believable character out of a giant talking rat who is also a martial arts master, I think that including these particular cliches was a lamentable mistake. Among other things, this approach made his character far too rigid and inflexible. But enough of that. Let's move to a more positive note.
The turtle costumes were quite satisfactory (even if the faces, particularly the mouths, did remind me a little too strongly of Kermit the Frog occasionally :-)). The costumes weren't so unwieldy as to interfere with the movement of the actors. (In fact, the only thing I can remember being distracted by is that the shell on their stomachs seemed too flexible. I distinctly saw it bend once or twice.) Nor were they so garish as to distract the eye from the movie. In short, if you can suspend your disbelief enough to accept the presence of the turtles in the comic books, you shouldn't have any trouble accepting them in the movie.
I was quite impressed with the screenplay. They managed to rearrange the plot without doing gratuitous damage to the the comic book. In fact, if anything, I'd say the movie is more tightly plotted than the comic book. Also noteworthy is the fact that many scenes are lifted from the book with practically no change. However, there were major changes in the arrangements of the scenes. If anyone is interested in knowing what some of them are, drop me a line.
One thing that might bother purists is that it is apparent that the violence of the book has been toned down considerably, probably in order to appeal to younger viewers.  (Thus, Shredder is the rival himself, not the rival's younger brother.) Similarly, Splinter does not send the Turtles to kill Shredder, as he does in the book. I suspect that both of these changes were made to avoid sticky questions about the morality of Splinter, the Turtles, and Splinter's master, and to keep the overall tone of the movie light. Similarly, the fight scenes lacked the intensity of the book. There was no blood dripping off sais in this movie. (Incidentally, I'll let someone else comment on the authenticity of the fight scenes. I'm not qualified to judge.)
Purists might also be concerned about the portrayal of the Turtles. Yes, there is one scene where one of the Turtles is riding a skateboard. And yes, they are obsessed with pizza. However, neither of these things is obnoxiously handled. A more serious flaw is the attitude they take toward Splinter and the art of the ninja in general. I can't speak for the TMNT cartoon or for the "Adventures of the TMNT," but I feel confident in stating that the Turtles I am familiar with would not wander off to order a pizza while Splinter was attempting to teach them. I'm also not entirely satisfied with their behavior during the fights. They're too cocky, too showy, not grim enough. I suspect this was also intended to keep the mood of the movie from getting too heavy. However, on the whole these are the same wise-cracking adolescents, stewed in the popular culture of the day, that I know from the books. I must admit that their voices were a little higher that I had expected, but then again, they are supposed to be 15 years old. (I'm going to gloss over the difficulties involved in such analogies between the TMNT and human adolescents.)
So, in summary, I was impressed by the movie. The failings I've noted occupy a relatively small amount of screen time and should do little to interfere with your enjoyment of the movie. This is a faithful adaptation of TEENAGE MUTANT NINJA TURTLES, a well-done movie, and all-in-all a great way to spend a couple of hours. Most importantly, this is *not* a movie aimed only at 5-year-olds. If you enjoyed the comic book, you should enjoy the movie.
--- David M. Jones ARPANET<EMAIL_ADDRESS>UUCP: ...!mit-eddie!mit-athena!dmjones USmail: 10 Chandler St., #2; Somerville, MA 02144
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