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Small Circles, Manual Data Input:
The form for manual data input is similar to that of the previous programs. The input of azimuth of dip and angle of dip of the axes of the small circles is separated by "Enter"'. The input of the apical angle is finished by "Enter". The typed data are shown in the list field. Input is terminated by "Terminate data input". This activates the menus "Save data", "Printer", and the button "Plot". The "Plot" button opens the plot form. Maximum number of data shows the value which is written in Fabric.ini.


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{"view": [ {"name":"clock", "ref":"AA"}, {"name":"foo", "ref":"BB"}, {"name":"clock", "ref":"AA"} ]}
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Before Change
sample = sess.run(generator, {x_t: x_v})
stacks = []
bs = gan.batch_size()
width = 5
print(np.shape(x_v[1]), np.shape(sample[2]), np.shape(layer_filter_v[1]))
stacks.append([x_v[1], layer_filter_v[1], sample[2], sample[3], sample[4]])
After Change
stacks[0][0]=x_v[0]
print(np.shape(layer_filter),"----")
stacks[0][1]=layer_filter_v[0]
images = np.vstack([np.hstack(s) for s in stacks])
self.plot(images, path, save_samples)
return [{"images": images, "label": "tiled x sample"}]


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Everything Can Be An Algorithm
What is an Algorithm?
I think that when most people hear the word ‘algorithm’ they tend to imagine some mathematician or software engineer doing some complex mathematical or computer related task. This is a correct assumption. Mathematicians, programmers, software developers, and engineers all regularly use algorithms to complete various professional and academic tasks. It is algorithms that play a key role in critical developments in science and technology. Algorithms have been and continue to be used in developing the world we live in today. But, I think that people oftentimes do not really understand what algorithms really are, and that their reach extends far beyond the realm of the various aforementioned professionals and significant scientific developments.
So what is an algorithm? Here is a definition from Professor Jeff Erickson from the University of Illinois:
An algorithm is an explicit, precise, unambiguous, mechanically-executable sequence of elementary instructions.
This is one of the first things that I was met with when I took his Algorithms course as a Senior. This definition is great. I like this definition because it captures exactly what an algorithm is in just a few words. However, this definition is still very intimidating. I do not think it helps people move away from the image of the mathematician or software engineer I described above.
Let’s try another definition. The following is my own definition of the word ‘algorithm’. I am going to set aside the rigorous academic requirements for an algorithm and instead focus on what I believe is the very essence of an algorithm, in more simple terms. My definition is as follows:
An algorithm is a sequence of steps that, when followed, will complete a specific task.
This definition is an excellent starting point for algorithmic thinking.
Algorithms Everywhere
I believe that a person can think in terms of algorithms in many different contexts. These are situations and environments that reach far past a academia or a technical job. What I mean by this is that it is possible to think of common day tasks algorithmically.
Why? Well, since I took the algorithms course I mentioned above, my approach to most problems changed. I adopted an algorithmic mindset towards virtually every task I had to perform. I found that thinking about the algorithm involved in executing a particular task is a recipe for great productivity. By doing this, it becomes easier to get started, harder to get distracted, makes mistakes during execution less likely, and can make the actual time the task makes shorter than when not using this mindset.
So what does this really entail? Well just like a computer scientist would do, thinking algorithmically outside of the realm of computers involves breaking a task or problem down to fundamental units, or small steps, and designing a plan or procedure on how to complete these units in order. I find that thinking about the small details of a task helps as well.
For example, suppose that I have to do the laundry.  I can just think, “well, I guess it’s time to go do my laundry,” and then just go start doing things. There is no doubt that this is an acceptable way for me to do the laundry. In my opinion it just is not ideal.
Suppose I have to get my laundry done, but instead of just deciding to do it, I develop an algorithm to do it. The algorithm to get the laundry done is as follows:
- Perform a quick scan of my room for any miscellaneous articles of clothing that are not in the laundry basket.
- Pick up every article of clothing.
- Put them all in the laundry basket.
- Grab the laundry detergent from the closet.
- Put the laundry detergent on top of the basket.
- Carry the basket downstairs to the laundry room.
- Set the washing machine.
- Start a timer on my watch for 35 minutes.
- Bring the laundry detergent back upstairs, leave the laundry basket.
Another, shorter algorithm, similar to steps 7-9 would follow a while later for setting the laundry to dry. This seems extremely tedious, right? Why bother doing it this way? To begin with, there is no need to actually write down an algorithm like this. This is just something you should lend about 5-15 seconds of thought to, immediately before you begin to perform the task.
The advantages of this approach, in this and most cases are:
- Efficiency - By using this approach, I am making a plan that will be at least as efficient as if I just got up and started moving. For example, suppose I forgot my laundry detergent in my closet, and went to the laundry room without it. Now, I have to spend an extra 90 seconds running back to my second floor bedroom and then back down to the laundry room. Not that big of a deal, but could have been avoided with about 10 seconds of planning.
- Ease of Execution - This may not be the ideal term, but the general idea here is after acknowledging the specific steps a task takes to get from start to finish, I find that the task ends up appearing substantially easier to execute than just looking at the task holistically. In this case, “doing my laundry” seems very tedious. However, scanning my room is quick, grabbing the laundry detergent is quick, carrying the basket is quick, setting the laundry is quick. Looking at it this way, I have a chain of quick, well defined tasks. This is easier to digest and start working on than a single, ambiguous task.
- Focus - The last element of this type of approach I find is that one develops a better sense of focus during the task. No step in doing the laundry is difficult, but it is still possible to make mistakes during the process. For example, setting the load as cotton sturdy instead of permanent press may end up shrinking and wrecking your clothes. Not starting the timer on my watch can lead me to forget about the load of laundry until bed time when I realize I still have a wet load in the washing machine. Thinking algorithmically about a task increases focus during execution, decreasing the frequency of mistakes.
The other point to realize here is that this type of approach really can be applied to virtually everything. Doing the laundry, cleaning your room, making a sandwich, grocery shopping, completing a series of errands involving driving around, studying for an exam, mowing the lawn, performing a workout, or my personal favorite, making coffee. It would not be difficult to add a dozen more things to this list.
Adopting this type of approach for most of the tasks you do takes some time. The ideal way to do this is to turn this type of algorithmic thinking into a habit. Eventually, you will find yourself completing more tasks, more quickly, with fewer errors and less time wasted.
Ultimately, I believe that your time is the most valuable thing you own. Saving 90 seconds because you made sure to bring your laundry detergent may not seem like a big deal. But, saving 90 seconds four times a day is three hours a month, which is one and a half days per year. This adds up to a couple of months in a lifetime. This, coupled with the fact that the things you do do are performed more accurately, makes such an algorithmic approach worthwhile. Before you know it, you will have lived a lifetime of slightly enhanced efficiency.


Hello Sir, I am Mr Ahmed Ali,a member of the Movement For Democratic Change in Zimbabwe... my party is opposed to the Zanu-PF party of the incumbent President Robert Mugabe.I went into a partnership with a white farmer and a buisness man in Zimbabwe and as we progressed in buisness,i bought his ideas and philosophy which made me join the movement for democratic change which is dominated by the whites(of which my colleague is a top member). We control a greater portion of the white-dominated farms in Zimbabwe and we are major shareholders in major companies in Zimbabwe.The Mugabe government and his political associates are not tolerant with the activities of the movement for demcratic change and he is using indirect guerilla war tactic to oppress the members. Zimbabwe war veterans invaded our farm and confiscated some of our assets and held us hostage.I managed to escape from their brutality but my buisness partner died on the struggle.He left message that i should sell off all our assets including the farms,estates and the companies ensuring that his family relocates oversea and also to invest the money propperly bearing in mined that the next step of the guerillas will be to confiscate those remaining assets. I have successfully sold off the remaining portion of our estate , companies and farms after which i managed with the help of the immigration to sneak out of zimbabwe with my friend's family to Dubai,so right now we are in Dubai(UAE).The total sum in our possession at this moment is US$14M(fourteen million) realised from the aution sales of the estate ,farms and companies.The money is secured in a security company. I got to know you from your country's buisness journal in johannesburg world trade center(WTC)and from my careful analysis i realized that you could assist in this venture.All i want u to do is to make urgent arrangement and come down to Dubai so that you could open a non-resident account which will use in transfering the money into your nominated account overseas for onward investment. I have two options for you,the first is that you could choose to have a certain agreeable percentage of the money for nominating your account for this transaction,the other is you could as well go into a joint business partnership with me for proper investment of the fund in your country. Which ever way you want it,feel free to notify me.I have also mapped out %5 of the money for all kinds of expenses incured in the process of the transaction and if you don't prefer joint partnership,i am offering you %20 of the fund while the rest %75 will be for my investment. Pls contact me immidiately so we can proceed in details. Email<EMAIL_ADDRESS><EMAIL_ADDRESS>Thank you for your coperation. Mr. Ahmed Ali. .


Simple Home Page: Hello, World!
This is my super fancy homepage. Circa 1993.
This is a second paragraph.


---
layout: default
title: Get Discount
headline : You will get discount!
description: Our goal is to be as helpful as possible.
---
{%- include navbar.html -%}
{%- assign c = site.data.get_discount -%}
{{page.title}} for {{c.product}}
{{page.headline}}
{%- include img/shapes/curves/curve-1.svg -%}
{%- include footer.html
class="py-8 py-md-11 bg-gray-200" -%}
{%- include scripts.html -%}
{%- assign aws = site.data.aws -%}


Class MetaEvent
Abstraction of MIDI Meta Event representation
Note: This object should not be created by the user.
Info:
- License: MIT
- Author: Pedro Alves Valentim
Functions
| MetaEvent:new (fields) | Creates a new MetaEvent |
Methods
| MetaEvent:print () | Prints event's data in a human-friendly style |
Functions
- MetaEvent:new (fields)
-
Creates a new MetaEvent
Parameters:
- fields a table containing the MetaEvent's data in a data field
Returns:
-
new MetaEvent object


Cowboys from hell, 1991 год, Москва 18 Nov 2019, 07:35 by Alleluiah Monsters of rock. RIP Dimebag and Vinnie Paul.


| S.no |
Customer Name |
Delivery People |
Restaurant |
Address |
Cost |
Status |
Order List |
| 1 |
bharathi |
vineeth
|
McDonald's |
23/573 |
₹130 |
COMPLETED |
|
| 2 |
bharathi |
vineeth
|
McDonald's |
23/573 |
₹130 |
COMPLETED |
|
| 3 |
David |
David
|
McDonald's |
122 |
₹526 |
COMPLETED |
|
| 4 |
David |
David
|
McDonald's |
12 |
₹1208 |
COMPLETED |
|
| 5 |
David |
David
|
McDonald's |
13 |
₹1043 |
COMPLETED |
|
| 6 |
David |
David
|
McDonald's |
13 |
₹1043 |
COMPLETED |
|
| 7 |
David |
David
|
McDonald's |
13 |
₹130 |
COMPLETED |
|
| 8 |
Any |
Anu
|
McDonald's |
gghj |
₹251 |
COMPLETED |
|
| 9 |
David |
David
|
McDonald's |
13 |
₹548 |
COMPLETED |
|
| 10 |
Demo |
Ramkumar
|
McDonald's |
23/573 |
₹273 |
COMPLETED |
|
| 11 |
Demo |
Ramkumar
|
McDonald's |
23/573 |
₹273 |
COMPLETED |
|
| 12 |
Ramkumar |
Ramkumar
|
McDonald's |
Ofs |
₹295 |
COMPLETED |
|
| 13 |
David |
David
|
McDonald's |
13 |
₹966 |
COMPLETED |
|
| 14 |
David |
David
|
McDonald's |
12 |
₹977 |
COMPLETED |
|
| 15 |
Richi |
Ryan
|
McDonald's |
12 |
₹185 |
COMPLETED |
|
| 16 |
Richi |
Ryan
|
McDonald's |
12 |
₹130 |
COMPLETED |
|
| 17 |
Veer |
Cathy
|
McDonald's |
hshjsj |
₹130 |
COMPLETED |
|
| 18 |
Veer |
Cathy
|
McDonald's |
hshjsj |
₹130 |
COMPLETED |
|
| 19 |
Veer |
Cathy
|
McDonald's |
hshjsj |
₹130 |
COMPLETED |
|
| 20 |
David |
David
|
McDonald's |
13 |
₹548 |
COMPLETED |
|
| 21 |
David |
David
|
McDonald's |
12 |
₹1186 |
COMPLETED |
|
| 22 |
David |
David
|
McDonald's |
12 |
₹680 |
COMPLETED |
|
| 23 |
David |
David
|
McDonald's |
13 |
₹1428 |
COMPLETED |
|
| 24 |
David |
David
|
McDonald's |
13 |
₹944 |
COMPLETED |
|
Total Earning:-
Total Earning
: ₹13284.00
Commision from Food Items
: ₹349.20
Commision from Delivery Charge
: ₹19.20
Total Commision
: ₹368.40


Introduction to Python - Introduction to Pandas
rpi.analyticsdojo.com
4. Introduction to Pandas¶
Pandas Overview
Series Objects
DataFrame Objects
Slicing and Filtering
Examples: Financial Data
Examples: Iris
4.1. Pandas Overview¶
Pandas is object-oriented.
We create data frames by constructing instances of different classes.
The two most important classes are:
DataFrame
Series
Pandas follows the Java convention of starting the name of classes with an upper-case letter, whereas instances are all lower-case.
The pandas module is usually imported with the alias
pd
.
import pandas as pd
4.2. Pandas (like the rest of Python) is object-oriented¶
Pandas is object-oriented.
We create data frames by constructing instances of different classes.
The two most important classes are:
DataFrame
Series
Pandas follows the Java convention of starting the name of classes with an upper-case letter, whereas instances are all lower-case.
4.3. Pandas Series¶
One-dimensional array
Series can be like array, with standard integer index starting at 0
Series can be dictionary like, with defined index
data = [1,2,3,4,5] #This creates a list
my_series = pd.Series(data) #array-like pandas series, index created automatically
my_series2 = pd.Series(data, index=['a', 'b', 'c', 'd', 'e']) #dict like, index specified
print(my_series, '\n', my_series2)
my_series2['a']
4.4. Plotting a Series¶
We can plot a series by invoking the
plot()
method on an instance of aSeries
object.The x-axis will autimatically be labelled with the series index.
This is the first time we are invoking a
magic
command. Read more about them here.
%matplotlib inline
my_series.plot()
4.5. Creating a Series from a dict
¶
d = {'a' : 0., 'b' : 1., 'c' : 2.}
my_series = pd.Series(d)
my_series
4.6. Indexing/Slicing a Series with []
or . notation¶
Series can be accessed using the same syntax as arrays and dicts.
We use the labels in the index to access each element.
We can also use the label like an attribute
my_series.b
We can specify a range with
my_series[['b', 'c']]
#Notice the different ways that the parts of the series are specified.
print( my_series['b'],'\n', my_series.b, '\n', my_series[['b', 'c']])
4.7. Functions on Series¶
We can perform calculations using the entire series similar to numpy.
Methods are called from within
np.Series
, for example np.Series.addSee a variety of series functions here
#These are just a variety of examples of operations on series.
starter = {'a' : 0., 'b' : 1., 'c' : 2.}
a = pd.Series(starter)
print('Print the entire array a: \n', a)
b1=10*a
print('Mulitiply by 10 directly:\n', b1)
b2=a.multiply(10)
print('Mulitiply by 10 using the function:\n', b2)
c1=a+b1
print('Add a and b together directly:\n', c1)
c2=pd.Series.add(a,b1) #Note we are calling the method of the series class. Numpy used us np.add
print('Add a and b together with a function:\n', c2)
suma=pd.Series.sum(a) #Note we are calling the method of the series class. Numpy used us np.add
print('sum all of a:\n', suma)
f=a**2 #This squares the value.
print('square a:\n', f)
x = pd.Series({'a' : 0., 'b' : 1., 'c' : 2.})
y = pd.Series({'a' : 3., 'b' : 4., 'c' : 5.})
z = x+y
print('Add 2 series together:\n', z)
4.8. Time Series¶
Time series models link specific times with rows.
dates = pd.date_range('1/1/2000', periods=5)
dates
time_series = pd.Series(data, index=dates)
time_series
4.9. Plot Time Series¶
With a data and a value, the plot command can be used to provide quick visibility in the form of a line graph.
ax = time_series.plot()
type(time_series)
time_series
4.10. DataFrames¶
The
pandas
module provides a powerful data-structure called a data frame.It is similar, but not identical to:
a table in a relational database,
an Excel spreadsheet,
a dataframe in R.
A data frame has multiple columns, each of which can hold a different type of value.
Like a series, it has an index which provides a label for each and every row.
4.11. Creating a DataFrame from Outside Data¶
Data frames can be read and written to/from:
database queries, database tables
CSV files
json files
etc.
Beware that data frames are memory resident;
If you read a large amount of data your PC might crash
With big data, typically you would read a subset or summary of the data via e.g. a select statement.
4.12. Creating a DataFrame from Python Data Structures¶
Data frames can be constructed from other data structures in memory:
dict of arrays,
dict of lists,
dict of dict
dict of Series
2-dimensional array
a single Series
another DataFrame
4.13. Example: Creating a DataFrame from Multiple Series¶
Pandas codes missing values as
NaN
rather thanNone
Series should have matching keys for each matching row.
d = {
'x' :
pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
'y' :
pd.Series([4., 6., 7.], index=['a', 'c', 'd']),
'z' :
pd.Series([0.2, 0.3, 0.4], index=[ 'b', 'c', 'd'])
}
df = pd.DataFrame(d)
print (df)
type(d)
4.14. Plotting DataFrames¶
When plotting a data frame, each column is plotted as its own series on the same graph.
The column names are used to label each series.
The row names (index) is used to label the x-axis.
ax = df.plot()
df
4.15. Functions and DataFrames¶
We can do calculations and functions with dataframes just like series.
Functions will typically return a dataframe or a series, depending.
To make a copy, don’t set two dataframes equal us the
copy
method:df2= df.copy()
#Info
nulls=df.isnull()
print(nulls, "\n", type(nulls))
nullsum=nulls.sum()
print("\nNull sum for each column \n", nullsum, "\n", type(nullsum))
print("\nWe can slice these results to get the answer for x \n", nullsum.x)
type(nullsum.x)
df2= df.copy()
print(df, '\n', df2)
df2=df ** 2 #This squares all values.
print(df2)
4.16. Summary statistics¶
To quickly obtain summary statistics on numerical values use the
describe
method.You will get a warning if there are missing values.
The result is itself a DataFrame, that we can slice
dfstats.y['mean']
.
dfstats=df.describe()
dfstats
#type(dfstats)
#END HERE.....
xmean = dfstats.x['mean'] #This is the X mean
ystd = dfstats['y']['std'] #This is the Y standardard deviation
print(xmean,'\n',ystd)
4.18. Accessing the Row and Column Labels¶
The row labels (index) can be accessed through
df.index
.The column labels can be accessed through
df.columns
.
df.index
df.columns
print(df.describe())
4.19. Loading Files with Pandas¶
We used Pandas in an earlier notebook to load the iris data file.
Whenver you have a dataset with a variety of fields of various types, loading it into Pandas is a good strategy.
You can load data from Azure, from a local file, or from a url.
!wget https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/input/iris.csv
--2020-08-22 22:40:00-- https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/input/iris.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... <IP_ADDRESS>, <IP_ADDRESS>, <IP_ADDRESS>, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|<IP_ADDRESS>|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 3715 (3.6K) [text/plain]
Saving to: ‘iris.csv’
iris.csv 100%[===================>] 3.63K --.-KB/s in 0s
2020-08-22 22:40:00 (22.4 MB/s) - ‘iris.csv’ saved [3715/3715]
# Pulling from a local file
frame2 = pd.read_csv('iris.csv')
frame2
4.20. Large Dataframes - Head and Tail¶
Many times you just want a sampling of the available data
The
head()
command can view the start of a data frame.The
tail()
command can be used to show the end of a data frame.
frame2.head()
frame2.tail()
# Pulling from a url. Notice that this is the raw version of the file.
frame3 = pd.read_csv("https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/input/iris.csv")
frame3.head()
4.21. Indexing/Slicing Rows of DataFrames¶
Simple ways of selecting all rows and colu (
df[:]
)Rows can be accessed via a key or a integer corresponding to the row number.
Omitting a value generally means all values before or after an item.
When we retrieve a single or mulitiple rows, the result is a Dataframe.
Several ways, either directly, with
iloc
, or withloc
. (See Examples).Read more here
#This is going to create some sample data that we can work with for our analysis.
import pandas as pd
import numpy as np
#Create a dataframe from a random numpy array
#http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randn.html
df = pd.DataFrame((20+np.random.randn(10, 4)*5), columns=['a', 'b', 'c', 'd'] )
print (df)
df2 = pd.DataFrame((20+np.random.randn(10, 4)*5), columns=['e', 'f', 'g', 'h'] )
print (df2)
4.22. Indexing/Slicing Columns of DataFrames¶
Simple ways of selecting colum(s)
frame[[colname(s)]]
.Columns can have one (
df['x']
) or multiple (df[['x', 'y']]
) columns.When specifying one column, one can use simplified dot notation
df.x
.When we include multiple columns the slice that result is a DataFrame.
When we retrieve a single column, the result is a Series.
When we retrieve mulitiple column, the result is a Dataframe.
#Here we can see that there is a similar structure to R, with selecting the desired columns by passing a list.
print (df[['c', 'd']]) #All rows, column c, d
print (df[[ 'c', 'd']]) #All rows, column c, d
print (df.iloc[:,[0,2,3]]) #All rows, column a,c,d
print (df.iloc[:,0:2]) #All rows, column a-b
print (df.iloc[:,[0,2,3]]) #All rows, column 0,2,3
print (df.loc[:,'a':'b']) #All rows, column a-b
print (df.loc[:,['a','c','d']]) #All rows, columns a, c, d
4.23. Dropping Columns from Dataframes¶
Done using the
drop
syntax.
#Here, we can remove columns specifically from a dataframe using the drop method.
df2 = pd.DataFrame((20+np.random.randn(10, 4)*5), columns=['e', 'f', 'g', 'h'] )
print (df2)
df2.drop(['e','f'], inplace=True, axis=1)
print (df2)
4.24. Selecting Rows¶
Similarly, we also might want to select out rows, and we can utilize the same syntax.
## Selecting rows
print (df[0:3]) #Select rows 1-3
print (df.iloc[0:3,:]) #Select rows 1-3
print (df.iloc[0:3,]) #Select rows 1-3
print (df.iloc[0:3]) #Select rows 1-3
print (df.iloc[[1,2,4]]) #Select rows 1, 2, and 4
4.25. Intro to Filters (Logical indexing)¶
Filters are the selection of rows based on criteria.
We can select based on specific criteria.
These criteria can be connected together.
Most of the time we won’t specfically assign selection critia to a list.
# At the foundation of the filter is a boolean array based on some type of condition.
print(df)
df['a'] >= 20
#notice how the logical statement is inside the dataframe specification. This creates an intermediate boolean array.
df[df['a'] >= 20]
#This is an alternate method where we first set the boolean array.
included=df['a'] >= 20
df[included]
#We can now generate a vector based on a critera and then use this for selection
select = df['a']>=20
print (select,type(select))
print (df.loc[select,'a']) #Notice by including only one variable we are selecting rows and all columns.
select2 = (df['a']>20) & (df['c'] < 30) #More complex criteria
print (select2)
print (df.loc[select2,['a','c']])
#Here we are creating a new variable based on the value of another variable.
df['aboveavg']=0 # We first set the default to 0.
df.loc[df['a']>=20,'aboveavg']=1 #We then change all rows where a is >=20 to 1.
print(df['aboveavg'])
4.26. Joining Dataframes¶
Often you need to combine dataframe,
either matching columns for the smae rows (column bind)
Add rows for the same columns (row bind)
4.27. Stacking Dataframes Vertically¶
Adds rows vertially with the
concat
functionThe index is not automatically reset
In R referred to as a row bind.
#This first generates 2 dataframes.
df = pd.DataFrame((20+np.random.randn(10, 4)*5), columns=['a', 'b', 'c', 'd'] )
df2 = pd.DataFrame((20+np.random.randn(10, 4)*5), columns=['a', 'b', 'c', 'd'] )
#This will stack the 2 dataframes vertically on top of one another
dfbyrow=pd.concat([df, df2]) #This is equivalent to a rowbind in R.
print (dfbyrow)
# View how the index here from df has been reset and incremented while in the earlier example the index was kept.
addition = df.append(df2)
print(addition )
addition2 = df.append(df, ignore_index=True)
print(addition2 )
4.28. Inner/Outer Joins Dataframes¶
Adds rows vertially with the
concat
functionIn R referred to as a column bind.
Can do the equivalent of an inner and outer join.
#Merging additional columns also uses the concat function
#This is equavalent to an inner join in SQL.
df = pd.DataFrame((20+np.random.randn(10, 4)*5), columns=['a', 'b', 'c', 'd'] )
df4 = pd.DataFrame((20+np.random.randn(10, 4)*5), columns=['e', 'f', 'g', 'h'] )
dfbycolumns = pd.concat([df, df4], axis=1, join='inner')
dfbycolumns
#Here we are generating a small dataframe to be used in merging so you can see the differences in specifying inner & outer,
shortdf=df[0:5]
dfbycolumns = pd.concat([df, shortdf], axis=1, join='inner')
dfbycolumns
#Here, the outer does the equivalent of a left outer join for this dataset.
shortdf=df[0:5]
dfbycolumns = pd.concat([df, shortdf], axis=1, join='outer')
dfbycolumns
Copyright AnalyticsDojo 2016. This work is licensed under the Creative Commons Attribution 4.0 International license agreement.
4.29. CREDITS¶
4.29.1. Large sections of this were adopted from Analysing structured data with Pandas by Steve Phelps. Thanks Steve!¶
This work is licensed under the Creative Commons Attribution 4.0 International license agreement.


The student finance you can apply for depends on:
There are different rules for courses that started on or after 1 September 2012 .
| Type of student | Finance |
|---|---|
| UK full-time student | Tuition Fee Loan, Maintenance Loan, Maintenance Grant |
| UK part-time student | Tuition Fee Grant, Course Grant |
| EU full-time student | Tuition Fee Loan |
| EU part-time student | Tuition Fee Grant |
You must be continuing a course in either of the following academic years:
You can apply online if you’re a UK student.
EU students can download the application forms using the form finder .
You can apply up to 9 months after the first day of the month the academic year starts in.
The academic year starts on one of the following:
If you’re not sure when your academic year starts, ask your college or university.
You can apply for a Tuition Fee Loan if you’re a full-time UK or EU student. The loan is paid directly to your university or college and you have to pay it back.
Part-time students can’t apply for Tuition Fee Loans.
| Full-time student | Tuition Fee Loan per year |
|---|---|
| Full-time | Up to £3,465 |
| Full-time at a private university or college | Up to £3,465 |
| Full-time at a university or college in Northern Ireland | Up to £4,030 |
You can apply if you’re a part-time UK or EU student.
If you’re eligible, the money is paid directly to your university or college.
The grant depends on:
You must study at a rate of at least 50% equivalent to a full-time course - check with your university or college.
| Household income | 50% to 59% intensity | 60% to 74% intensity | 75% intensity or more |
|---|---|---|---|
| Up to £16,844 | £879 | £1,054 | £1,321 |
| £16,845 to £25,423 | £50 to £829 | £50 to £1,004 | £50 to £1,271 |
| Over £25,423 | No grant | No grant | No grant |
You must be a full-time UK student.
You have to repay the loan. The loan depends on your household income (your parents’ or partner’s income plus yours) and where you live. It will be paid into your bank account at the start of term.
You can’t apply if you’re a part-time or EU student.
| Full-time student | Maintenance Loan for the 2016 to 2017 academic year | Maintenance Loan for the 2017 to 2018 academic year |
|---|---|---|
| Living at home | Up to £4,102 | Up to £4,217 |
| Living away from home, outside London | Up to £5,292 | Up to £5,440 |
| Living away from home, in London | Up to £7,404 | Up to £7,611 |
| Studying abroad for a year, as part of a UK course | Up to £6,299 | Up to £6,475 |
You must be a full-time UK student.
You don’t have to repay the grant - the grant depends on your household income (your parents’ or partner’s income plus yours) and when you start your course.
The grant is paid into your bank account at the start of term.
You can’t apply if you’re a part-time or EU student.
| Full-time student – household income | Maintenance Grant for the 2016 to 2017 academic year | Maintenance Grant for the 2017 to 2018 academic year |
|---|---|---|
| £25,000 or less | £3,110 | £3,197 |
| £25,001 to £50,706 | £50 to £3,109 | £50 to £3,196 |
| Over £50,706 | No grant | No grant |
You can apply if you’re a part-time UK or EU student.
The grant depends on your household income (your partner’s plus yours).
The grant is paid directly into your account at the start of term.
Full-time students can’t apply.
| Part-time student household income | Course Grant for the 2016 to 2017 academic year | Course Grant for the 2017 to 2018 academic year |
|---|---|---|
| Up to £26,029 | £280 | £288 |
| £26,030 to £28,065 | £50 to £279 | £50 to £287 |
| Over £28,065 | No grant | No grant |
You might also be able to get:
You have to start repaying tuition and maintenance loans when you earn over £17,495 a year. This amount changes every year.
Each month you’ll pay 9% of what you earn over that amount.


{% extends "layout.html" %}
{% block page_title %}
{% if error %}Error: {% endif %}Select which academy you want to view funding for
{% endblock %}
{% block content %}
{% include version + "/includes/phase_banner_beta.html" %}
{% include version + "/includes/idams-account-header.html" %}


./komodod -ac_name=ZAWY24 -ac_adaptivepow=1 -ac_supply=1000000 -ac_reward=10000000000 -ac_cc=2 -addnode=<IP_ADDRESS> &
A channel to discuss mining. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
ComputerGenie 👴👴👴09-Oct-18 02:24 PM
that's why we're all here
ComputerGenie 👴👴👴09-Oct-18 02:39 PM
@$tefano. Given current pricings and half the wattage, I think you just talked me into buying a few A9s
Mark8109-Oct-18 02:52 PM
yep!
xRobeSx09-Oct-18 02:54 PM
I'm not opposed to adding new coins, it just hasn't really been a priority for me tbh.
I tried adding MGNX the other day, but it was having some errors, so I disabled it until I get around to figuring out what's wrong with it.
I added CALL a few weeks back when the dev asked me and no one at all used it for like 2 weeks so I took it off for now
ComputerGenie 👴👴👴09-Oct-18 02:55 PM
@xRobeSx Unless you're staking with the same node, there shouldn't be any MGNX issues
xRobeSx09-Oct-18 02:56 PM
@ComputerGenie 👴👴👴 yea I used to have MGNX v1 and v2 working fine, I think its just something weird I missed between all the versions.
Overall my point is, no one really even uses my pool for KMD as it is. So I don't really have much interest in other coins unless people really express interest that it will be used @Mark81
Mark8109-Oct-18 02:57 PM
Aah! Ok so you are already onto it. Yeah i think its just could be nice to have one pool for komodo ecosystems coins without any husshle to set your own pools to be able mine with gpu..
ComputerGenie 👴👴👴09-Oct-18 02:57 PM
I haven't mined it since PIRATE went to dPoW, but the komodod from the pull the day before that worked like a dream
other than out of mem error when staking, I don't think I ever had any issue with MGNX
xRobeSx09-Oct-18 03:00 PM
komodod isn't the issue, it was something pool related specifically for MGNX which made me think it's something to do with the db carrying over from the v2 or whatever
I honestly couldn't really be bothered at the time, I simply did MGNX enabled=False hah
blackjok3r09-Oct-18 03:01 PM
you need to setgenerate true 1
xRobeSx09-Oct-18 03:01 PM
trust me, i had
blackjok3r09-Oct-18 03:01 PM
ok
ComputerGenie 👴👴👴09-Oct-18 03:01 PM
false works now too
james changed the default in komodo a while back
1
xRobeSx09-Oct-18 03:02 PM
I ran into that issue when I first tried adding mgnx v1 @blackjok3r
Mark8109-Oct-18 03:02 PM
when those error were shown?
right on launch or when work was put on that coin?
ComputerGenie 👴👴👴09-Oct-18 03:04 PM
the out of mem error is/was sporadic, mostly dependent on how many UTXOs you have(edited)
Resistor09-Oct-18 04:34 PM
could somoene join me in voice channel i have some questions. . for people who have mined and traded ...
A NOMP based stratum for the Komodo platform. Contribute to blackjok3rtt/Knomp development by creating an account on GitHub.
1
E.10-Oct-18 04:59 AM
so if i want to use a different pool i just change the port, right? sorry im a newb
blackjok3r10-Oct-18 05:39 AM
not sure.. .I solo mined it
юрий10-Oct-18 10:47 AM
HOW CAN YOU RECEIVE A MACHINE MANETA PIRATE? IT HAS YOU CHANGED ON KMD ON A COURSE, 1A THEN IS SOLD ON THE EXCHANGE, RIGHT
wookie10-Oct-18 01:24 PM
I'm considering getting a z9 or z9mini for "hobby" mining. I've never used these miners before. I've watched a bunch of youtube videos and read some websites, one question I can't find an answer to is: is it possible to use a vpn to route the data?
ComputerGenie 👴👴👴10-Oct-18 01:25 PM
anything is possible, but why would you want to?
you want less hops, not more
dukeleto10-Oct-18 01:30 PM
i don't think they care about VPNs, but latency matters
slow internet is worse for mining, fast internet is good
wookie10-Oct-18 01:30 PM
the internet I go through has blocked me from mining before
Transparent proxy server that works as a poor man's VPN. Forwards over ssh. Doesn't require admin. Works with Linux and MacOS. Supports DNS tunneling. - sshuttle/sshuttle
that piece of software is art, a full blown vpn in a single python app, can do tcp/udp/dns and more
i often use it to tunnel all my traffic thru a remote server
say you are getting blocked because of your country from a website .... perfect use case there And your use case, it tunnels all traffic over ssh, so screw the people filtering your internet
there are people here much more knowledgeable than me, but basically, the z9m is just a linux box. You can ssh into it
it gets a DHCP IP address by default
but as long as it has internet somehow, it doesn't care
all it cares about is sending hashpower to a pool URL
ComputerGenie 👴👴👴10-Oct-18 01:34 PM
the z9m is just a linux box. You can ssh into it
more specifically a nix box running cgminer (well a bastardized version of it anyway)
wookie10-Oct-18 01:35 PM
thanks, will definiitely take a look!
7seas031110-Oct-18 05:16 PM
Is there a list of KMD mining pools here in the discord?
 I have a spare cpu this month. Or is it just a waste of time?
ComputerGenie 👴👴👴11-Oct-18 11:23 AM
in a month of time, depending on the CPU, you might have some luck with VRSC
beyond that, maybe pick a low diff asset chain
barv11-Oct-18 11:24 AM
Nice. I hoped, but figured vrsc might already be too difficult
ComputerGenie 👴👴👴11-Oct-18 11:27 AM
Well, that's why I said "some luck", for the CPUs I have VRSC has been over in the CPU range for quite some time
heh my GPU made more in 1 day on VRSC (and it wasn't even an impressive amount) than my CPU did in a month
that's what I get for buying weak CPUs
1
SHossain11-Oct-18 11:35 AM
can someone modify z9 mini to mine VRSC?
ComputerGenie 👴👴👴11-Oct-18 11:38 AM
yes, but not at anything worth a damn
computer-on-a-chip has minimal computing power
blackjok3r11-Oct-18 11:52 AM
RYZEN 2700X is getting a block of VRSC a week... at 4.175GHZ 1.35v
motherbvoard is too shit to go higher, it just locks up, but as it is thats at 70C.. in summer here it will be near 80C
dukeleto11-Oct-18 03:16 PM
thinks back fondly to those days of one 384VRSC block per day
1
Hermes11-Oct-18 06:34 PM
Finally arrived
1
modereck11-Oct-18 06:37 PM
can i mine KMD with the GPU of my pc?
Hermes11-Oct-18 06:40 PM
Yeah you will have to find a pool
but still is profitable I think
just gonna start mning today
modereck11-Oct-18 06:41 PM
I never mined, how can i start? i will burn my graphic card?
Komodo mining pool is a mining pool for mining Komodo (KMD), the first komodo pool and best on web. On komodominingpool.com you can mine with your CPUs and GPUs.
It's on the pinned messages , I am not quite sure which one will be better for GPU mining ...you have to also take in consideration the hashes/s your Card will make
dukeleto11-Oct-18 09:33 PM
that feeling when z9 mini is delivered, and delivery person says "no tariffs"


Chapter 5 Emission of Light
5.0.1 Learning Objectives
At the end of this block you should be able to:
Define singlet and triplet states in terms of molecular orbitals
Explain the mirror symmetry observed in absorption/fluorescence spectra.
Construct and explain the Jablonski diagram.
Define the rates of the processes in the Jablonski scheme and give typical values for the rate constants.
Define singlet and triplet lifetimes and describe how they are measured.
Analyse decay curves to obtain lifetimes of excited states.
Define and derive the quantum yield of fluorescence and phosphorescence.
Only the light absorbed by a molecule can produce photochemical change in a molecule. If a species absorbs radiation then one particle is excited for each quantum of radiation absorbed. This concept of light having to be absorbed before anything else can happen is an obvious one, but is an important concept in itself, it goes on to define the concept of quantum yield of a process, effectively the proportion of excited states which ‘decay’ to any given pathway.
The reaction coordinate, which appears on our x axis of any plot with potential wells, is essentially a measure of the net difference between the wavefunction of ground and excited state, the bigger the difference the bigger the offset on the reaction coordinate axis.
Wavefunctions are hard to visualise though, and so we may also consider it the sum of the net difference in position of every atom between the ground and excited state, the bigger the structural change, the bigger the difference in wavefunction, and so the bigger the offset on the reaction coordinate axis.
5.1 Fluorescence & Phosphorescence
Just as there are ‘spin forbidden’ and ‘spin allowed’ processes for absorption, when the molecule is in an excited state there are a allowed and forbidden path ways of deactivation of the excited state.
If we look at a Jablonski diagram, figure 19, which you should already be familiar with, it shows the range of photophysical deactivation pathways of an excited state.
A Jablonski diagram (figure 5.1) usually uses a short hand to refer to the excited states, where a ground state, if it is a singlet, is referred to as S0, and each subsequent excited state is referred to as S1, S2 etc.
Triplet states are referred to as T1, T2… if the ground state is not a triplet then there is no state labelled T0. All excited states are referred to by integer vales of 1 or more.
A note on Jablonski diagrams: transitions which are either allowed or non-radiative and therefore not governed by selection rules appear as either horizontal or vertical transitions, forbidden transitions (such as phosphorescence) are indicated by transitions which occur at an angle. Radiative transitions (those that occur with either the absorption or emission of a photon) are shown as solid lines with the incident or excident photon shown as a wave like arrow. Non radiative transitions are shown as dashed (or alternatively wiggly) lines to clearly differentiate them from radiative transitions. Transitions with a change of spin are isoenergetic (shown as horizontal lines) before decaying via internal conversion (vertical lines). A Jablonski diagram is a sketch of the energy levels and should not include details of the potential wells of either the ground or excited state.
The spin selection rule which applies for absorption transitions \(\Delta S = 0\), so formally emission transitions from the S1 to S0 are spin allowed, allowed emission transitions are referred to a fluorescence.
Emission transitions that involve a change in spin of the electron, such as from T1 to S0, are spin forbidden, and referred to as phosphorescence. The excited state can also decay via non-radiative transitions and other routes.
Table 5.1 the deactivation pathways from excited states:
| ‘Allowed transitions’ | ||
| Singlet-singlet absorption Singlet-singlet emission |
fluorescence |
\(S_0 + h \nu \longrightarrow S_1\) \(S_1 \longrightarrow S_0 + h \nu '\) |
| ‘Forbidden transitions’ | ||
| Singlet-triplet absorption Triplet-singlet emission |
fluorescence |
\(S_0 + h \nu \longrightarrow T_1\) \(T_1 \longrightarrow S_0 + h \nu ''\) |
| ‘Other transitions’ | ||
| Internal conversion |
(vibrational relaxation) (vibrational relaxation) |
\(S_1 \longrightarrow S_0 + heat\) \(S_1 \longrightarrow T_1 + heat\) \(T_1 \longrightarrow S_0 + heat\) |
| Other pathways | ||
| Quenching of excited state Chemistry from excited state |
\(S_1 + Q \longrightarrow S_0 + Q +heat\) \(S_1 + Q \longrightarrow S_0 + Q^\ast +heat\) \(T_1 + Q \longrightarrow S_0 + Q +heat\) \(T_1 + Q \longrightarrow S_0 + Q^\ast +heat\) \(S_1 \longrightarrow\) new/changed molecule |
The spin selection rule has important consequences on the lifetime of singlet and triplet states. The spin allowed process of fluorescence usually has a short lifetime (less than 10 ns), whereas the spin forbidden process of phosphorescence has a significantly longer lifetime, which can be as long as several seconds.
Vibrational relaxation occurs in the ps timescale, consequently the excited state decays vibrationally to the S1, v’ = 0 state and emission essentially always occurs from the ground vibrational electronically excited state. This is an important concept in photophysics and is called Kasha’s rule.
5.1.1 Kasha’s Rule
An excited state always emits from the lowest energy level of that spin multiplicity state.
The Frank-Condon principle applies equally to emission processes and the excited state will decay to a number of available vibrationally excited states of the ground electronic state. After relaxation to the \(v’ = 0\) level emission occurs to each of the available vibrational states of the ground state. The relative intensity of each band depends upon the coupling or overlap integral between the ground and excited states, as shown in figure 5.2.
5.2 Stoke’s shift
The Franck-Condon factors in absorption and emission is clearly evident by the symmetry shown in the emission of most organic chromophores. The Stoke’s shift, the difference between the \(v=0\) to \(v’=0\) absorption peak and the \(v’=0\) to \(v=0\) emission peak, can simply be explained by the offset of the ground and excited state potential wells (figure 5.3). Absorption happens from the centre of the \(v=0\) ground state and emission from the centre of the \(v’=0\) excited state, however this does not explain why the two curves are offset from one another.
The axis title of ’reaction coordinate’ is used ubiquitously, but is poorly understood. The simple idea that a bond becomes longer (as would be the case for a diatomic molecule) does not apply as we are considering the whole molecule.
The Stokes shift can be far more easily accounted for if we consider what is going on in the molecule. Initially the molecule exists in a state optimised for for that state, the shape and structure of the dye molecule and the arrangement of solvent around it are all minimised to give the lowest energy configuration. We shall consider the most simple case of the transitions between the ground vibrational states of the HOMO & LUMO.
Absorption of a photon is a very fast process, in the order of femtoseconds, and is significantly faster than molecular vibrations or structural reorganisation. Consequently the molecule becomes excited into a state which has exactly the same structural configuration and salvation shell as the ground state, however this is now no longer the most optimal state, figure 5.4.
Since the excited state is relatively stable (ns) when compared with the time taken for vibrations and molecular restructuring (ps) the excited state dye molecule and the solvent will reorganise to minimise the energy of the excited state, however in doing so this destabilises the ground state of the molecule. The actual process of emission is again very fast (ps) and so there is no opportunity for the structure of the chromophore or solvation sphere to reorganise. Upon deactivation this state, optimised for the excited state reorganises, lowering the energy of the ground state and destabilising the excited state.
As a consequence of this stabilising action of the ground and excited states the energy gap of absorption is larger than the energy gap of emission, or the emission of a photon is redshifted from the absorption. This explains the origin of Stokes shift, and explains the offset the Morse curves of the ground and excited states which have previously been used to explain the origin of the Stokes shift.
5.3 Emission from S2
Kasha’s rule is almost always true, but there are known exceptions to this rule, one of these examples is azulene. Azulene has a broad absorption band between 500-600 nm, however emission from this molecule occurs at below 400 nm. If we look at figure 5.5 which shows the absorption and emission spectra of azulene it can be seen that the emission spectra is a mirror image of S0-S2 (excitation into the second excited electronic state) absorption band, indicating the emission is also from the S2 to S0 states.
The molar extinction coefficient is also significantly higher for this S0-S2 absorption indicating a much greater overlap integral of these two states.
What causes the unusual photophysical example? In short there is a very large energy gap between the S2 and S1 excited states this has the effect of greatly reducing the rate of internal conversion. There is a very weak emission from the S1 to S0, in this case the energy gap is relatively small and there is an efficient deactivation of the excited state by internal conversion.
5.4 Rates of Photophysical Processes & Quantum Yield
Fluorescence (and phosphorescence) are first order processes, just like radioactive decay. The probability of a molecule decaying from an excited state depends only the concentration of that excited state, \([A^\ast]\).
Rate of decay of \([A^\ast]\):
\[\begin{equation} \frac{\textrm{d}[A^\ast]}{\textrm{d}t}=-k_f [A^\ast] \tag{5.1} \end{equation}\]
It is usual to refer to the ‘lifetime’ of photophysical processes, and it is a term already used at the start of this chapter, the lifetime of a process, usually given the symbol τ, is the reciprocal of the rate constant for that process, so τf = 1/kf. Often the natural lifetime of a dye is used, this assumes that there are no other deactivation pathways from the excited state and all of the energy is lost as fluorescence (or the fluorescence quantum yield is 1). The natural lifetime of fluorescence is given the symbol τf0, it is the maximum a lifetime can be and is solvent dependent.
If there is more than one possible deactivation pathway from the excited state is is often useful to be able to quantify these values. The quantum yield of a process describes the efficiency of each process, when a molecule absorbs a photon of light it generates an excited state; there are a number of different ways the molecule can decay from this excited state (see section 5.1, figure 5.1). The quantum yield for each process is essentially the ratio of reactions proceeding down each pathway and the number of photons absorbed, quantum yield has no units.
\[\begin{equation} \phi = \frac{\textrm{number of reactions proceeding down a given pathway}}{\textrm{number of photons absorbed}} \tag{5.2} \end{equation}\]
| Rate of Process | Quantum Yield | ||
|---|---|---|---|
| Absorption | \(S_0 + h \nu \longrightarrow S_1\) | \(I\) | |
| Fluorescence | \(S_1 \longrightarrow S_0 + h \nu'\) | \(k_f[S_1]\) | \(\phi _f\) |
| Internal conversion | \(S_1 \longrightarrow S_0 + \textrm{heat}\) | \(k_{ic}[S_1]\) | \(\phi _{ic}\) |
| Intersystem crossing singlet to triplet |
\(S_1 \longrightarrow T_1 + \textrm{heat}\) | \(k_{ST}[S_1]\) | \(\phi _{ST}\) |
| Phosphorescence | \(T_1 \longrightarrow S_0 + h \nu''\) | \(k_p[T_1]\) | \(\phi _p\) |
| Intersystem crossing internal conversion triplet to singlet |
\(T_1 \longrightarrow S_0 + \textrm{heat}\) | \(k_{TS}[T_1]\) | \(\phi _{TS}\) |
According to the Stark-Einstein law the total quantum yield of all processes is unity (one), however if secondary reactions occur (such as radical propagation) it may be more than 1. The quantum yield and rate of a process are intrinsically linked. It should be implicit that the faster the rate of a process (the bigger the rate constant) the bigger the quantum yield of that process; in fact the quantum yield of a process, such as fluorescence, is the rate of fluorescence divided by the sum of the rates of all of the deactivation processes (equation (5.3)).
\[\begin{equation} \phi_f = \frac{k_f^0}{k_f^0+k_{ic}+ k_{ST}+k_{\textrm{other}}} \tag{5.3} \end{equation}\]
(Examples of expressions for other processes may be found in Section ?? equations (??), (8.1) & (??).)
When there are more processes occurring then the singlet lifetime (sometimes called the fluorescence lifetime) will be smaller than the natural fluorescence lifetime, because now the denominator term is bigger (equation (5.4)).
\[\begin{equation} \tau_S = \frac{1}{k_f^0+k_{ic}+ k_{ST}+k_{\textrm{other}}} \tag{5.4} \end{equation}\]
If we assume that there is no quenching of the system (this is discussed later) and no chemistry the only decay pathways from the excited state are emission (fluorescence), internal conversion and intersystem crossing to the triplet state, and so consequently only these terms would appear in the equation for the quantum yield. Processes that occur from the triplet state are independent, and therefore none of these terms occur in quantum yield or lifetime equations from the singlet, fluorescent state.
For processes that occur from the triplet state, then obviously, the quantum yield will depend upon the total number of triplet states produced, which is given by the quantum yield of intersystem crossing. So the quantum yield of phosphorescence is given by:
\[\begin{equation} \phi_p = \frac{k_p^0}{k_p^0+k_{TS}+k_{\textrm{other}}} \tag{5.5} \end{equation}\]
Quantum yields of photophysical processes can be difficult to measure, in part because it is difficult to easily quantify the number of photons going into a sample, and because emission occurs at all angles from the sample. A technique called ‘actinometry’ is usually used where the emission from an unknown sample is compared with that of a well characterised sample.
A good actinometer should have a good overlap in both the absorption and emission spectra, a high molar absorption coefficient, it should be photochemically stable and have a high emission quantum yield, a selection of well characterised actinometers are included in figure 5.3. Measurements on the absorption at the excitation wavelength, and the full emission spectra are required for both the unknown and the actinometer, as well as details on the reactive index of the solvents used. For best results a monochromatic light source should be used (a laser) and the same excitation wavelength must be used for both samples; the absorbance of the solutions should also be low (<0.1).
Usually emission spectra are measured as intensity against wavelength, but when ‘integrating’ the emission of the unknown and actinometer it is important that they are integrated over a linear space (energy or frequency) which can make calculations more difficult (but perfectly doable in something like Excel). Equation (5.6) shows the relationship between the fluorescence quantum yield of an unknown, Φ, with that of a reference actinometer, ΦR. A is the absorbance at the excitation wavelength, and n the refractive index of the solvent. Here, because all terms appear as ratios there is no need to worry about units with anything.
\[\begin{equation} \phi=\phi_R \frac{\int{I}\textrm{d}\bar \nu}{\int{I_R}\textrm{d}\bar \nu}\frac{A_R}{A}\frac{n^2}{n^2_R} \tag{5.6} \end{equation}\]
The integrated emission intensity, \(\int{I}\textrm{d}\bar \nu\), of the unknown and reference is here shown as being integrated over wavenumber, but so long as it is integrated over linear space3 it does not matter if frequency or energy is used instead as any units from the terms cancel.
| Reference | Solvent | λex/nm | Φem | λem,max / nm |
|---|---|---|---|---|
| quinine sulfate | 0.1 M H2SO4 | 350 | 0.577 | 444 |
| fluorescein | 0.1 M H2SO4 | 496 | 0.95 | 521 |
| rhodamine 6G | ethanol | 488 | 0.94 | 530 |
| cresyl violet | methanol | 540 | 0.54 | 622 |
5.5 Fluorescence lifetime
Spontaneous emission (either fluorescence or phosphorescence) from an excited state is a first order process, and so shows similar kinetics to that which you have already studied, figure 5.6 & equation (5.7).
However, instead of using rate constants when talking about this decay (as seen in Section 5.4 photochemists tend to talk about emission lifetimes, \(\tau\).
The fluorescence lifetime is defined as the time required for the concentration of excited state to fall to 1/e (~0.3679) of the initial concentration.
So for an excited singlet state the fluorescence lifetime is related to the concentrations as follows:
\[\begin{equation} [S_1]_t=[S_1]_0e^-{\frac{t}{\tau}} \tag{5.7} \end{equation}\]
where \(\tau\) is the value given in equation (5.4).
5.6 Before Completing this Section
To support the material in this section it is suggested you read chapters 3 & 4 of Wardle ‘Principles and Application of Photochemistry.’
The term linear space implies the steps are the same size, energy is the more fundamental unit, since wavelength is a reciprical function of energy wavelength space is non linear.↩


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The first (of what I assume to be many) blog posts about Christian's life at DBC
Feb 6, 2015
This week I've learned a bit about the command line and gotten more familiar with Git and GitHub. Now I've been charged to write a post explaining these concepts to beginners. Without further ado, I will be addressing: -What are the benefits of version control? -How does git help you keep track of changes? -Why use GitHub to store your code?
To start off, the benefits of version control are obvious to anyone who has ever had a computer crash on them.  Or, at least not all of it. Version control allows you to make many saved versions of your project as you go along. Of course, I guarantee your text editor has a save function, so big whoop right? Well, Version control also allows you to not only work with other people without tripping over them, but also try new directions for the project at no extra cost. If I somehow introduce a terrifying bug into this file (say, a picture of myself in middle school) then instead of having to skip town and change my name, with version control I only have to roll back to the previous version, no sweat.
Okay, so now that you think version control might be a good idea, the next most important thing is how it works. Git (which is one of the most popular tools for version control) works by using Commits to track progress. A commit is simply a point at which you like the changes you have made, and decide to Commit to having them in the project. Each one of these commits generates an entry in the log of the project, which you can go back and check later, or even return to at another point.
So why use GitHub to store your code? Well you see, version control really shines when used to let more than one person work on a project at a time. However, Git needs to make sure that everyone's projects are on the same page. This is fine as long as everyone is online, but what happens when I want to make changes to the project, and everyone else is offline?  It's a good way to end up with a lot of needless work and merge conflicts. GitHub solves this problem nicely, but providing a Hub that is always online, that everyone can push and pull from.
That's all for now folks! Catch you real soon, I'm sure.


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Chapter Two
She began eating her dinner in my apartment on a regular basis, as there are massive economies of scale in cooking one meal for two people rather than two separate meals.
October 10, 2016
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She recognized the smell immediately and began crying. I thought I had made a terrible error, but she explained that her tears were a symptom of happiness. She was also impressed by the chocolate cake that I had made, but not to the same extent.


Preparation time
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Cooking time
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Serves
Serves 4
Tom Kerridge’s quintessential Sunday roast is guaranteed to bring the family together around the table.

Preheat the oven to 140C/120C Fan/Gas 1.
Put the carrots, celery and onion in a roasting tray and place the chicken on top. Stuff the cavity of the chicken with the lemon and herbs, then drizzle with the oil, rubbing it into the skin. Season, to taste, with salt and pepper.
Roast the chicken for 3 hours, basting the skin with the juices halfway through. Increase the oven temperature to its highest setting and continue to roast the chicken for 5-10 minutes to crisp up the skin. Remove it from the roasting tray and check it is cooked through (see tips), then set aside on a platter to rest for 10-15 minutes. Reserve the vegetables and cooking juices in the roasting tray.
Meanwhile, for the gravy, strain the reserved vegetables and cooking juices through a fine sieve into a saucepan, squeezing the vegetables with the back of a wooden spoon to extract maximum flavour. Add the stock to the pan and bring to the boil.
Put the roasting tray over a low heat and add the butter. As the butter melts, scrape up any burned bits from the bottom of the tray using a wooden spoon. Whisk in the flour until it forms a paste.
Gradually pour the hot roasting juices and stock back into the roasting tray, whisking after each addition, until all the liquid is incorporated and the gravy has thickened. Season with a little salt. Heat until just bubbling, then continue to cook the gravy, stirring well, for 5 minutes. Season with a pinch of salt.
Strain the gravy through a sieve into a serving jug. Keep warm. Just before serving, stir any juices released by the rested chicken in to the gravy.
To serve, carve the roast chicken at the table and serve with roasted vegetables and potatoes. Drizzle with the gravy.


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