000e+01 4.80000000e+01 -9.90000000e+01 1.60000000e+01 8.00000000e+00 2.30000000e+01 2.50000000e+01 9.20000000e+01 7.00000000e+00 1.20000000e+01 7.00000000e+01 5.60000000e+01 1.40000000e+01 3.40000000e+01 0.00000000e+00 6.60000000e+01 4.40000000e+01 3.00000000e+01 1.37000000e+02 1.80000000e+01 1.10000000e+02 1.47000000e+02 4.30000000e+01 5.00000000e+01 5.40000000e+01 1.13000000e+02 2.15000000e+02 7.90000000e+01 3.80000000e+01 1.02000000e+02 1.38000000e+02 1.00000000e+02 9.00000000e+01 9.00000000e+00 2.10000000e+01 1.14000000e+02 3.90000000e+02 1.89000000e+02 2.64000000e+02 4.60000000e+01 1.18000000e+02 1.31000000e+02 1.27000000e+02 1.57000000e+02 3.30000000e+01 9.70000000e+01 7.10000000e+01 1.61000000e+02 1.52000000e+02 2.20000000e+01 1.25000000e+02 1.51000000e+02 1.29000000e+02 4.20000000e+01 3.10000000e+01 3.20000000e+01 1.62000000e+02 8.80000000e+01 1.44000000e+02 1.15000000e+02 1.90000000e+01 4.00000000e+01 1.10000000e+01 1.30000000e+02 1.39000000e+02 1.56000000e+02 2.70000000e+01 1.64000000e+02 6.90000000e+01 3.60000000e+01 1.07000000e+02 1.99000000e+02 2.01000000e+02 1.50000000e+01 8.00000000e+01 4.70000000e+01 2.00000000e+01 1.70000000e+01 8.30000000e+01 2.40000000e+01 6.40000000e+01 2.30000000e+02 8.10000000e+01 1.88000000e+02 7.50000000e+02 3.90000000e+01 4.96000000e+02 1.65000000e+02 9.10000000e+01 1.72000000e+02 1.04000000e+02 5.10000000e+01 1.90000000e+02 4.10000000e+01 2.80000000e+01 6.20000000e+01 2.90000000e+01 6.10000000e+01 7.80000000e+02 2.67000000e+02 1.83000000e+02 8.56000000e+02 2.07000000e+02 9.90000000e+01 2.00000000e+02 1.03000000e+02 3.25000000e+02 3.50000000e+01 6.30000000e+01 9.50000000e+01 1.28000000e+02 4.00000000e+02 7.00000000e+02 1.26000000e+02 1.20100000e+03 4.50000000e+01 4.90000000e+01 7.30000000e+01 8.37000000e+02 9.30000000e+01 1.67000000e+02 1.53000000e+02 8.50000000e+01 2.16000000e+02 1.92000000e+02 2.31000000e+02 3.00000000e+02 1.01000000e+02 3.01000000e+02 1.70000000e+02 5.70000000e+01 1.08000000e+02 1.40000000e+02 5.50000000e+01 6.00000000e+02 7.96000000e+02] ======== addnotes <type 'float'> 14151 [nan 'The Cairo Chief of Police, William Petersen, resigned as a result of the attack.' 'Damages were estimated to be between $20,000-$25,000.' ..., 'The victims included Commander Shakoor, Noorullah, and Attiqullah.' "There is doubt that this incident meets terrorism-related criteria. Sources noted that the attack may have been related on the victim's reporting on corruption and organized crime." 'There is doubt that this incident meets terrorism-related criteria. Witnesses stated that the attack was started by government forces and other sources suspected that the incident may have started due to a disagreement between residents and security forces.'] ======== scite1 <type 'float'> 75888 [nan '"Police Chief Quits," Washington Post, January 2, 1970.' 'Committee on Government Operations United States Senate, "Riots, Civil, and Criminal Disorders," U.S. Government Printing Office, August 6, 1970.' ..., '"15 soldiers killed, 19 injured in Niger terror attack," Hindustan Times, February 24, 2017.' '"Gunmen shoot at home of Mexican indigenous rights reporter," Big News Network, January 10, 2017.' '"Rebels To Blame For Nierteti Killing: Central Darfur Governor," Radio Dabanga Online, January 2, 2017.'] ======== scite2 <type 'float'> 56563 [nan '"Cairo Police Chief Quits; Decries Local \'Militants\'," Afro-American, January 10, 1970.' 'Christopher Hewitt, "Political Violence and Terrorism in Modern America: A Chronology," Praeger Security International, 2005.' ..., '"Asia: US Army ASD Philippine Terrorist Media Weekly Wrap-up 25 - 31 December 2016," Summary, January 4, 2017.' '"\x96 Gunmen shoot at home of Mexican indigenous rights reporter," Human Rights Network For Journalists - Uganda, January 10, 2017.' '"Security deteriorates in West Darfur alongside attacks by Sudanese forces and local militias," African Centre for Justice and Peace Studies, January 13, 2017.'] ======== scite3 <type 'float'> 32563 [nan 'Christopher Hewitt, "Political Violence and Terrorism in Modern America: A Chronology," Praeger Security International, 2005.' 'The Wisconsin Cartographers\' Guild, "Wisconsin\'s Past and Present: A Historical Atlas," The University of Wisconsin Press, 2002.' ..., '"10-year-old girl used as human bomb in Nigeria attack," Nigeria Sun, January 1, 2017.' '"28 killed in Baghdad double suicide bombing 2 minutes ago," UPI, December 31, 2016.' '"Dozens Killed In Baghdad Blasts Claimed By Islamic State," Radio Free Europe, December 31, 2016.'] ======== dbsource <type 'str'> 26 ['PGIS' 'Hewitt Project' 'Hijacking DB' 'CBRN Global Chronology' 'CAIN' 'HSI' 'Disorders and Terrorism Chronology' 'State Department 1997 Document' 'UMD Miscellaneous' 'UMD Encyclopedia of World Terrorism 2012' 'Hyland' 'Armenian Website' 'UMD Sri Lanka 2011' 'UMD South Africa' 'Anti-Abortion Project 2010' 'UMD Assassinations Project' 'Eco Project 2010' 'UMD Algeria 2010-2012' 'CETIS' 'Leuprecht Canadian Data' 'UMD Schmid 2012' 'UMD Black Widows 2011' 'Sageman' 'UMD JTMM Nepal 2012' 'ISVG' 'START Primary Collection'] ======== INT_LOG <type 'numpy.int64'> 3 [ 0 -9 1] ======== INT_IDEO <type 'numpy.int64'> 3 [ 0 1 -9] ======== INT_MISC <type 'numpy.int64'> 3 [ 0 1 -9] ======== INT_ANY <type 'numpy.int64'> 3 [ 0 1 -9] ======== related <type 'float'> 20830 [nan '197001300001,<PHONE_NUMBER>02' '197001300002,<PHONE_NUMBER>01' ..., '201612310006,<PHONE_NUMBER>07,<PHONE_NUMBER>08' '201612310027,<PHONE_NUMBER>28' '201612310034,<PHONE_NUMBER>35,<PHONE_NUMBER>36,<PHONE_NUMBER>37'] ========
# Get rid of the rows without geolocation details
df.longitude.dropna(axis = 0, inplace=True)
df_ = df[['iyear','imonth','iday', 'region', 'region_txt', 'country','country_txt','provstate',
'city', 'latitude', 'longitude', 'location', 'attacktype1',
'attacktype1_txt', 'target1', 'targtype1_txt', 'targsubtype1',
'motive','nkill','nwound','weaptype1']]
df_.shape
(170350, 21)
df_.rename(index = str, columns = {'iyear':'year','imonth':'month', 'iday':'day' }, inplace=True)
#Replace dates where day is 0 to 1
df_['day'] = df_.day.replace(0, 1)
#Drop those lines where month is 0
df_ = df_[df_.month != 0]
# Turn Year/Month/Day into a Date
df_['Date'] = pd.to_datetime(df_[['year', 'month', 'day']])
# Set Date as index
df_ = df_.set_index('Date')
# Let's check what we have
df_.head()
/Users/baurjansafi/anaconda/lib/python2.7/site-packages/pandas/core/frame.py:2844: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy **kwargs) /Users/baurjansafi/anaconda/lib/python2.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy after removing the cwd from sys.path.
| year | month | day | region | region_txt | country | country_txt | provstate | city | latitude | ... | location | attacktype1 | attacktype1_txt | target1 | targtype1_txt | targsubtype1 | motive | nkill | nwound | weaptype1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||||||||||||||
| 1970-07-02 | 1970 | 7 | 2 | 2 | Central America & Caribbean | 58 | Dominican Republic | NaN | Santo Domingo | 18.456792 | ... | NaN | 1 | Assassination | Julio Guzman | Private Citizens & Property | 68.0 | NaN | 1.0 | 0.0 | 13 |
| 1970-01-01 | 1970 | 1 | 1 | 5 | Southeast Asia | 160 | Philippines | Tarlac | Unknown | 15.478598 | ... | NaN | 1 | Assassination | Employee | Journalists & Media | 54.0 | NaN | 1.0 | 0.0 | 13 |
| 1970-01-01 | 1970 | 1 | 1 | 8 | Western Europe | 78 | Greece | Attica | Athens | 37.983773 | ... | NaN | 3 | Bombing/Explosion | U.S. Embassy | Government (Diplomatic) | 46.0 | NaN | NaN | NaN | 6 |
| 1970-01-01 | 1970 | 1 | 1 | 4 | East Asia | 101 | Japan | NaN | Fukouka | 33.580412 | ... | NaN | 7 | Facility/Infrastructure Attack | U.S. Consulate | Government (Diplomatic) | 46.0 | NaN | NaN | NaN | 8 |
| 1970-01-01 | 1970 | 1 | 1 | 1 | North America | 217 | United States | Illinois | Cairo | 37.005105 | ... | NaN | 2 | Armed Assault | Cairo Police Headquarters | Police | 22.0 | To protest the Cairo Illinois Police Deparment | 0.0 | 0.0 | 5 |
5 rows × 21 columns
%matplotlib inline
df_.groupby("year").month.count().plot(figsize=(12,8),
title= 'Yearly Attacks',
fontsize=14,
)
plt.ylabel('Number of Attacks per Year')
<matplotlib.text.Text at 0x1209b87d0>
%matplotlib inline
df_.groupby("month").month.count().plot(figsize=(12,8),
title= 'Monthly Attacks',
fontsize=14)
df_.groupby("month").month.count()
month 1 14036 2 12975 3 14284 4 14213 5 15771 6 14258 7 15247 8 14802 9 13246 10 14728 11 14064 12 12706 Name: month, dtype: int64
df_.to_csv('data/gup_.csv')
%matplotlib inline
l = df_.attacktype1_txt.unique()
for i in l:
df_[df_.attacktype1_txt == i].groupby("year").attacktype1_txt.count().plot(figsize=(12,8),
title= "Yearly Attacks by Type of Attack",
fontsize=14)
_ = plt.legend(df_.attacktype1_txt.unique())
df_.region_txt.value_counts().plot(figsize=(12,8),
kind = 'bar',
title = "Number of Attacks by Region",
fontsize = 14
)
# let's see what regions have for terror attacks for the past 46 years.
<matplotlib.axes._subplots.AxesSubplot at 0x120a98ed0>
# import urban population data for each country in the past 46 years.
# resource is World Bank
df_pop= pd.read_csv('data/global_urban_population.csv')
# Convert the imported file into mergeable data
z = pd.DataFrame()
x = 0
for i in range(df_pop.shape[0]):
for j in range(1,df_pop.shape[1]):
z.set_value(x,'Country_Year', (df_pop['Country Name'][i] +"_" + str(df_pop.columns[j])))
z.set_value(x,'Urban_Pop', df_pop.iloc[i][j])
x += 1
z.head()
| Country_Year | Urban_Pop | |
|---|---|---|
| 0 | Aruba_1970 | 29900.0 |
| 1 | Aruba_1971 | 30082.0 |
| 2 | Aruba_1972 | 30275.0 |
| 3 | Aruba_1973 | 30470.0 |
| 4 | Aruba_1974 | 30605.0 |
# I found the date with global population and wanted to get an idea if the global terrorism
# is also a sign of overpopultion. Needed to convert it into a mergeable form for the Tableau visualization
z.to_csv('data/gup_zz.csv')
oilprice = pd.read_csv('data/OilPrice.csv')
oilprice.set_index(['year'], inplace = True)
oilprice.plot(kind = 'line', color = 'black', figsize = (12,8), fontsize = 14)
plt.xlabel('Year 1970 - 2016', fontsize = 14)
plt.ylabel('Price for Oil $/barrel', fontsize = 14)
<matplotlib.text.Text at 0x1209e5550>
for i in df.region_txt.unique():
print i, df.region[df.region_txt == i].mean()
Central America & Caribbean 2.0 North America 1.0 Southeast Asia 5.0 Western Europe 8.0 East Asia 4.0 South America 3.0 Eastern Europe 9.0 Sub-Saharan Africa 11.0 Middle East & North Africa 10.0 Australasia & Oceania 12.0 South Asia 6.0 Central Asia 7.0
Terror attacks are a ripe area of research for Bayesian inference. Given their infrequency, it is (thankfully) difficult for us to assume a high number of samples that approach some normal distribution. Because of this, we should construct a prior about the amount of terror a given area has seen and update that prior with new information (like a new year of attacks or a contrasting country from within the same region). You should compare two populations of your choosing using Bayesian inference. We want to know if the amount of terror one area has seen differs in a significant way than another area (or time period!) For example, if you are interested in knowing if one country in South America differs in a significant way from another area, you may make your prior assume that some country is a country in South America with μ average attacks and σ variation across South American countries. You would then update that prior with the information of a single country in South America as well as a separate country in South America. How significantly do the resulting posteriors differ? (An important assumption made here is that the time periods are being held constant, perhaps a single year.) You should structure your own test of populations rather than using the above example. If you're unable to setup a different test, brainstorm with your squad in the Slack chat. You must justify the prior you selected and interpret your results (use credible intervals.) Remember you can attempt to use different priors (but don't "prior hack" to affect your output!)
# I would go with Bombings/Explosions in Russia
dfba = df_[(df_.attacktype1 == 3)&(df_.country == 167)]
dfba.year.value_counts().sort_index().plot(figsize=(12,8),
kind = 'bar',
title = "Number of Bomb Explosions in Russia",
fontsize = 14
)
# Which is in line with the global trends and fead by price in oil that
# They used to steal from pipelines
<matplotlib.axes._subplots.AxesSubplot at 0x1191f7890>
import pymc3 as pm
import theano as thno
import theano.tensor as T
# Let me see if terrorism in Dagestan and Chechnya before Vladimir Putin
# became the president of Russia was of different nature?
dfba_prior = dfba[dfba.year < 2000]
# And after Putin has come to power
dfba_post = dfba[dfba.year >= 2000]
# Average number of bomb attacks in Russia before 2010:
prior_mean = dfba_prior.year.value_counts().mean()
prior_std = dfba_prior.year.value_counts().std()
print prior_mean
print prior_std
19.7142857143 9.1417410033
Dagestan = dfba_post[(dfba_post.provstate.str.contains('Dages'))|(dfba_post.city.str.contains('Dages'))].year.value_counts().values
Chechnya = dfba_post[(dfba_post.provstate.str.contains('Chech'))|(dfba_post.city.str.contains('Chech'))].year.value_counts().values
Ingushet = dfba_post[(dfba_post.provstate.str.contains('Chech'))|(dfba_post.city.str.contains('Chech'))].year.value_counts().values
plt.figure(figsize=(16,8))
D = df_[(df_.provstate.str.contains('Dages'))|(df_.city.str.contains('Dages'))].year.value_counts().sort_index().plot()
Ch = df_[(df_.provstate.str.contains('Chech'))|(df_.city.str.contains('Chech'))].year.value_counts().sort_index().plot()
In = df_[(df_.provstate.str.contains('Ingush'))|(df_.city.str.contains('Ingush'))].year.value_counts().sort_index().plot()
plt.legend(['Dagestan', 'Chechnya', 'Ingushetia'], fontsize = 14)
<matplotlib.legend.Legend at 0x132c243d0>
# Instatiating the model
with pm.Model() as model:
D_mean = pm.Normal('bombings_Dagestan_mean', prior_mean, sd = prior_std)
Ch_mean = pm.Normal('bombings_Chech_mean', prior_mean, sd = prior_std)
std_prior_lower = 0.01
std_prior_upper = 100.0
with model:
D_std = pm.Uniform('bombings_Dagestan_st', lower = std_prior_lower, upper = std_prior_upper)
Ch_std = pm.Uniform('bombings_Chechnya_st', lower = std_prior_lower, upper = std_prior_upper)
with model:
grpD = pm.Normal('group_Dagestan', mu=D_mean,sd = D_std, observed=Dagestan)
grpCh = pm.Normal('group_Chechnya', mu=Ch_mean,sd = Ch_std, observed=Chechnya)
with model:
diff_of_means = pm.Deterministic('difference of means', D_mean - Ch_mean)
diff_of_stds = pm.Deterministic('difference of stds', D_std - Ch_std)
effect_size = pm.Deterministic(
'effect size',
diff_of_means/np.sqrt((D_std**2+Ch_std**2)))
with model:
trace = pm.sample(20000, njobs = 4)
Auto-assigning NUTS sampler... Initializing NUTS using ADVI... Average Loss = 156.93: 5%|▍ | 9088/200000 [00:00<00:14, 13039.87it/s] Convergence archived at 9900 Interrupted at 9,900 [4%]: Average Loss = 161.14 100%|██████████| 20500/20500 [00:59<00:00, 342.14it/s]
pm.plot_posterior(trace[2000:],
varnames = ['bombings_Dagestan_mean', 'bombings_Chech_mean',
'bombings_Dagestan_st', 'bombings_Chechnya_st'],
color = '#87ceeb')
array([<matplotlib.axes._subplots.AxesSubplot object at 0x14bcfd9d0>, <matplotlib.axes._subplots.AxesSubplot object at 0x14bfd8f10>, <matplotlib.axes._subplots.AxesSubplot object at 0x14bfa9210>, <matplotlib.axes._subplots.AxesSubplot object at 0x14cbc8a10>], dtype=object)
pm.plot_posterior(trace[2000:],
varnames=['difference of means', 'difference of stds', 'effect size'],
ref_val=0,
color='#87ceeb')
array([<matplotlib.axes._subplots.AxesSubplot object at 0x14e8cca90>, <matplotlib.axes._subplots.AxesSubplot object at 0x14c57dd90>, <matplotlib.axes._subplots.AxesSubplot object at 0x13fc91350>], dtype=object)
pm.summary(trace[2000:], varnames = ['difference of means', 'difference of stds', 'effect size'])
difference of means: Mean SD MC Error 95% HPD interval ------------------------------------------------------------------- 1.665 6.430 0.032 [-10.943, 14.293] Posterior quantiles: 2.5 25 50 75 97.5 |--------------|==============|==============|--------------| -10.992 -2.635 1.685 5.971 14.256 difference of stds: Mean SD MC Error 95% HPD interval ------------------------------------------------------------------- -1.997 5.947 0.022 [-14.170, 9.762] Posterior quantiles: 2.5 25 50 75 97.5 |--------------|==============|==============|--------------| -14.160 -5.608 -1.918 1.721 9.784 effect size: Mean SD MC Error 95% HPD interval ------------------------------------------------------------------- 0.056 0.210 0.001 [-0.351, 0.473] Posterior quantiles: 2.5 25 50 75 97.5 |--------------|==============|==============|--------------| -0.354 -0.086 0.056 0.197 0.470
from pymc3 import traceplot
traceplot(trace)
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x14ccb8390>, <matplotlib.axes._subplots.AxesSubplot object at 0x14c096c10>], [<matplotlib.axes._subplots.AxesSubplot object at 0x14f829bd0>, <matplotlib.axes._subplots.AxesSubplot object at 0x13fab0b90>], [<matplotlib.axes._subplots.AxesSubplot object at 0x14a39b350>, <matplotlib.axes._subplots.AxesSubplot object at 0x1498e1b90>], [<matplotlib.axes._subplots.AxesSubplot object at 0x14ccf5610>, <matplotlib.axes._subplots.AxesSubplot object at 0x1494da5d0>], [<matplotlib.axes._subplots.AxesSubplot object at 0x144729290>, <matplotlib.axes._subplots.AxesSubplot object at 0x148d84750>], [<matplotlib.axes._subplots.AxesSubplot object at 0x14f7c4a90>, <matplotlib.axes._subplots.AxesSubplot object at 0x14f78db50>], [<matplotlib.axes._subplots.AxesSubplot object at 0x14f679c50>, <matplotlib.axes._subplots.AxesSubplot object at 0x14a6a7d10>]], dtype=object)
The year 1993 is missing from our dataset! Given there is a wealth of information across different types of attacks, we will focus analysis on attacktype1 bombings (category 3, as per the codebook) For this section, you should determine a methodology that allows you to best fill in the missing values. Perhaps you want to consider hemispheres to be separate models, for example. (That's the only hint you'll receive - and even that hint is not an optimal option.) Once you've created your methodology and imputed the number of bombings in 1993, you should feel free to apply your methodology to OTHER attack categories. (Bonus opportunity: turn your method into a pipeline. Use that pipeline to fill in other missing attack values.)
# First let's select the how far we would to go to predict 1993
# You will see below on this notebook that I observed a correlation
# with the oil price. And the observed correlation remains from two
# to six years after. This means 6 effective years (2, 3, 4, 5, 6, 7)
# Meaning that I will look into 1970 until 1999
dfb_1993 = df_[(df_.attacktype1 ==3)&(df_.year >= 1970)&(df_.year < 2000)].year.value_counts().sort_index()
%matplotlib inline
dfb_1993.plot(figsize=(12,8),
kind = 'line',
title = "Number of Bomb Explosions Globally \n Years 1970 - 1999",
fontsize = 14,
color = 'red'
)
<matplotlib.axes._subplots.AxesSubplot at 0x1539ebe90>
dfb_1993.head()
1970 333 1971 238 1972 187 1973 149 1974 282 Name: year, dtype: int64
from statsmodels.tsa.stattools import adfuller
def test_stationarity(i, data):
#Determing rolling statistics
#Tried pd.rolling - it spits out a deprecation notification
rolmean = data.rolling(window=i,center=False).mean()
rolstd = data.rolling(window=i,center=False).std()
#Plot rolling statistics:
fig = plt.figure(figsize=(12, 8))
orig = plt.plot(data, color='blue',label='Original')
mean = plt.plot(rolmean, color='red', label='Rolling Mean')
std = plt.plot(rolstd, color='black', label = 'Rolling Std')
plt.legend(loc='best')
plt.title('Rolling Mean & Standard Deviation')
plt.show()
#Perform Dickey-Fuller test:
print 'Results of Dickey-Fuller Test:'
dftest = adfuller(data)#, autolag='AIC')
dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
for key,value in dftest[4].items():
dfoutput['Critical Value (%s)'%key] = value
print dfoutput
for i in range(1,10):
test_stationarity(i, dfb_1993)
print 'Lag - ', i
Results of Dickey-Fuller Test: Test Statistic -2.444203 p-value 0.129647 #Lags Used 7.000000 Number of Observations Used 21.000000 Critical Value (5%) -3.013098 Critical Value (1%) -3.788386 Critical Value (10%) -2.646397 dtype: float64 Lag - 1
Results of Dickey-Fuller Test: Test Statistic -2.444203 p-value 0.129647 #Lags Used 7.000000 Number of Observations Used 21.000000 Critical Value (5%) -3.013098 Critical Value (1%) -3.788386 Critical Value (10%) -2.646397 dtype: float64 Lag - 2
Results of Dickey-Fuller Test: Test Statistic -2.444203 p-value 0.129647 #Lags Used 7.000000 Number of Observations Used 21.000000 Critical Value (5%) -3.013098 Critical Value (1%) -3.788386 Critical Value (10%) -2.646397 dtype: float64 Lag - 3
Results of Dickey-Fuller Test: Test Statistic -2.444203 p-value 0.129647 #Lags Used 7.000000 Number of Observations Used 21.000000 Critical Value (5%) -3.013098 Critical Value (1%) -3.788386 Critical Value (10%) -2.646397 dtype: float64 Lag - 4
Results of Dickey-Fuller Test: Test Statistic -2.444203 p-value 0.129647 #Lags Used 7.000000 Number of Observations Used 21.000000 Critical Value (5%) -3.013098 Critical Value (1%) -3.788386 Critical Value (10%) -2.646397 dtype: float64 Lag - 5
Results of Dickey-Fuller Test: Test Statistic -2.444203 p-value 0.129647 #Lags Used 7.000000 Number of Observations Used 21.000000 Critical Value (5%) -3.013098 Critical Value (1%) -3.788386 Critical Value (10%) -2.646397 dtype: float64 Lag - 6
Results of Dickey-Fuller Test: Test Statistic -2.444203 p-value 0.129647 #Lags Used 7.000000 Number of Observations Used 21.000000 Critical Value (5%) -3.013098 Critical Value (1%) -3.788386 Critical Value (10%) -2.646397 dtype: float64 Lag - 7
Results of Dickey-Fuller Test: Test Statistic -2.444203 p-value 0.129647 #Lags Used 7.000000 Number of Observations Used 21.000000 Critical Value (5%) -3.013098 Critical Value (1%) -3.788386 Critical Value (10%) -2.646397 dtype: float64 Lag - 8
Results of Dickey-Fuller Test: Test Statistic -2.444203 p-value 0.129647 #Lags Used 7.000000 Number of Observations Used 21.000000 Critical Value (5%) -3.013098 Critical Value (1%) -3.788386 Critical Value (10%) -2.646397 dtype: float64 Lag - 9
rollingmean = dfb_1993.rolling(window = 6, center=False).mean()
rollingmean
1970 NaN 1971 NaN 1972 NaN 1973 NaN 1974 NaN 1975 259.666667 1976 274.000000 1977 339.333333 1978 415.500000 1979 566.833333 1980 686.000000 1981 804.833333 1982 922.333333 1983 1025.000000 1984 1213.666667 1985 1284.500000 1986 1369.333333 1987 1435.166667 1988 1522.666667 1989 1614.500000 1990 1607.000000 1991 1691.333333 1992 1730.000000 1994 1676.000000 1995 1533.000000 1996 1436.333333 1997 1335.000000 1998 1087.666667 1999 906.666667 Name: year, dtype: float64
# Since, Dickey-Fuller p-value in none of iterations achieved <.05 threshold
# I will use the rolling mean with a window of 6 years behind
# and will calculate the average of the rolling mean for 1992 and 1994
print 'The estimated number of bombings in 1993 is - ', rollingmean[22:24].mean()
The estimated number of bombings in 1993 is - 1703.0
# So I re-run the same code and shift the oil price
# from 0 to 9 years behind, to see if there is a delayed effect
Rcoef = []
for i in range(10):
oilprice = pd.read_csv('data/OilPrice.csv')
oilprice.set_index(['year'], inplace = True)
oilprice.OilPrice = oilprice.OilPrice.shift(i)
dt = pd.DataFrame(df_.year.value_counts().sort_index())
dt.rename(columns = {'year':'NAttacks'}, inplace = True)
result = dt.join(oilprice, how = 'inner')
result.dropna(inplace = True)
Rcoef.append([i, np.corrcoef(result.NAttacks, result.OilPrice)[0,1]])
Rcoef
[[0, 0.30980912578825276], [1, 0.4795848590166959], [2, 0.59225100395794661], [3, 0.58892375992158874], [4, 0.57763461354041634], [5, 0.55058267312996534], [6, 0.55071667825621773], [7, 0.52625825483024113], [8, 0.50824527921362839], [9, 0.37333712263327307]]
Summary
EDA Our dataset had over 170 thousand observations with 135 features. I focused only on those that had were more consistent from observation to observation. Left 20 features and only those observations that had clear attachment to location. Dropped observations with 0 months, and fixed day 0’s to 1’s.
Added observations on urban population and oil price for the same number of years.
Bayesian inference I was reading about the first and second Russian war on Chechnya. Chechnya announced independence from Russia in 1994 and Russia introduced its troops almost the same year. The military campaign was held by Yeltsin and ended in 1996. There was another one in 1999 - 2000 before Putin was appointed as Russia’s interim leader. I checked on details of how Chechen rebels financed their activity and it turned out that they were stealing oil from the bypassing oil pipelines. It became clear that the terrorist activity spreads along the oil producing areas in the North Caucasus. So I decided to compare if terrorist activity is different or similar in the neighboring republic of Dagestan. Compared the means and standard distribution on both republics and they turned out to be very similar.
It is particularly interesting to observe because there is another Republic of Ingushetia, that is ethnically much closer to Chechnya but it has not been so much involved into terrorist activity.
Imputing 1993 It was a very simple task, since I connected the correlation between oil and terrorist activity I take terrorist activity as far as 6 years ago to impute 1993. The next step was just to calculate the average mean of 1994 and 1992.


GER ON
Rangers boss Steven Gerrard willing to move fixtures to help Scotland get to Euro 2020
by Gary TaylorRANGERS are willing to move fixtures in order to help Scotland's bid to reach Euro 2020.
Gers boss Steven Gerrard revealed that the Ibrox club will do whatever is necessary to aid the national side.
Steve Clarke's men learned their Nations League play-off fate this afternoon.
The Dark Blues will take on Israel at Hampden in the semi-finals.
If they come through that, then it will be a one-off away trip to either Norway or Serbia to battle it out for a spot at the major tournament.
It would be a first in 21 years if Scotland were to make it, and Gerrard is happy to help out Steve Clarke.
Speaking ahead of their clash with Hamilton, he said: "I certainly won't be standing in the way.
"I represent this club in this country so for me I give it the support I need to give it. But we don't make those decisions. Ultimately it will be down to the SFA and stuff.
"We'll go with the flow and if it means us skipping a game or adjusting a game we'll do everything we can to support (Scotland). That's what you've got to do."
It comes after ex-Kilmarnock boss Clarke has urged Scottish football's governing bodies to do all that they can to ensure the national team is helped.
Meanwhile, Stevie G admits he still has to "pinch himself" every time he thinks about how cheap Rangers bought Glen Kamara for.
The 24-year-old was initially snapped up on a pre-contract agreement last January from Dundee, along with Jordan Jones at Killie.
However, unlike with Jones, the Light Blues opted to bring Kamara in early - signing him for a mere £50,000.
Gerrard said: "We haven't heard of any interest down south, we've read tons about it though.
"But I wouldn't be surprised if there was interest in Glen Kamara because he's been outstanding since he walked in the door.
"Sometimes I still have to pinch myself for how we got him and what we got him for.
"He got a real strong contract when he came here from Dundee.
"The message for Glen and the other players is we always reward our players for consistent performances over a long period of time. Glen will be treated no different."


int
Sim_Init(
Tcl_Interp * interp,
Main_Manager_t * manager
)
- This function is needed by tcl/tk to initialize this
package. Initialization includes registering of commands and initialization
of internal data structures used by this package.
- Side Effects SimManagerInit() is called and a "global" data
structure, namely the simulation manager is created.
- See Also
optional
void
Sim_SimAtomArrayFree(
array_t * simAtomArray
)
- This routine frees an array of simAtoms. This is
useful to free the array returned by Sim_SimAtomReturnSortedArray.
- Side Effects required
- See Also
optional
Atm_Atom_t*
Sim_SimAtomReadAtom(
Sim_Atom_t* simAtom
)
- This function returns a pointer to the atom that is
represented by this simAtom.
- Side Effects User should not free the returned pointer.
- See Also
optional
boolean
Sim_SimAtomReadIsUserAtom(
Sim_Atom_t* simAtom
)
- This function returns true if the simAtom is an user
atom, i.e. atom controlled by the user.
- Side Effects required
- See Also
optional
char*
Sim_SimAtomReadName(
Sim_Atom_t* simAtom
)
- This function returns the name of the simAtom.
- Side Effects User should not free the returned string.
- See Also
optional
array_t *
Sim_SimAtomReturnSortedArray(
Mdl_Module_t * module,
array_t * atomArray,
int isUserAtom
)
- Given a module, an array of users atoms (Atm_Atom_t
*), and the type of the atoms (are they userAtoms?), this routine creates
the simAtoms for the module and returns an array of topologically sorted
simAtoms. Note that the non-user atoms will come as late as possible in the
linearization so that the user will have the least knowledge of how the
non-user atom behaves.
- Side Effects The atomDependencyInfo will be updated. The sorted array
will be returned. User should free the array (Sim_SimAtomArrayFree) after
use.
- See Also
Sim_SimAtomArrayFree


-
Start time
{{ ds.start_time }}
-
End time
{{ ds.end_time }}
{% for ds_ps in datasets_perstats%}
{% if ds_ps.ds_name == ds.name %}
-
# persons
{{ ds_ps.num_persons }}
-
# activities
{{ ds_ps.num_activities }}
-
# recorded activities
{{ ds_ps.num_recorded_activities }}
{% endif %}
{% endfor %}
-
# devices
{{ ds.num_devices }}
-
# recorded events
{{ ds.num_recorded_events }}
-
Data size
{{ ds.data_size | filesizeformat }}


distance v0.2.1 Distance
Basic distance calculations for cartesian coordinates for calculting
distances on a single plane. If you are looking to calculating distance
on the surface of Earth, check out the Distance.GreatCircle
module.
Examples
iex> Distance.distance({2.5, 2.5}, {4, 0.8})
2.2671568097509267
iex> Distance.segment_distance({2.5, 2.5}, {4, 0.8}, {-2, 3})
1.0797077632696
iex> Distance.distance([{2.5, 2.5}, {4, 0.8}, {-2, 3}, {1, -1}])
13.657774933219109
Summary
Functions
Returns the geometric distance of the linestring defined by the List of points. Accepts 2- or 3-dimensional points
Returns the geometric distance between two points. Accepts 2- or 3-dimensional points
Returns the square of the distance between two points. This is used by the
Distance.distance
function above, but having access to the value before
the expensice sqaure root operation is useful for time-sensitive applications
that only need values for comparison
Returns the geometric distance from a point p
and the line segment
between two points p1
and p2
. Note that this is a line segment, not
an infinite line, so points not between p1
and p2
will return the
distance to the nearest of the two endpoints
Similar to Distance.distance_squared
, this provides much faster comparable
version of Distance.segment_distance
Functions
Returns the geometric distance of the linestring defined by the List of points. Accepts 2- or 3-dimensional points.
Examples
iex> Distance.distance([{2.5, 2.5}, {4, 0.8}, {2.5, 3.1}, {2.5, 3.1}])
5.013062853300123
iex> Distance.distance([{1, -2, 1}, {-2, 2, -1}, {-2, 1, 0}, {2, -3, 1}])
12.543941016045627
Returns the geometric distance between two points. Accepts 2- or 3-dimensional points.
Examples
iex> Distance.distance({1, -2}, {-2, 2})
5.0
iex> Distance.distance({1, -2, 2}, {-2, 2, 1})
5.0990195135927845
Returns the square of the distance between two points. This is used by the
Distance.distance
function above, but having access to the value before
the expensice sqaure root operation is useful for time-sensitive applications
that only need values for comparison.
Examples
iex> Distance.distance_squared({1, -2}, {-2, 2})
25
iex> Distance.distance_squared({1, -2, 2}, {-2, 2, 1})
26
Returns the geometric distance from a point p
and the line segment
between two points p1
and p2
. Note that this is a line segment, not
an infinite line, so points not between p1
and p2
will return the
distance to the nearest of the two endpoints.
Examples
iex> Distance.segment_distance({3, 2}, {-2, 1}, {5, 3})
0.4120816918460673 # distance between the point {3, 2} and the closest point along line segment ({-2, 1}, {5, 3})
iex> Distance.segment_distance({1, -2}, {-2, 2}, {-10, 102})
5.0
iex> Distance.segment_distance({1, -2}, {-2, 2}, {1, -2})
0.0


2020
Currently working in the "Service Configuration team" in RENAULT as consultant, with the main activity that is powerBI reporting, analyzing datas using datasets that are excel sheets or Json files.
Another side activity inside my team is the update and the maintenance of VBA macros for the Json catalogue generation and supporting the maintenance and update of the configuration services data model
In August 2020 did a "refresh" of my SQL language knowledge applied to real problems and relational database, taking into account that probably in my current team, we will use an SQL database in the future.


Sweden detains Iranian on suspicion of murder in Iran in 1988
STOCKHOLM (Reuters) - A 58-year-old Iranian citizen has been arrested in Sweden on suspicion of murder in Iran in 1988, prosecutors said on Wednesday, and national news agency TT said the case involved killings of political prisoners.
Human rights groups say the Islamic Republic executed thousands of political detainees in the late 1980s. Many of them belonged to opposition groups. Iran denies having political prisoners or having executed any such prisoner.
The prosecutor’s detention request said the man was arrested on Nov. 9 after what TT said was his arrival to visit relatives. He was remanded in custody Wednesday pending a decision by Dec. 11 on whether he should be formally charged and put on trial.
TT said the man was suspected of playing a leading role in a prison in Karaj, a city west of the Iranian capital Tehran, where many detainees were executed in 1988.
The man’s lawyer was not immediately available for comment. The lawyer told TT the man denies the allegations.
Iranian officials have denied the late 1980s killings took place. However, in 2016 a 28-year-old audio tape surfaced in which Ayatollah Hossein Ali Montazeri, the deputy Supreme Leader of Iran at the time, said the executions were the “biggest crimes committed by the Islamic Republic”.
Amnesty International said in a report last year that the lowest estimates put the number of executed at around 5,000.


As you can see, your state hospital system is about to be highly over capacity. There are roughly three options:
60,000 Dead in the US, 24 Months to TRUE Normalcy, 15% Hit to Economy, 1 Year to Recovery
We move to contain the disease by locking down, similar to South Korea, within the next 48 hours. This likely means getting citizens to remain in their homes for about a month, while scaling up healthcare infrastructure. In conjunction, we an aid bill that couples tax breaks for businesses, financial support for individuals, and free widespread testing and health care is needed.
Results: By the end of April, the disease is contained, and has killed less people than the flu does annually (~60,000 in the US). The US economy contracts by about 15%, but recovers within a year. Aggressive testing must continue at the US border for several years, until a vaccine has been developed, dragging out a true return to true normalcy. We risk facing periodic virus resurgence, as we have not built “herd immunity”. Pundits speculate about what they see as “an overreaction.”
10,000,000+ Dead in the US, 3-4 Months to Normalcy, 15-30% Hit to Economy, 1-1.5 Years to Recovery
We do nothing (or effectively nothing). The disease rockets through the population, infecting 70-80% of us.
Results: Due to the uncontrolled nature of the disease, and the overwhelming of the hospital system, 3-5% of those infected die (the current numbers in Italy are 6.6%), resulting in >10 million deaths in less than 6 months. The public achieves herd immunity by June/July, and business returns to normal. The US economy contracts by about 15-30%, but recovers within 12-18 months.
3,000,000+ Dead in the US, 6-12 Months to Normalcy, 30-50% Hit to Economy, 1.5-2 Years to Recovery
This is not a single option, but really a range of options. Both government and corporations gradually implement strong but voluntary measures to increase “social distancing,” over the next 2-4 weeks, and reduce the rate at which people are infected such that the healthcare system is not overrun (ie #flattenthecurve).
Results: While 70% of the population is infected in 2020, we avoid a worst case scenario, and achieve a death rate of about 1-1.5%. >3 million people die in the US (50-100x the flu). However, the US economy is slowed to a crawl for 6-12 months, contracting 30-50%, and taking 18-24 months to fully recover. We may implement an aid bill to help offset the cost to companies and the public, as outlined above. We now enjoy “herd immunity,” and can fully reopen borders at the end of the year.


Before Change
self.Hc = Hc
self.selection_threshold = selection_threshold
if meta_classifier is None:
warnings.warn("No classifier model passed for the Meta-Classifier. Using a Naive Bayes instead")
self.meta_classifier = MultinomialNB()
else:
self.meta_classifier = meta_classifier
self.op_knn = None
self.n_meta_features = (self.k * 2) + self.Kp + 2
def fit(self, X, y):
After Change
Cruz, R.M., Sabourin, R., Cavalcanti, G.D. and Ren, T.I., 2015. META-DES: A dynamic ensemble selection framework
using meta-learning. Pattern Recognition, 48(5), pp.1925-1935.
Cruz, R.M., Sabourin, R. and Cavalcanti, G.D., 2015, July. META-des. H: a dynamic ensemble selection technique
using meta-learning and a dynamic weighting approach. In Neural Networks (IJCNN), 2015 International Joint
Conference on (pp. 1-8).
R. M. O. Cruz, R. Sabourin, and G. D. Cavalcanti, “Dynamic classifier selection: Recent advances and perspectives,”


Click on any of the images below and watch it disappear. click the 'restore' button to restore the images.


The Challenge
Dick's Sporting Goods is a leading retailer in sporting equipment in the US. They are the go-to spot for shoppers looking to start a new sport as they pride themselves in staffing athletes to help with equipment testing and purchase. Generally, they have focused their strategy in suburban areas, but they challenged us to reimagine urban sport commerce and create a sustainable concept catered to the right targets.
Research, Design, Build
Kicking off this challenge, we interviewed sports shoppers both urban and suburban. Our team being located in Evanston allowed us to do shopalongs and other in-context interviews. We spoke to young professionals, families, students, employees, etc. Our findings led to several insights on which we were able to build several personas around behaviors and perceptions.While these personas were distinct with different goals and habits, they all places a high value on selection and variety when purchasing equipment.
Being all former athletes, our team tried to avoid anchoring our research on our own personal experiences. However, our sports knowledge came in handy in building a framework mapping "equipment intensity" of sports. For example, lacrosse and baseball players have different needs but are more equipment intense and more likely to shop at Dick's Sporting Goods. Runners on the other hand, were much less equipment intense and significantly less likely to shop at Dick's Sporting Goods. This brought to light interesting insights about our personas and Dick's Sporting Goods.
Greater equipment intensity meant higher chance of the athlete (or parent of the athlete) heading to a large sporting good retailer like Dick's Sporting Goods.
Additionally, we found that urban sports shoppers value convenience over instant gratification in terms of selection, trial, purchase, and delivery of sporting goods. How might we provide our urban customers with convenience of selection, trial, purchase, and delivery given the contrains of an urban environment?
DSG Urban Exhibition
After a few solo storms and shareouts, we came together around our prototype solution - DSG Urban Exhibition. The concept is a small, urban space for equipment testing, trial, and order. We wanted to capture the excitement young urbanites feel signing up for new sports activities, such as an intramural soccer league or company sponsored softball game.
DSG Urban Exhibition is a sample only, testing centric store with a small footprint, coupled with an online platform for selection of variants and purchasing of products which are then delivered to the customer's home.
For young professionals and families living in urban areas, DSG Urban Exhibition is a retail experience that provides unmatched convenience in testing, purchasing and delivery of sporting goods, offering the satisfaction of physically finding the correct item combined with the convenience of online shopping.
Takeaways
Being early on in my Kellogg experience, this was my first real foray into design thinking. While my team and I definitely made plenty of mistakes along the way, we learned the importance of in-context research. Shopalongs reveal so much about a consumers behavior. In-context interviews provide the opportunity to find behaviors consumers wouldn't know they do - they are "caught in the act". This leads to moments when an experience can build excitement or ameliorate anxiety. In finding these otherwise unkown moments, products can deliver an exceptional, unique experience to users.


EuroDry Ltd. 2019 Q3 - Results - Earnings Call Presentation
by SA Transcripts, https://seekingalpha.com/author/sa-transcriptsThe following slide deck was published by EuroDry Ltd. in conjunction with their 2019 Q3 earnings call.
The following slide deck was published by EuroDry Ltd. in conjunction with their 2019 Q3 earnings call.


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|
Panda3D
|
Public Member Functions | |
| def | __init__ (self, canvas, parent, item, menuList=[]) |
| def | collapse (self, event=None) |
| def | createPopupMenu (self) |
| def | deselect (self, event=None) |
| def | deselectall (self) |
| def | deselecttree (self) |
| def | destroy (self) |
| def | draw (self, x, y, fUseCachedChildren=1) |
| def | drawicon (self) |
| def | drawtext (self) |
| def | edit (self, event=None) |
| def | edit_cancel (self, event=None) |
| def | edit_finish (self, event=None) |
| def | expand (self, event=None) |
| def | find (self, searchKey) |
| def | flip (self, event=None) |
| def | geticonimage (self, name) |
| def | lastvisiblechild (self) |
| def | popupMenu (self, event=None) |
| def | popupMenuCommand (self) |
| def | reveal (self) |
| def | select (self, event=None) |
| def | select_or_edit (self, event=None) |
| def | setChildrenTag (self, tag, fModeChildrenTag) |
| def | setFSortChildren (self, fSortChildren) |
| def | update (self, fUseCachedChildren=1, fExpandMode=0) |
| def | updateAll (self, fMode, depth=0, fUseCachedChildren=1) |
| def | view (self) |
Public Attributes | |
| canvas | |
| children | |
| childrenTag | |
| entry | |
| fModeChildrenTag | |
| fSortChildren | |
| iconimages | |
| image_id | |
| item | |
| kidKeys | |
| label | |
| menuList | |
| menuVar | |
| parent | |
| selected | |
| setAsTarget | |
| state | |
| text_id | |
| x | |
| y | |
| def __init__ | ( | self, | |
| canvas, | |||
| parent, | |||
| item, | |||
menuList = [] |
|||
| ) |
| def collapse | ( | self, | |
event = None |
|||
| ) |
| def createPopupMenu | ( | self | ) |
| def deselect | ( | self, | |
event = None |
|||
| ) |
| def deselectall | ( | self | ) |
| def deselecttree | ( | self | ) |
| def destroy | ( | self | ) |
| def draw | ( | self, | |
| x, | |||
| y, | |||
fUseCachedChildren = 1 |
|||
| ) |
| def drawicon | ( | self | ) |
| def drawtext | ( | self | ) |
| def edit | ( | self, | |
event = None |
|||
| ) |
| def edit_cancel | ( | self, | |
event = None |
|||
| ) |
| def edit_finish | ( | self, | |
event = None |
|||
| ) |
| def expand | ( | self, | |
event = None |
|||
| ) |
| def find | ( | self, | |
| searchKey | |||
| ) |
| def flip | ( | self, | |
event = None |
|||
| ) |
| def geticonimage | ( | self, | |
| name | |||
| ) |
| def lastvisiblechild | ( | self | ) |
| def popupMenu | ( | self, | |
event = None |
|||
| ) |
| def popupMenuCommand | ( | self | ) |
| def reveal | ( | self | ) |
| def select | ( | self, | |
event = None |
|||
| ) |
| def select_or_edit | ( | self, | |
event = None |
|||
| ) |
| def setChildrenTag | ( | self, | |
| tag, | |||
| fModeChildrenTag | |||
| ) |
| def setFSortChildren | ( | self, | |
| fSortChildren | |||
| ) |
| def update | ( | self, | |
fUseCachedChildren = 1 , |
|||
fExpandMode = 0 |
|||
| ) |
| def updateAll | ( | self, | |
| fMode, | |||
depth = 0 , |
|||
fUseCachedChildren = 1 |
|||
| ) |
| def view | ( | self | ) |
| canvas |
| children |
| childrenTag |
| entry |
| fModeChildrenTag |
| fSortChildren |
| iconimages |
| image_id |
| item |
| kidKeys |
| label |
| menuList |
| menuVar |
| parent |
| selected |
| setAsTarget |
| state |
| text_id |
| x |
| y |


Time left for {{ name }}
{{ date }}, total {{ current_total }}, increasing {{ rate }}% per day
If the growth rate stays the same as the last 7 days
{{ time_1_per_500}}
days
Your neighbor will be infected (1 in 500)
*Including people who get recovered
{{ time_1_per_10}}
days
Your next door will be infected (1 in 10)
{{ time_1_per_3}}
days
Your family member will be infected (1 in 3)
{{ time_everyone}}
days
Everyone will be infected
You can change this future
WEAR A MASK
WASH YOUR HANDS
Data source
{{ data_source | safe }}How it's calculated
Precise estimation is not my goal,
I want to change the predicted future instead.
Simple extrapolation is used for the estimations.
1) Extract the number of cases for the last 7 days.
2) Calculate the growth ratio by applying a linear regression in log space.
3) Extrapolate the numbers using the rate.
Recovery population is not taken into account.
The number of hospital beds is NOT a live update.


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Iced Tea and Coffee


Golden Gate National Parks Conservancy
Watershed boundaries are the ridgetops or the high elevations around a
valley, and as such represent logical bounding areas for natural
processes. In the Golden Gate Natural Resource Area, our native plant
nurseries collect seeds and cuttings within watershed and subwatershed
boundaries to grow plants for reintroduction to the landscape after a
construction project, find and document rare plants, and track genetic
change and diversity to prevent mixing species across ridgetops.


Generate thumbnails in your browser. Simply choose your thumbnail properties and drag and drop images onto the field below. Find out more on the MDN blog and fork/get the source on GitHub.


{% extends 'base/base.html' %} {% block content %} {% include 'component/custom-select.html'%} Quick Report Date Select Employee selected = option.id" :class="{ 'is-danger' : errors.selected == true }"> [[ props.option.name ]] Company , Location Quick Report for [[ employee ]] Select Feedback Positive Negative Submit Please fix the errors {% endblock %}
Quick Report


Class MissingItemInconsistency<TValue>
Denotes the inconsistency that an item is missing in a synchronized collection
Inheritance
object
MissingItemInconsistency<TValue>
Inherited Members
object.Equals(object, object)
object.GetType()
object.MemberwiseClone()
object.ReferenceEquals(object, object)
object.ToString()
Assembly: NMF.Synchronizations.dll
Syntax
public class MissingItemInconsistency<TValue> : IInconsistency, IEquatable<IInconsistency>
Type Parameters
| Name |
Description |
| TValue |
The type of the item
|
Constructors
|
Improve this Doc
View Source
MissingItemInconsistency(ISynchronizationContext, ICollection<TValue>, ICollection<TValue>, TValue, bool)
Declaration
public MissingItemInconsistency(ISynchronizationContext context, ICollection<TValue> sourceCollection, ICollection<TValue> targetCollection, TValue source, bool isLeftMissing)
Parameters
| Type |
Name |
Description |
| ISynchronizationContext |
context |
|
| System.Collections.Generic.ICollection<T><TValue> |
sourceCollection |
|
| System.Collections.Generic.ICollection<T><TValue> |
targetCollection |
|
| TValue |
source |
|
| bool |
isLeftMissing |
|
Properties
|
Improve this Doc
View Source
CanResolveLeft
Describes inconsistencies that can occur between the right hand and the left hand of a synchronization
Declaration
public bool CanResolveLeft { get; }
Property Value
|
Improve this Doc
View Source
CanResolveRight
Describes inconsistencies that can occur between the right hand and the left hand of a synchronization
Declaration
public bool CanResolveRight { get; }
Property Value
|
Improve this Doc
View Source
Context
Gets the context in which this inconsistency occured
Declaration
public ISynchronizationContext Context { get; }
Property Value
|
Improve this Doc
View Source
IsLeftMissing
True, if the element is missing in the left side, otherwise false
Declaration
public bool IsLeftMissing { get; }
Property Value
|
Improve this Doc
View Source
Source
Gets the source element that is missing
Declaration
public TValue Source { get; }
Property Value
|
Improve this Doc
View Source
SourceCollection
Gets the source collection where the element is contained
Declaration
public ICollection<TValue> SourceCollection { get; }
Property Value
| Type |
Description |
| System.Collections.Generic.ICollection<T><TValue> |
|
|
Improve this Doc
View Source
TargetCollection
Gets the target collection where the element is missing
Declaration
public ICollection<TValue> TargetCollection { get; }
Property Value
| Type |
Description |
| System.Collections.Generic.ICollection<T><TValue> |
|
Methods
|
Improve this Doc
View Source
Equals(IInconsistency)
Declaration
public bool Equals(IInconsistency other)
Parameters
Returns
|
Improve this Doc
View Source
Equals(object)
Declaration
public override bool Equals(object obj)
Parameters
| Type |
Name |
Description |
| object |
obj |
|
Returns
Overrides
object.Equals(object)
|
Improve this Doc
View Source
GetHashCode()
Declaration
public override int GetHashCode()
Returns
Overrides
object.GetHashCode()
|
Improve this Doc
View Source
ResolveLeft()
Describes inconsistencies that can occur between the right hand and the left hand of a synchronization
Declaration
public void ResolveLeft()
|
Improve this Doc
View Source
ResolveRight()
Describes inconsistencies that can occur between the right hand and the left hand of a synchronization
Declaration
public void ResolveRight()
Implements
System.IEquatable<T>


Community risks for fire departments were determined via statistical regression models that predict various types of risk as a function of community attributes. Five community risks were assessed through the statistical modelling exercise:
Community risks were estimated as a function of the socio-demographic and geographic characteristics of the locations (census tracts) of low risk structure fires over a 9 year period (2014-2012) according to NFIRS data. The socio-demographic attributes to help predict risk included such items as:
Models were separately calculated for groups of departments with similar protected population size and region. (Departments serving fewer than 10,000 people were excluded from the analysis.) One exception is that departments serving 1 million or more people were analyzed as a single group regardless of region. Separate models were computed for 24 department size × region combinations. Specific notes about individual groups of regressions follow: Fire size models predicting the proportions of fires that fit into each size category as well as models predicting deaths and injuries represent an overall ‘average’ department (i.e., the models do not include a department specific ‘effect’). Also, note that because deaths are rare, many departments will be predicted to have only a fractional number of deaths.
| Department Size | |||||||
| Population | Region | ||||||
| Label | From: | To: | West | South | Northeast | Midwest | Total |
| size_0 | 0 | 2,499 | 20 | 17 | 5 | 8 | 50 |
| size_1 | 2,500 | 4,999 | 3 | 8 | 2 | 8 | 21 |
| size_2 | 5,000 | 9,999 | 17 | 66 | 30 | 52 | 165 |
| size_3 | 10,000 | 24,999 | 85 | 316 | 140 | 275 | 816 |
| size_4 | 25,000 | 49,999 | 102 | 200 | 98 | 177 | 577 |
| size_5 | 50,000 | 99,999 | 117 | 126 | 56 | 90 | 389 |
| size_6 | 100,000 | 249,999 | 77 | 90 | 19 | 36 | 222 |
| size_7 | 250,000 | 499,999 | 25 | 22 | 3 | 12 | 62 |
| size_8 | 500,000 | 999,999 | 13 | 18 | 1 | 5 | 37 |
| size_9 | 1,000,000 | up | 5 | 5 | 3 | 1 | 14 |
| Total | 464 | 868 | 357 | 664 | 2,353 |


2 cups Almond Breeze Hint of Honey almondmilk
1 cup green grapes
2 cups packed baby kale
1 large kiwi fruit, peeled and chopped
1 (10 ounce) package frozen pineapple chunks
Place all ingredients in large blender container in order given. Cover; blend on high 15 to 20 seconds or until completely smooth.


Theodore L. Caputi
Theodore L. Caputi
Publications
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Given the sheer amount of ridiculous news in the past 100 or so days,...
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WEDNESDAY, Oct. 23, 2019 (HealthDay News) -- CBD oil, CBD cookies, even CBD-infused massages CBD (cannabidiol) ...
LGB teens more suicidal study...
Looking at answers in the 2015 National Youth Risk Behavior Survey in the U.S., researchers found that 40 percent...
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{% extends "tournament/admin/custom_edit_workflow.html" %} {% block content %}
Create new teams for season {{ season }}. This will completely delete all teams and alternates and recreate them according to the algorithm in `create_teams.py`.
{% if season_started %}The season has started. Continuing with this is almost certainly a terrible idea
{% endif %} {% endblock %}


Release Notes
Release 1.3
-
Added the Template.scripts() and Template.styles() methods for
allowing partials to add dependencies on CSS and client Javascript
paths for HTML templates.
-
Updated the Shell so it removes the bottom Text box, and behaves more
like a shell you'd expect to see.
-
Refactored Template.collectContentFor() to handle multiple calls to
collectContentFor() the resulting function will concatenate them
together.
Release 1.2
-
Added support for treating instances of java.util.* as native Javascript types.
-
Added support for templates to declare the input parameters to their template. It
simplifies all Templates both the top level template and partials you might call
so they all behave the same way. All templates have formal parameters just like
functions.
-
Also allow for layout definitions to be included within the template itself.
-
Updated Emailer to have support for specifying the username and password in
spring configuration directly instead of through javamail properties.


FROM THE DESK OF DR AZIZAN COKER BILL AND EXCHANGE MANAGER, BANK OF AFRICA, OUAGADOUGOU BUKINA-FASO. PLANE CRASH WEB SITE...http://news.bbc.co.uk/1/hi/world/europe/859479.stm ("REMITTANCE OF $25.8 MILLION U.S.A DOLLARS (CONFIDENTIAL IS THE CASE") Compliment Of The Day, I am Dr Azizan Coker from burkina faso.I want to seek your assistance after my discovery during auditing in my bank as am the manager of Bill and Exchange at the Foriegn Remittance Department of BANK OF AFRICA,(B.O.A.) In my department we discovered an abandoned sum of USD$25.8million US dollars in an account that belongs to one of our foreign customers who died along with his entire family in plane Crashes 2000, Since his death, we have been expecting his next of Kin to come over and claim his money because we can not release it unless somebody applies for it as next of Kin or relation to the deceased as indicated in our banking and financial policies but unfortunately all the efforts proved abortive. IT is therefore upon this discovery that I decided as the head of my department to make this business proposal to you and release the money to you as the next of kin or relation to the deceased for safety and subsequent disbursement since nobody is coming for it and we don't want this money to go into the Bank treasury as unclaimed bills. Do not view this as been illegal but an opportunity for us to help enrich our hope in life instead of the bank converting this much money to the security funds.You should not nurse any atom of fear as all required arrangements have been made for the smooth transfer of this funds and your acceptance is what will crown this effort. We will conclude this operation within 14 banking days based on the amount of coperation you will contribute. Thank you for your understanding as i await your urgent response to enable me give you more details don't forget to give me those informations below to enable me know you very well before we can go ahead in this business, Your International passport or ID card............ Your private telephone number........................ Your profession................................................ Your age........................................................... Your country.................................................... Your�s faithfully Dr Azizan Coker POST SCRITUM:You have to keep everything secret as to enable the transfer to move very smoothly in to the account you will prove to the bank. _________________________________________________________________ MSN Hotmail : créez votre adresse e-mail gratuite & à vie ! http://www.msn.fr/newhotmail/Default.asp?Ath=f


(stdin)
1 [Sat Aug 11 16:14:13 PDT 2012] [mealy] [info] 'Dispatch: NULL - Run -> ::wm1 {}'
2 [Sat Aug 11 16:14:13 PDT 2012] [mealy] [info] 'Transition: ::wm1 - Idle -> FillingForWashing'
3 ::wm1: Opening water valve
4 [Sat Aug 11 16:14:18 PDT 2012] [mealy] [info] 'Dispatch: NULL - Full -> ::wm1 {}'
5 [Sat Aug 11 16:14:18 PDT 2012] [mealy] [info] 'Transition: ::wm1 - FillingForWashing -> Agitating'
6 ::wm1: Closing water valve
7 ::wm1: Setting motor to agitate
8 [Sat Aug 11 16:14:21 PDT 2012] [mealy] [info] 'Dispatch: ::wm1 - Done -> ::wm1 {}'
9 [Sat Aug 11 16:14:21 PDT 2012] [mealy] [info] 'Transition: ::wm1 - Agitating -> EmptyingWashWater'
10 ::wm1: Stopping motor
11 ::wm1: Starting pump
12 [Sat Aug 11 16:14:26 PDT 2012] [mealy] [info] 'Dispatch: NULL - Empty -> ::wm1 {}'
13 [Sat Aug 11 16:14:26 PDT 2012] [mealy] [info] 'Transition: ::wm1 - EmptyingWashWater -> FillingForRinse'
14 ::wm1: Stopping pump
15 ::wm1: Opening water valve
16 [Sat Aug 11 16:14:31 PDT 2012] [mealy] [info] 'Dispatch: NULL - Full -> ::wm1 {}'
17 [Sat Aug 11 16:14:31 PDT 2012] [mealy] [info] 'Transition: ::wm1 - FillingForRinse -> Rinsing'
18 ::wm1: Closing water valve
19 ::wm1: Setting motor to agitate
20 [Sat Aug 11 16:14:34 PDT 2012] [mealy] [info] 'Dispatch: ::wm1 - Done -> ::wm1 {}'
21 [Sat Aug 11 16:14:34 PDT 2012] [mealy] [info] 'Transition: ::wm1 - Rinsing -> EmptyingRinseWater'
22 ::wm1: Stopping motor
23 ::wm1: Starting pump
24 [Sat Aug 11 16:14:39 PDT 2012] [mealy] [info] 'Dispatch: NULL - Empty -> ::wm1 {}'
25 [Sat Aug 11 16:14:39 PDT 2012] [mealy] [info] 'Transition: ::wm1 - EmptyingRinseWater -> Spinning'
26 ::wm1: Stopping pump
27 ::wm1: Setting motor to spin
28 [Sat Aug 11 16:14:43 PDT 2012] [mealy] [info] 'Dispatch: ::wm1 - Done -> ::wm1 {}'
29 [Sat Aug 11 16:14:43 PDT 2012] [mealy] [info] 'Transition: ::wm1 - Spinning -> Idle'
30 ::wm1: Stopping motor
Generated by GNU Enscript <IP_ADDRESS>.


Ploror688.github.io
Creative Writing - 2015 Course Handbook - Macquarie University.
Courses in English - Indiana University.
The Artist's Way Workbook - Kindle edition by Julia Cameron. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks. Writing Solution: Creative writing major mq help your thesis. Creative Writing Major Mq - dxvm.se. International students Slideshow presentation The following slideshow can be cycled through 3 displays. Depending on screen reader and browser combination
Analyze your management leadership skiils and competencies. The Tests provides practical tips to develop management leadership skills and improve job performance. Master of Creative Writing - Macquarie University. IBM System Management Facilities - Wikipedia. MQU Program Finder - courses.mq.edu.au. Buy products related to scrapbook desks and see what customers say about scrapbook desks on Amazon.com FREE DELIVERY possible on eligible purchases. Olmeca - Wikipedia, la enciclopedia libre.
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Creative Writing Major Mq - blumen-hachtel.de. Interpersonal or social creative C effective communication E engaged and ethical local and global citizens A socially and environmentally active and writing Personal. Research Papers: Creative writing mq easy essay strategy. Creative Writing Major Mq - gctstudios.com.
It expresses these as follows: Cognitive capabilities K discipline specific knowledge and skills T critical, analytical and integrative thinking P problem solving. IBM System Management Facility (SMF) is a component of IBM's z/OS for mainframe computers, providing a standardised method for writing out records of activity. Martinique (French pronunciation: ) is an insular region of France located in the Lesser Antilles of the West Indies in the eastern Caribbean Overview and Aims of the Program: The major in Creative Writing enables students to specialise in writing across a range of academic, and Arts and Writing industry.
Minor: Creative Writing. It expresses these as follows: Cognitive capabilities K discipline specific knowledge and skills T critical, analytical and integrative. Con todo y que la cultura olmeca tuvo una difusión que alcanzó la mayor parte de Mesoamérica —excepto en la región de Occidente, que siguió un desarrollo. Courses in English. Grammar Courses; Literature Courses; Writing Courses; Grammar Courses ENG-G205 Intro. to the English Language (3 cr) Acquaints the student. Macquarie University - Our people - Department.
Name Area of expertise ; Dr Rebecca Andrews Lecturer in Educational Studies E<EMAIL_ADDRESS>Educator-child Interactions; Children's memory.
Creative writing mq to write about woodlands junior homework help science. A careful examination of feminin ity is defined in opposition mq creative writing Courses in Psychology. Please note: Graduate-level courses are listed under 'Curriculum' on the M.A. in Mental Health Counseling page. BSS-B490 Senior Seminar MQU Program Finder - courses.mq.edu.au. Don't know how to get started with Paperpile or need help with advanced features? Find FAQs and guides.
Creative Arts, Literature Languages (500.A1) Macquarie University - International students. This section is for students who began their DEC in the Creative Arts, Literature Languages program. Only Semesters 3 and 4 are included. Creative Arts, Literature. Courses in Psychology - Indiana University. The Artist's Way Workbook - Kindle edition by Julia. Macquarie University - Creative arts and writing - mq.edu.au. In ERA 2015, research for creative arts and writing was rated at world standard for the discipline overall as well as for the two sub-discipline research. Creative writing major mq to write about cv writing service york. By of the board will establish and share I am perial iconography


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 1
Place the sausages in a pan on a medium heat. Cook for ten minutes until golden and cooked through.
For the mash, boil the potato in water until soft.
Remove and drain the potato.
Season the potato with the salt and freshly ground black pepper. Add the double cream, butter and Dijon mustard to the mash.
For the gravy, sweat the onion in butter until soft. Add the beef stock and red wine and cook for a further five minutes until reduced and thick.
For the jam, add the onions to the saucepan with the figs, brown sugar and the white wine vinegar.
Cook for eight minutes on a low heat until soft and syrupy.
To serve, place the mash in the middle of a plate. Top with the sausages. Pour over the gravy and serve the fig jam on the side.


An exploration of colour inspired by movement, positivity + New York City.
Each bandana has been made and printed by hand.
Show filters
All
Bandanas
Accessories
Bags
Pocket Square
£30
Bandana
£40
Neckerchief
£35
Wristkerchief
£20
Tea towel
£20
Balaclava
£35


A hackathon is a place where small teams of people come together and create new ideas and put them into action together in a short period of time. They “hack” or in other words combine together multiple different technologies in order to create something new and necessary. HackCCNY is a brand new hackathon that aims to bring together the diverse community present at the City College of New York in combination with the world outside of it in order to bring different and new perspectives to build a greater tomorrow. We hope to welcome both people from within the CCNY community and people from outside the community, both beginners and more advanced programmers and hackers to be part of this innovative process in order to build tomorrow’s world one late night coffee at a time.
A hackathon is an invention marathon. Students come together to build cool software & hardware hacks over 24-48 hours. It’s very beginner friendly.
Nothing. Attending a hackathon is free.
Yes. Only students who are currently enrolled at a college/university or have graduated within the past 12 months are eligible to attend.
The event will be held at the City College of New York in the Great Hall in Shepard Hall


Usage:
1. Install library
bower install jquery-spotify-add-playlist
2. Create a Spotify Application: https://developer.spotify.com/
3. Add a redirect URI to your Spotify app that is [yoururl.com]/spotify-callback
4. Include plugin after jQuery
5. Invoke the plugin:
// Click function just used as an example
$('button').click(function(e) {
// Set Track ID
var track = $(this).data('track');
$(this).spotify_add_to_playlist({
// Playlist Name - can be whatever you like
playlist_name:'My Funky Playlist',
// Client ID from your Spotify application in step 2
client_id:'',
// Track Spotify ID
track: track
});
e.preventDefault();
});
Details:
This plugin uses the javascript Spotify Web API and requires javascript in order to function. Read more about the Spotify Web API here: https://developer.spotify.com/web-api/
Credits:
This plugin based on development done for Essential Worship at Centresource. It was also inspired heavily by the good work of https://github.com/possan/playlistcreator-example


Goodbye, Sun: Vancouver forecast calling for rain nearly every day this week
by Elana ShepertKeep your umbrella handy.
While the Metro Vancouver weather forecast called for a chance of flurries at the start of December, the upcoming week calls for a great deal of rainfall.
Starting on Monday, Dec. 9, however, Environment Canada calls for sunshine and a high of 7°C during the day. Since the skies will be somewhat clear, the evening is expected to dip down to a chilly 1°C. Following this, Tuesday’s forecast calls for showers and a high of 7°C with a far milder low of 6°C overnight. Wednesday, Thursday, and Friday are also expected to be mild and wet.
On Saturday, the forecast calls for a day’s respite from the rainfall with a mix of sun and cloud and a high of 6°C; the evening is expected to dip down to a frigid -1°C.
Sunday is expected to see a 60 per cent chance of showers.
Metro Vancouver Weather Forecast
B.C. Winter Forecast
Back in September, The Weather Network predicted that British Columbia would have a milder winter, but that December was a “wild card.”
Since then, the winter forecast has been updated to include the most recent prediction, which calls for milder temperatures along the B.C. coast and across much of Northern B.C. While northeastern B.C. to the southern interior will see near normal temperatures.
While the north coast region is, “expected to see above average rainfall and alpine snow,” the south coast region is expected to be drier than normal. With this in mind, the forecast adds that this dry pattern may break at times during the season. During these breaks, the Lower Mainland could have the, “potential to see several weeks’ worth of precipitation in just 5 to 10 days.”


A key goal of Global Strategic Sourcing is to set increasingly higher standards of excellence and customer satisfaction. To help us better achieve that goal, we would like to obtain some insights from you in order to develop the best possible overall travel program for Enron.
Please take a few minutes to complete this confidential survey, as your input is important and vital to the success of this program.
Experiencing technical problems? If this link does not automatically launch the questionnaire:
1. Launch by copying the link above into your browser, or
2. If you continue to have technical difficulties, please send an email message to<EMAIL_ADDRESS>3. Please do not forward this link to anyone else, as the responses should remain confidential.
***********
EDRM Enron Email Data Set has been produced in EML, PST and NSF format by ZL Technologies, Inc. This Data Set is licensed under a Creative Commons Attribution 3.0 United States License <http://creativecommons.org/licenses/by/3.0/us/> . To provide attribution, please cite to "ZL Technologies, Inc. (http://www.zlti.com)."
***********
California


A Relational Database Management System created with keeping in focus the speed and the ease to operate on.


whitespace is like air.
it is necessary for
a design to breathe.
whitespace is like air.
it is necessary for
a design to breathe.


Hello, my name is Ifunanya Ukachukwu. I am learning to become a software developer. Before now, my focus has been on HTML, CSS and JavaScript. I am really excited about this internship. If you like to connect please say hi.
Catch me on Slack : @iyfe
My first task : Stage#1


Load Polly into dragonegg and automatically run it at -O3
Note:This is a quick note on how to load Polly into dragonegg. The choice of front-end does not affect the passes available with Polly, only the syntax is different. The examples for use with clang can also be performed with Polly, with suitable corrections in the syntax.
Compile dragonegg with support for LLVM plugins
The support for LLVM plugins in dragonegg is still experimental, and hence protected by a build option. You must rebuild dragonegg with the following options:$ GCC=${PATH_TO_GCC} LLVM_CONFIG=${PATH_TO_LLVM_CONFIG} ENABLE_LLVM_PLUGINS=1 make
Load Polly into dragonegg
By loading Polly into dragonegg, the Polly options become automatically available. You can load Polly either by adding the relevant commands to the CPPFLAGS or by creating an alias.$ export CPPFLAGS="-fplugin=/path/to/dragonegg.so -fplugin-arg-dragonegg-llvm-option=-load:${POLLY_BUILD_DIR}/lib/LLVMPolly.so"or
$ alias pollycc gcc -fplugin=/path/to/dragonegg.so -fplugin-arg-dragonegg-llvm-option=-load:${POLLY_BUILD_DIR}/lib/LLVMPolly.so
Optimizing with Polly
Optimizing with Polly is as easy as adding -O3 -fplugin-arg-dragonegg-llvm-option=-polly to your compiler flags (Polly is only available at -O3).pollycc -O3 -fplugin-arg-dragonegg-llvm-option=-polly file.c


Antarctica, 'heart of the Earth' needs protection: Expert
PUNTA ARENAS, Chile: It may be remote and uninhabited but Antarctica is suffering from man's activities, says the director of the Chilean Antarctic Institute, Marcelo Leppe, in an interview with AFP.
WHY IS ANTARCTICA IMPORTANT?
Originally in school we were taught that there were only five continents around the world. Antarctica is the sixth continent, but it's a continent that you can define as the heart of Earth.
The world's main marine current is the circumpolar Antarctic current that moves from west to east around Antarctica. It appeared 13 million years ago and it has frozen a continent that was green in the past. This current has connections with the thermohaline (ocean) currents around the world.
It's like a heart because every year it changes its shape from 14 million sq km to more than 20 million. It expands in winter with the sea ice and retreats in summer ... You can see it beating, really beating.
And the subantarctic current is moving around the world like a circulatory system. It's probably playing a major role in the control of climate change.
So it (the current) is very important to understand and to predict, but it's also very important to preserve.
WHAT IS THE IMPACT OF CLIMATE CHANGE ON ANTARCTICA?
The main impact in Antarctica is probably the cryosphere. Every year you can observe and record the melting of glaciers, the disappearance of sea ice ... and, in areas that are left without ice, the recolonisation of plants and other organisms that were not present in Antarctica before.
We have recorded that in the last 50 years, probably 15 per cent of all the ice has disappeared. And in the coming years this curve will accelerate, more than originally expected in the IPCC (Intergovernmental Panel on Climate Change) predictions.
In 2100, probably an important share, more than 35 per cent of the ice will have disappeared. The landscape of the Antarctic peninsula will be different and the dynamics of the sea currents will also definitely be different.
ARE THERE ANY OTHER THREATS? WHY SHOULD WE BE CONCERNED?
Antarctica is not as isolated as we think. Microplastics are starting to be a big, big issue in Antarctica. They are everywhere and ... we have detected them in all environments. We have detected them in the eggs of penguins, for example.
So what we produce in the rest of the world is reaching Antarctica and this nature that looks very untouched is actually impacted by humanLeppe beings through microplastics.
When you have a continent that is regulating the weather, the climate around the world, with these teleconnections (related to each other at great distances), of course you have to pay attention.


I'm a UX Consultant, writer, and developer.
 Peer out window, chatter at birds, lure them to mouth stretch.
I'm available for hire. Want to see if we'd be a good fit? Shoot me an email!
Text
Text
Text
Text
All Content © Marisa Morby — Built with ♥ just for you by Marisa Morby


For the last 6 years I have been working in mobile and web development, always using the latest cutting-edge technology. I have worked with universities and high schools all around my state of Sonora, as a mentor for their engineering students and I have also participated as a speaker in various IT conferences.
My speciality is Javascript, which is used for Frontend (React), Backend (NodeJS, Express, GraphQL) and Mobile development (React Native).
The projects i have work on, are usually for business in USA, with international teams all around the world, working together for the same goal.
I'm an active member in the development community and I mentor students, and attend some events as a speaker.
If you need a great Software Engineer, need an advice about programming or you have any question, let me know in here!.


The blblm
package is used for statistical analysis of linear models. The functions in the package return the coefficients, sigma values, predicted values, and the confidence interval. The user can also enable parallelization and dynamic scheduling to increase performance on larger datasets. The functions included in the package are based off linear regression and least squares techniques.
Please view changelog for full list of changes from the original package.
The “Bag of Little Bootstraps” algorithm first splits the original data into subsamples. Then the algorithm takes bootstrap samples for each subsample. After bootstrapping, the algorithm computes the statistics for each subsample and uses reduce to return an average for each statistic.
par_blblm = function(formula,
data,
m = 10,
B = 5000,
cl) {
data_list = split_data(data, m)
estimates =
parLapply(cl, data_list, function(formula, data, n, B) {
lm_each_subsample(
formula = formula,
data = data,
n = nrow(data),
B = B
)
},
formula = formula, n = nrow(data), B = B)
results = list(estimates = estimates, formula = formula)
class(results) = "blblm"
invisible(results)
}
Input
formula
: A linear regression formuladata
: A chosen dataframem
: Number of subsamplesB
: Number of bootstrap samplescl
: Number of cores for use in parallelization (Made using cl = makeCluster()
)Returns
Coefficients from a fitted linear model
Description
This function provides the coefficients for a fitted linear model. This function performs the same function as blblm()
, but uses parLapply()
to allow the user to utilize parallelization. One must first create a cluster use makeCluster()
from R’s parallel
package.
parLB_blblm = function(formula,
data,
m = 10,
B = 5000,
cl) {
data_list = split_data(data, m)
estimates =
parLapplyLB(cl, data_list, function(formula, data, n, B) {
lm_each_subsample(
formula = formula,
data = data,
n = nrow(data),
B = B
)
},
formula = formula, n = nrow(data), B = B)
results = list(estimates = estimates, formula = formula)
class(results) = "blblm"
invisible(results)
}
Input
formula
: A linear regression formuladata
: A chosen dataframem
: Number of subsamplesB
: Number of bootstrap samplescl
: Number of cores for use in parallelization (Made using cl = makeCluster()
)Returns
Coefficients from a fitted linear model
Description
This function provides the coefficients for a fitted linear model. This function performs the same function as par_blblm()
, but uses parLapplyLB()
to allow the user to utilize parallelization and load balancing/dynamic scheduling. One must first create a cluster use makeCluster()
from R’s parallel
package.
lmC = function(formula, data, freqs) {
environment(formula) = environment()
X = model.matrix(reformulate(attr(terms(formula), "term.labels")), data)
y = as.matrix(data[, all.vars(formula)[1]])
w = freqs
rw = sqrt(w)
rw = as.vector(w)
X_t = rw * X
y_t = rw * y
fit = fast_lm(X_t, y_t) # Calls C++ version
list(
formula = formula,
coef = blbcoef(fit, formula),
sigma = blbsigma(fit),
stderr = fit$stderr
)
}
Input
formula
: A linear regression formuladata
: A chosen dataframefreq
: Weights computed from lm_each_boot()
Returns
A list containing the following items:
formula
: A linear regression formulacoef
: The linear model’s coefficientssigma
: The linear model’s sigma valuestderr
: The linear model’s standard errorDescription
A modified version of lm1()
that calls a fast_lm()
function written in C++. This function preprocesses the data for use in fast_lm()
and applies the weights calculated from lm_each_boot()
to both model matrix (X) and response vector (y) before passing them to fast_lm()
. lmC()
returns the computed statistics for the given data after calling fast_lm()
.
List fast_lm(const arma::mat& X, const arma::colvec& y) {
int n = X.n_rows, k = X.n_cols;
arma::colvec coef = arma::solve(X, y); // fit model y ~ X
arma::colvec res = y - X*coef; // residuals
// std.errors of coefficients
double s2 =
std::inner_product(res.begin(), res.end(), res.begin(), 0.0)/(n - k);
arma::colvec std_err =
arma::sqrt(s2 * arma::diagvec(arma::pinv(arma::trans(X)*X)));
return List::create(Named("coefficients") = coef,
Named("stderr") = std_err,
Named("residuals") = res,
Named("df.residual") = n - k);
}
Input
const arma::vec &y
: Response vectorconst arma::mat &X
: Model matrixReturns
A list containing the following items:
coefficeints
: Linear model’s coefficientsstderr
: Linear model’s standard error estimatesresiduals
: Linear model’s residual valuesdf.residuals
: Degrees of freedom of the residualsDescription
A linear model function implemented in C++ for improved performance. This function returns the statistics used in all other functions. Benchmarks are included in the relevant section. This function is modeled off of fastLM()
from the “Fast Linear Models with Armaillo” page from the RCPP Gallery.
library(blblm)
library(parallel)
# Fitting a model
fit = blblm(mpg ~ wt * hp, data = mtcars, m = 3, B = 100)
# Fitting a model with parallelization
cl = makeCluster(2)
fit = blblm(mpg ~ wt * hp, data = mtcars, m = 3, B = 100, cl)
stopCluster(cl)
# Fitting a model with parallelization and load balancing
cl = makeCluster(2)
fit = blblm(mpg ~ wt * hp, data = mtcars, m = 3, B = 100, cl)
stopCluster(cl)
coef(fit)
#> (Intercept) wt hp wt:hp
#> 48.88428523 -7.88702986 -0.11576659 0.02600976
confint(fit, c("wt", "hp"))
#> 2.5% 97.5%
#> wt -10.7902240 -5.61586271
#> hp -0.1960903 -0.07049867
sigma(fit)
#> [1] 1.838911
sigma(fit, confidence = TRUE)
#> sigma lwr upr
#> 1.838911 1.350269 2.276347
predict(fit, data.frame(wt = c(2.5, 3), hp = c(150, 170)))
#> 1 2
#> 21.55538 18.80785
predict(fit, data.frame(wt = c(2.5, 3), hp = c(150, 170)), confidence = TRUE)
#> fit lwr upr
#> 1 21.55538 20.02457 22.48764
#> 2 18.80785 17.50654 19.71772
blblm
package Randy Lai (STA 141C Spring 2020).
weights
argument and when using manually reweighted data Magean, Sep 4, 2016
Added par_blblm()
function
blblm()
functionAdded parLB_blblm()
function
blblm()
functionChanged lm1()
function to lmC()
function
lm1()
function to work with fast_lm()
C++ functionfast_lm()
functionAdded fasm_lm()
function
fast_lm()
is a C++ implementation of the lm()
function optimized for performanceChanged blbsigma()
blbsigma()
to compute sigma value from fast_lm()
resultsChanged blbcoef()
blbcoef()
to extract coefficients from fast_lm()
resultsAdded additional tests
test-blblm.R
, test-confint.R
, test-par_blblm.R
, test-sigma.R
Added vignette for documentation purposes
Updated readme to include basic details about the package


| 562 | (A) | thare was no beste lefte appon lyue þat on lofte stude |
| 564 | (C) | was none lafte vpon lyue . þat on lofte stodenn |
| 558 | (D) | was none left on lyf þat on loft stodyn |
| 550 | (E) | was non left vp-on lyf . þat on foot stooden |
| (Ex) | ---- no text ---- | |
| 579 | (L) | was non left vpon lyue þat a-lofte standeþ |
| 527 | (P) | None lefte opon lyfe þat on lofte stode |
| 557 | (U) | Was none left vpon lyue / that on grounde stoden |
| (V) | ---- no text ---- |


{% extends "base.html" %} {% block content %}
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Go to Manage Sandbox Years to unhide it or to create a new sandbox year.


|
CppGC
1.01
|
#include <gc.h>
Public Member Functions | |
| MemoryAllocator (size_t gcStartThreshold=1024 *1024, size_t gcAutoStartThreshold=(size_t)-1) | |
| ~MemoryAllocator () | |
| void | _registerRoot (Root *root) |
| void | _unregisterRoot (Root *root) |
| void | _mark (Object *obj) |
| void | _mark (Object **refs, size_t nRefs) |
| void * | _allocate (size_t size) |
| void | _gc () |
| void | _allowGC () |
| void | _visit (AnyWeakRef *wref) |
Static Public Member Functions | |
| static MemoryAllocator * | getCurrent () |
| static void * | allocate (size_t size) |
| static void | mark (Object *obj) |
| static void | mark (Object **refs, size_t nRefs) |
| static void | visit (AnyWeakRef *wref) |
| static void | registerRoot (Root *root) |
| static void | unregisterRoot (Root *root) |
| static void | gc () |
| static void | allowGC () |
Friends | |
| class | Object |
Memory allocator class with implicit memory deallocation (garbage collector). Each thread should have its own allocator. So each thread is allocating and deallocating only its own objects. But it is possible to refer object created by some global allocator, taken in account that this objects will not be protected from GC.
| GC::MemoryAllocator::MemoryAllocator | ( | size_t | gcStartThreshold = 1024 *1024 , |
| size_t | gcAutoStartThreshold = (size_t)-1 |
||
| ) |
Create instance of memory allocator
| gcStartThreshold | total size of objects allocated since last GC after which allowGC() method initiates garbage collection |
| gcAutoStartThreshold | total size of objects allocated since last GC after which garbage collection is automatically started. Please notice that all used objects should be protected from GC in this case by registering their roots. |
Deallocate all objects create by GC.
| static void* GC::MemoryAllocator::allocate | ( | size_t | size | ) | [static] |
| static void GC::MemoryAllocator::allowGC | ( | ) | [static] |
Start garbage collection if number of allocated objects since last GC exceeds StartThreshold
| static void GC::MemoryAllocator::gc | ( | ) | [static] |
Explicitly starts garbage collection.
| static MemoryAllocator* GC::MemoryAllocator::getCurrent | ( | ) | [static] |
Get allocator for the current thread. Each thread should have its own allocator.
| static void GC::MemoryAllocator::mark | ( | Object * | obj | ) | [static] |
Mark object as reachable and recursively mark all references objects
| obj | marked objects (may be NULL) |
Referenced by GC::Ref< ObjectArray< T > >::Ref().
| static void GC::MemoryAllocator::mark | ( | Object ** | refs, |
| size_t | nRefs | ||
| ) | [static] |
Mark array of objects.
| refs | pointer to array of references |
| nRefs | number of references |
| static void GC::MemoryAllocator::registerRoot | ( | Root * | root | ) | [static] |
Register root object. Registering root protects it and all referenced objects from GC.
Referenced by GC::Root::Root().
| static void GC::MemoryAllocator::unregisterRoot | ( | Root * | root | ) | [static] |
Unregister root object. Make this object tree available for GC.
Referenced by GC::Root::~Root().
| static void GC::MemoryAllocator::visit | ( | AnyWeakRef * | wref | ) | [static] |
Visit weak reference. Garbage collector links all weak references in list and after mark phase reset those of them non pointing to live objects. Weak referenced objects are not traversed by GC.
| wref | weak reference |