# my net house

WAHEGURU….!

## Deploy Micro-service in Seconds Using Falcon

Falcon framework is great when you have to build micro-services in Python, All we need to do is create a Class that would provide the specific Purpose to Establish one Micro-service as Route and add the route to the API would be able to server as deploy multiple modes inside the Falcon Architecture as well.


# sample.py

import falcon

class QuoteResource:
def on_get(self, req, resp):
"""Handles GET requests"""
quote = {
'quote': (
"I've always been more interested in "
"the future than in the past."
),
'author': 'Grace Hopper'
}

resp.media = quote
class HelpObject:
def on_get(self, req, resp):
"""Handles GET requests"""
quote = {
'quote': (
"I've always been more interested in "
"the future than in the past."
),
'author': 'Arshpreet Singh'
}

resp.media = quote

api = falcon.API()
<span id="mce_SELREST_start" style="overflow:hidden;line-height:0;">&#65279;</span>


## IBM hyper ledger Business Card creation and Code familiarity [inbetween]

model/*.cto file- This file is able to describe Asset, Participant and Transactions those occur at that business network as well .

• Namespace
• Resources
• Imports from other namespaces, as required

If your model is very large, you can have multiple .cto model files, as necessary. Every .cto model file must include a single namespace and at least one resource definition.

NameSpace: NameSpace is just naming convention for the File-System. For each of your .cto file namespace would be an unique name for the file. Each of the resources available in the file would be taken from that namespace.

Resource: Now we have following kind of resources we can use.

Asset: A business network Asset(Example Bit-coin is an Asset in Bit-coin-Network)

Event: Notification of something happening in the network

Enumerated Type: A set of named Values

Concept: Any object you want to model that is not one of the other types

Each resource type corresponds to its model type of the same name (for example, asset is used to model an Asset, participant models a Participant, etc.)

asset Vehicle identified by vehicle_id {

}

## Learning Dataframes in Julia

Week4_DataF

### Week 4 – Working with Distributions and DataFrames.¶

In [1]:
# Import the required packages
using Distributions, DataFrames

In [2]:
# Seed the random number generator
srand(1234);

In [3]:
# Question 4: Create the 3 x 30 array named array_1
# 30 rows and 3 columns array
array_1 = [rand(30) rand(30) rand(30)]
size(array_1)
array_1

Out[3]:
30×3 Array{Float64,2}:
0.590845   0.931115   0.643704
0.766797   0.438939   0.401421
0.566237   0.246862   0.525057
0.460085   0.0118196  0.61201
0.794026   0.0460428  0.432577
0.854147   0.496169   0.082207
0.200586   0.732      0.199058
0.298614   0.299058   0.576082
0.246837   0.449182   0.218177
0.579672   0.875096   0.362036
0.648882   0.0462887  0.204728
0.0109059  0.698356   0.932984
0.066423   0.365109   0.827263
⋮
0.0566425  0.404953   0.0396356
0.842714   0.499531   0.79041
0.950498   0.658815   0.431188
0.96467    0.515627   0.137658
0.945775   0.260715   0.60808
0.789904   0.59552    0.255054
0.82116    0.292462   0.498734
0.0341601  0.28858    0.0940369
0.0945445  0.61816    0.52509
0.314926   0.66426    0.265511
0.12781    0.753508   0.110096
0.374187   0.0368842  0.834362
In [4]:
# Question 5: Mean and variance of column 1
mean_column_1 = mean(array_1[:,1])
var_column_1=var(array_1[:,1])
println("mean=",mean_column_1)
println("var=",var_column_1)

mean=0.5014887976938368
var=0.10653465363277906

In [5]:
# Question 5 (continued): Mean and variance of column 2
mean_column_2 = mean(array_1[:,2])
var_column_2=var(array_1[:,2])
println("mean=",mean_column_2)
println("var=",var_column_2)

mean=0.4160447968360426
var=0.06360439983290869

In [6]:
# Question 5 (continued): Mean and variance of column 3
mean_column_3 = mean(array_1[:,3])
var_column_3=var(array_1[:,3])
println("mean=",mean_column_3)
println("var=",var_column_3)

mean=0.4372634519427959
var=0.07568707224628725

In [7]:
# Question 6: Import array_1 into a DataFrame named df
df = DataFrame(array_1)

Out[7]:
x1 x2 x3
1 0.5908446386657102 0.9311151512445586 0.6437042811826996
2 0.7667970365022592 0.43893895933102156 0.40142056533714965
3 0.5662374165061859 0.24686248047491066 0.5250572942486489
4 0.4600853424625171 0.011819583479107054 0.6120098074984683
5 0.7940257103317943 0.046042826396498704 0.43257652982765626
6 0.8541465903790502 0.496168672722459 0.0822070287962946
7 0.20058603493384108 0.7320003814997245 0.19905799020907944
8 0.2986142783434118 0.29905752670238184 0.5760819730593403
9 0.24683718661000897 0.4491821088563024 0.21817706596841413
10 0.5796722333690416 0.8750962647851142 0.3620355262053865
11 0.6488819502093455 0.046288741031345504 0.20472832290217324
12 0.010905889635595356 0.6983555060532487 0.93298350850828
13 0.06642303695533736 0.3651093677271471 0.8272627957034728
14 0.9567533636029237 0.3024777928234499 0.09929915955881308
15 0.646690981531646 0.3725754415996787 0.6342997886044144
16 0.11248587118714015 0.15050782744925795 0.1327153585755645
17 0.2760209506672211 0.14732938279328955 0.7751941503856596
18 0.6516642063795697 0.2834013103457036 0.8692366891234362
19 0.05664246860321187 0.40495283364883794 0.039635617270926904
20 0.8427136165865521 0.49953074411487797 0.7904095314876494
21 0.9504984071553011 0.6588147837334961 0.43118828904466633
22 0.9646697763820897 0.5156272179795256 0.1376583132625555
23 0.9457754052519123 0.26071522632820776 0.6080803126880718
24 0.7899036826169576 0.5955204840509289 0.2550540600167448
25 0.8211604203482923 0.2924615242315285 0.4987340031883092
26 0.03416010848943718 0.2885798506061561 0.09403688346569439
27 0.09454448946400307 0.6181597973815087 0.5250899072103514
28 0.31492622391998415 0.6642598175011505 0.2655109248498748
29 0.12780989889368866 0.7535081177709988 0.11009621399607639
30 0.374186714831074 0.03688418241886171 0.8343616661080064
In [8]:
# check available names and fieldnames in Julia, Python's alternative
f_name =fieldnames(df)
name=names(df)
println(f_name,name)

Symbol[:columns, :colindex]Symbol[:x1, :x2, :x3]

In [9]:
# Accessing different columns of df
df[:x3]

Out[9]:
30-element Array{Float64,1}:
0.643704
0.401421
0.525057
0.61201
0.432577
0.082207
0.199058
0.576082
0.218177
0.362036
0.204728
0.932984
0.827263
⋮
0.0396356
0.79041
0.431188
0.137658
0.60808
0.255054
0.498734
0.0940369
0.52509
0.265511
0.110096
0.834362
In [10]:
# Question 7: Change the names of the columns to Var1, Var2, and Var3
rename!(df,Dict(:x1=>:Var1,:x2=>:Var2,:x3=>:Var))

Out[10]:
Var1 Var2 Var
1 0.5908446386657102 0.9311151512445586 0.6437042811826996
2 0.7667970365022592 0.43893895933102156 0.40142056533714965
3 0.5662374165061859 0.24686248047491066 0.5250572942486489
4 0.4600853424625171 0.011819583479107054 0.6120098074984683
5 0.7940257103317943 0.046042826396498704 0.43257652982765626
6 0.8541465903790502 0.496168672722459 0.0822070287962946
7 0.20058603493384108 0.7320003814997245 0.19905799020907944
8 0.2986142783434118 0.29905752670238184 0.5760819730593403
9 0.24683718661000897 0.4491821088563024 0.21817706596841413
10 0.5796722333690416 0.8750962647851142 0.3620355262053865
11 0.6488819502093455 0.046288741031345504 0.20472832290217324
12 0.010905889635595356 0.6983555060532487 0.93298350850828
13 0.06642303695533736 0.3651093677271471 0.8272627957034728
14 0.9567533636029237 0.3024777928234499 0.09929915955881308
15 0.646690981531646 0.3725754415996787 0.6342997886044144
16 0.11248587118714015 0.15050782744925795 0.1327153585755645
17 0.2760209506672211 0.14732938279328955 0.7751941503856596
18 0.6516642063795697 0.2834013103457036 0.8692366891234362
19 0.05664246860321187 0.40495283364883794 0.039635617270926904
20 0.8427136165865521 0.49953074411487797 0.7904095314876494
21 0.9504984071553011 0.6588147837334961 0.43118828904466633
22 0.9646697763820897 0.5156272179795256 0.1376583132625555
23 0.9457754052519123 0.26071522632820776 0.6080803126880718
24 0.7899036826169576 0.5955204840509289 0.2550540600167448
25 0.8211604203482923 0.2924615242315285 0.4987340031883092
26 0.03416010848943718 0.2885798506061561 0.09403688346569439
27 0.09454448946400307 0.6181597973815087 0.5250899072103514
28 0.31492622391998415 0.6642598175011505 0.2655109248498748
29 0.12780989889368866 0.7535081177709988 0.11009621399607639
30 0.374186714831074 0.03688418241886171 0.8343616661080064
In [11]:
### we can also tail function see last required entries
tail(df,20)

Out[11]:
Var1 Var2 Var
1 0.6488819502093455 0.046288741031345504 0.20472832290217324
2 0.010905889635595356 0.6983555060532487 0.93298350850828
3 0.06642303695533736 0.3651093677271471 0.8272627957034728
4 0.9567533636029237 0.3024777928234499 0.09929915955881308
5 0.646690981531646 0.3725754415996787 0.6342997886044144
6 0.11248587118714015 0.15050782744925795 0.1327153585755645
7 0.2760209506672211 0.14732938279328955 0.7751941503856596
8 0.6516642063795697 0.2834013103457036 0.8692366891234362
9 0.05664246860321187 0.40495283364883794 0.039635617270926904
10 0.8427136165865521 0.49953074411487797 0.7904095314876494
11 0.9504984071553011 0.6588147837334961 0.43118828904466633
12 0.9646697763820897 0.5156272179795256 0.1376583132625555
13 0.9457754052519123 0.26071522632820776 0.6080803126880718
14 0.7899036826169576 0.5955204840509289 0.2550540600167448
15 0.8211604203482923 0.2924615242315285 0.4987340031883092
16 0.03416010848943718 0.2885798506061561 0.09403688346569439
17 0.09454448946400307 0.6181597973815087 0.5250899072103514
18 0.31492622391998415 0.6642598175011505 0.2655109248498748
19 0.12780989889368866 0.7535081177709988 0.11009621399607639
20 0.374186714831074 0.03688418241886171 0.8343616661080064
In [12]:
# Creatring Second DataFrame
df2=DataFrame(tail(df,20))

Out[12]:
Var1 Var2 Var
1 0.6488819502093455 0.046288741031345504 0.20472832290217324
2 0.010905889635595356 0.6983555060532487 0.93298350850828
3 0.06642303695533736 0.3651093677271471 0.8272627957034728
4 0.9567533636029237 0.3024777928234499 0.09929915955881308
5 0.646690981531646 0.3725754415996787 0.6342997886044144
6 0.11248587118714015 0.15050782744925795 0.1327153585755645
7 0.2760209506672211 0.14732938279328955 0.7751941503856596
8 0.6516642063795697 0.2834013103457036 0.8692366891234362
9 0.05664246860321187 0.40495283364883794 0.039635617270926904
10 0.8427136165865521 0.49953074411487797 0.7904095314876494
11 0.9504984071553011 0.6588147837334961 0.43118828904466633
12 0.9646697763820897 0.5156272179795256 0.1376583132625555
13 0.9457754052519123 0.26071522632820776 0.6080803126880718
14 0.7899036826169576 0.5955204840509289 0.2550540600167448
15 0.8211604203482923 0.2924615242315285 0.4987340031883092
16 0.03416010848943718 0.2885798506061561 0.09403688346569439
17 0.09454448946400307 0.6181597973815087 0.5250899072103514
18 0.31492622391998415 0.6642598175011505 0.2655109248498748
19 0.12780989889368866 0.7535081177709988 0.11009621399607639
20 0.374186714831074 0.03688418241886171 0.8343616661080064
In [13]:
# Question 9: Calculate simple descriptive statistics of all the columns in df2 using the describe() function
describe(df2)

Var1
Summary Stats:
Mean:           0.484341
Minimum:        0.010906
1st Quartile:   0.108001
Median:         0.510439
3rd Quartile:   0.826549
Maximum:        0.964670
Length:         20
Type:           Float64

Var2
Summary Stats:
Mean:           0.397753
Minimum:        0.036884
1st Quartile:   0.277730
Median:         0.368842
3rd Quartile:   0.601180
Maximum:        0.753508
Length:         20
Type:           Float64

Var
Summary Stats:
Mean:           0.453279
Minimum:        0.039636
1st Quartile:   0.136423
Median:         0.464961
3rd Quartile:   0.778998
Maximum:        0.932984
Length:         20
Type:           Float64


In [14]:
# Question 10: Add a column to df2 named Cat1 to df2 consisting of randomly selecting either the strings GroupA or GroupB
df2 = hcat(df2, rand(["GroupA","GroupB"],20))
rename!(df2,Dict(:x1=>:Cat1))

Out[14]:
Var1 Var2 Var Cat1
1 0.6488819502093455 0.046288741031345504 0.20472832290217324 GroupB
2 0.010905889635595356 0.6983555060532487 0.93298350850828 GroupB
3 0.06642303695533736 0.3651093677271471 0.8272627957034728 GroupA
4 0.9567533636029237 0.3024777928234499 0.09929915955881308 GroupA
5 0.646690981531646 0.3725754415996787 0.6342997886044144 GroupA
6 0.11248587118714015 0.15050782744925795 0.1327153585755645 GroupA
7 0.2760209506672211 0.14732938279328955 0.7751941503856596 GroupB
8 0.6516642063795697 0.2834013103457036 0.8692366891234362 GroupB
9 0.05664246860321187 0.40495283364883794 0.039635617270926904 GroupB
10 0.8427136165865521 0.49953074411487797 0.7904095314876494 GroupB
11 0.9504984071553011 0.6588147837334961 0.43118828904466633 GroupA
12 0.9646697763820897 0.5156272179795256 0.1376583132625555 GroupB
13 0.9457754052519123 0.26071522632820776 0.6080803126880718 GroupA
14 0.7899036826169576 0.5955204840509289 0.2550540600167448 GroupB
15 0.8211604203482923 0.2924615242315285 0.4987340031883092 GroupA
16 0.03416010848943718 0.2885798506061561 0.09403688346569439 GroupB
17 0.09454448946400307 0.6181597973815087 0.5250899072103514 GroupB
18 0.31492622391998415 0.6642598175011505 0.2655109248498748 GroupA
19 0.12780989889368866 0.7535081177709988 0.11009621399607639 GroupA
20 0.374186714831074 0.03688418241886171 0.8343616661080064 GroupA
In [15]:
# Question 11: Create a new DataFrame named df3
df3 = DataFrame(A=1:20,B=21:40,C=41:60)

Out[15]:
A B C
1 1 21 41
2 2 22 42
3 3 23 43
4 4 24 44
5 5 25 45
6 6 26 46
7 7 27 47
8 8 28 48
9 9 29 49
10 10 30 50
11 11 31 51
12 12 32 52
13 13 33 53
14 14 34 54
15 15 35 55
16 16 36 56
17 17 37 57
18 18 38 58
19 19 39 59
20 20 40 60
In [16]:
# Question 12: Change indicated values to empty entries
#In a code cells below, change the values in df3 of the following cells to NA: row 10, column 1, row 15, column 2 and row #19, column 3
df3[10,1] = NA
df3[15,2] = NA
df3[19,3] = NA
df3

Out[16]:
A B C
1 1 21 41
2 2 22 42
3 3 23 43
4 4 24 44
5 5 25 45
6 6 26 46
7 7 27 47
8 8 28 48
9 9 29 49
10 NA 30 50
11 11 31 51
12 12 32 52
13 13 33 53
14 14 34 54
15 15 NA 55
16 16 36 56
17 17 37 57
18 18 38 58
19 19 39 NA
20 20 40 60
In [17]:
# Question 13: Create DataFrame df4 that contains no rows with NaN (NA) values
df4 = completecases!(df3)

Out[17]:
A B C
1 1 21 41
2 2 22 42
3 3 23 43
4 4 24 44
5 5 25 45
6 6 26 46
7 7 27 47
8 8 28 48
9 9 29 49
10 11 31 51
11 12 32 52
12 13 33 53
13 14 34 54
14 16 36 56
15 17 37 57
16 18 38 58
17 20 40 60

## Some Plugs-Plays with Julia Programing

Week3_PR_Template

# Title: Week 3 – Fitting a Curve¶

In [17]:
# Initilization of Plots Package
using Plots
pyplot()

Out[17]:
Plots.PyPlotBackend()

### Reading data from given Sample file¶

In [18]:
data_tofit = readdlm("Week3_PR_Data.dat", '\t', header=true)
typeof(data_tofit)

Out[18]:
Tuple{Array{Float64,2},Array{AbstractString,2}}

### Using For loop to print data in array¶

In [19]:
new_array=data_tofit[1]
for i in 1:size(new_array)[1]
println(new_array[i,:])
end

[0.501309, -0.977698]
[1.52801, 0.527711]
[1.70012, 1.71152]
[1.99249, 1.891]
[2.70608, -0.463428]
[2.99493, -0.443567]
[3.49185, -1.27518]
[3.50119, -0.6905]
[4.45992, -5.51613]
[4.93697, -6.0017]
[5.02329, -8.36417]
[5.04234, -7.92448]
[5.50739, -10.7748]
[5.56867, -10.9172]


### Scatter plot¶

In [20]:
# Create the arrays x and y, assigning x the first column of data_tofit and y the second column
x,y = new_array[:,1],new_array[:,2]
scatter(x,y)

Out[20]:

### Creating parabfit() one-liner function¶

In [21]:
# Create a function called parabfit, with x as the argument, returning a*x^2 + b*x + c
parabfit(x)=a*x^2 + b*x + c

Out[21]:
parabfit (generic function with 1 method)

### Ploting against Default values of a,b and c¶

In [22]:
a = 1
b = 1
c = 1

plot(parabfit,-2,2)

Out[22]:

### Ploting using different range for parabfit()¶

In [23]:
# Create variables a, b and c, assigning each the value 1
a = 1
b = 1
c = 1

# Plot the function parabfit, for x values between -5 and 5
plot(parabfit,-5,5)

Out[23]:
In [24]:
# More plot!() tries.
a,b,c = 1,1,1
scatter(x_axis,y_axis)
plot!(parabfit,-5,5)

UndefVarError: x_axis not defined

Stacktrace:
[1] include_string(::String, ::String) at ./loading.jl:515

Optimize parameters a, b and c such that it fits the data points more concisely.

1. Parbola should be downwards that detarmines cofficient a must be negative.
2. As from the data points value of cofficient c should be close to zero.
3. Cofficient b determines the values of y axis that must be possitive.
In [25]:
# More plot!() tries.
a,b,c = -1,2,3
scatter(x,y)
plot!(parabfit,-5,5)

Out[25]:
In [26]:
# More plot!() tries.
a,b,c = -1,0.1,2
scatter(x_axis,y_axis)
plot!(parabfit,-5,5)

UndefVarError: x_axis not defined

Stacktrace:
[1] include_string(::String, ::String) at ./loading.jl:515
In [27]:
# More plot!() tries.
a,b,c = -1,0.8,3
scatter(x,y)
plot!(parabfit,-5,5)

Out[27]:
In [28]:
# More plot!() tries.
a,b,c = -0.9,2.7,0.05
scatter(x,y)
plot!(parabfit,-5,5)

Out[28]:

### Optimising variable c¶

In [29]:
a,b = 1,1
plot(scatter(x,y,alpha=0.5))
c=0
plot!(parabfit,-5,5)
c = -1
plot!(parabfit,-5,5)
c = -2
plot!(parabfit,-5,5)
c = -3
plot!(parabfit,-5,5)
c = -4
plot!(parabfit,-5,5)
c = -5
plot!(parabfit,-5,5)
c = 2
plot!(parabfit,-5,5)

Out[29]:

### Optimising Variable a¶

In [31]:
c,b = 1,1
plot(scatter(x,y,alpha=0.5))
a=0
plot!(parabfit,0,5)
a = -1
plot!(parabfit,0,5)
a = -2
plot!(parabfit,0,5)
a = -3
plot!(parabfit,0,5)
a = -4
plot!(parabfit,0,5)
a = -5
plot!(parabfit,0,5)
a = 2
plot!(parabfit,0,5)

Out[31]:
In [37]:
#Locating final value for a
c,b = 3,1
plot(scatter(x,y,alpha=0.5))
a = -1
plot!(parabfit,0,5)

Out[37]:

### Optimising for b¶

In [53]:
c,a = 2,-1
plot(scatter(x,y,alpha=0.5))
b=0
plot!(parabfit,0,5)
b = 1
plot!(parabfit,0,5)
b = 2
plot!(parabfit,0,5)
b = 3
plot!(parabfit,0,5)
b = 4
plot!(parabfit,0,5)
b = 5
plot!(parabfit,0,5)
b = -1
plot!(parabfit,0,5)

Out[53]:
In [57]:
# plotting for b=4
c,a = 1,-1
plot(scatter(x,y,alpha=0.5))
b = 3
plot!(parabfit,0,8)

Out[57]:

### final Values of a,b and c¶

In [65]:
# plotting for b=4
c,a,b = 1,-1,3
plot(scatter(x,y,alpha=0.5))
plot!(parabfit,0,5)

Out[65]:

To optimize values of a,b,c we had to plot one variable many times to find out one variable’s occurrence at different levels
of scale. By changing the range of parabola function it was more easy to come up with more accurate values of a,b and c

In [ ]:

## OOPS and More OOPS in Python

Have to work on following these two things and have to look more into this whole stuff.

https://jeffknupp.com/blog/2014/06/18/improve-your-python-python-classes-and-object-oriented-programming/

https://rhettinger.wordpress.com/2011/05/26/super-considered-super/

http://www.pythonforbeginners.com/super/working-python-super-function

## Concurrency in Python or Natural way of life(Not yet completed POST)

There are various ways one can think about computing , Multiprocessing, Asynchronous, Multi-threading as well as “Parallel Processing” If I would talk about theoratical things I Would say we have to distribute our one particular task in various forms so multiple resources should be available for system to run things or in other way we can say multiprocessing is more of Programmer’s way of understanding the Flow of precess and sometimes rules according to theory does not assure that if one is providing multiple resources to process it will be FAST! it could be FAT! also.

Now let me start with very simple Example by taking following function as use case:

# Function that run multiple tasks def get_response(url): “””returns response for URL ””” response = requests.get((url),verify=False) return response.text

Now above function is simple enough that is getting one URL and returning response but if I have to pass multiple URLs but I want that get request to each URL should be fired at same time then That would be Asynchronous process not multiprocessing because in Multiprocessing Threads/Processes needs to communicate with each other but on the other hand in case of Asynchrounous threads don’t communicate(in Python because Python uses Process based multiprocessing not Thread Based although you can do thread-based multiprocessing in Python but then you are on your OWN 😀 😛 Hail GIL (Mogambo/Hitler)).

So above function will be like this as usual:

from multiprocessing import Pool pool = Pool(processes=20) resp_pool = pool.map(get_response,tasks) URL_list = [] resp_pool = _pool.map(get_response,tasks) pool.terminate() pool.join() 

One thing you have to understand very carefully and that is GIL does not harm for i/o bound operations but yes when it comes to non-i/o bound operations in python You have Numpy,Scipy,Pandas,Cython where one can really release GIL and take full advantage of the code.

How to release GIL using Cython: https://lbolla.info/blog/2013/12/23/python-threads-cython-gil
Although one can look for interesting features about GIL: http://www.dabeaz.com/python/NewGIL.pdf

Intel has also provided Python Distribution that is helpful get speedups in Python but that would only be helpful for Machine-learning and Data-Science work.

http://www.techenablement.com/orders-magnitude-performance-intel-distribution-python/(Seems like worth to give it a Try:::)

Now there is one important thing you must need to care about when you are releasing GIL in Python.

Although Numba is also there but make one thing for sure Use such tools only when your Operation is CPU bound not I/O bound because as I have stated that I/O bound operations don’t care about GIL.

Although you will find out that GIL is not just Python’s Problem:

https://www.jstorimer.com/blogs/workingwithcode/8085491-nobody-understands-the-gil

I/O Bound:

The I/O bound state has been identified as a problem in computing almost since its inception. The Von Neumann architecture, which is employed by many computing devices, is based on a logically separate central processor unit which requests data from main memory,[clarification needed] processes it and writes back the results. Since data must be moved between the CPU and memory along a bus which has a limited data transfer rate, there exists a condition that is known as the Von Neumann bottleneck. Put simply, this means that the data bandwidth between the CPU and memory tends to limit the overall speed of computation. In terms of the actual technology that makes up a computer, the Von Neumann Bottleneck predicts that it is easier to make the CPU perform calculations faster than it is to supply it with data at the necessary rate for this to be possible.

In simple cases CPU is Faster and Memory is Slower.
https://en.wikipedia.org/wiki/I/O_bound

Let’s make things more precise:
Sync: Blocking operations.
Async: Non blocking operations.
Concurrency: Making progress together.
Parallelism: Making progress in parallel.

Now Questions arises that do we need all those things together:
http://docs.python-guide.org/en/latest/scenarios/speed/
https://pawelmhm.github.io/asyncio/python/aiohttp/2016/04/22/asyncio-aiohttp.html

## One mintue read to one minute Manager

### Get out more results in less time.

Autocratic VS Democratic:
Autocratic are result oriented and Democratic are happiness Oriented So we
need to be one minute Managers. 🙂

1. One minute Goal Setting:

Everyone should be knowing the goals of the company.
People must know what their roles are in the company.
Goals must not be more than 250 words.

2. One minute Praising:

Give True Feedback.
Always praise immediately.
Share happiness and encourage your people.

3. One minute reprimand:

Immediately point people out for their mistakes.
Tell people how you feel about it.
Point out mistake but don’t criticize.
Be on the side of your people.

conclusion:
Look for the good things in the beginners and bad things in the experienced.
Share what you learn.
We don’t manage people, We manage behaviors.
Love your people and make sure they are also loving you back.
Define your problem grammatically. (What is happening and What you want to be happen.)

## Lessons Learned from life

Complete Basics those just went out of my mind, No idea how those gone away. 😦

Work-Life Balance.

You can’t be successful in one day.

All the time people around you tell how to do it, Either you ignore  it or take to next level.

Better late than never.

Never Leave your Day job(even if it is cutting grass ).

Don’t try to be OVER-SMART.

Learn to respect your personal space as well as others.

Learn to turn off your mind from consistence thinking of things.

Love your work—-Work is never Ending process, Don’t take so much pressure to complete it or start next one.

Have a group of friends outside work.

Nobody is slowing you down Except you.

Learn to say sorry, please, thanks, welcome.

Help others but respect your time and Energy.

Break the pattern of your life.

Be hungry, be foolish – Stop believing that.

Sikhism has different way of living life.(Either believe in that or Live with sorrows.)

If you want to earn more, Be-crazy, Get-exploited and create a big hole inside you, that is your choice as well. 🙂

## Law of Wealthy LIfe

Wealthy life does not just mean to having lots of money in the bank but it is much more like creating various things in your society or running various engines those work in the manner that you are really able to make things happen in your life instantly, One thing you must remember or know carefully and that is If you really want to do it fast,  do it well. 🙂

Speed of implementation

Respect your time(Don’t waste on social media and stuff)

Go to bed early and get up early. Although I am writing this post so Late.:P 😦 😉

## Important Julia Packages

1. Julia Ipython

Julia is able to run very well on you Ipython notebook Environment. After all, All you have to do is Data-Science and Machine-Learning. 🙂

1.1 Open Julia Prompt(At Ubuntu it works like typing ‘julia’ command in your Terminal)

1.2 run command > Pkg.add(“IJulia”) # it will do almost all the work.

2. DataFrames: Whenever you have to read lot of files in Excel-Style Julia DataFrames Package is good to go.

Pkg.add("DataFrames")

3. Arduino:

A Julia Package for interacting with Arduino.

https://github.com/rennis250/Arduino.jl

4. Neural Network Implementation of Julia

https://github.com/compressed/BackpropNeuralNet.jl

5. Visualizing and Plotting in Julia:

https://github.com/bokeh/Bokeh.jl

6. Reading and writing CSV files in Julia