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Footsteps envisions a world in which all who choose to leave insular religious communities are able to live self-determined lives, have the resources to flourish.
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A foot more light, a step more true, Ne'er from the heath-flower dashed the dew. The grass stoops not, she treads on it so light. William Shakespeare , Venus and Adonis , line 1, Steps with a tender foot, light as on air, The lovely, lordly creature floated on. Sed summa sequar fastigia rerum.

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But I will trace the footsteps of the chief events. Methought I saw the footsteps of a throne. External links [ edit ]. Look up footsteps in Wiktionary , the free dictionary. Category : Themes. Namespaces Page Discussion.

Skygofree: Following in the footsteps of HackingTeam

Views Read Edit View history. In other languages Add links. Using Python, I gathered the data using python-fitbit , a Fitbit API Python implementation and also conducted the time series analysis using the package Prophet. On the other hand, with R, I performed the descriptive analysis and exploratory analysis of the data. Said dataset consists of all the steps registered by my Fitbit from July 9, , to August 1 , grouped in 15 minutes intervals. During my time in Malaysia, I walked a total of steps across 24 days, an average of In this first plot, we are looking at the number of steps per day.

The initial impression I got from this image is its zigzag pattern. This shape indicates that I barely had two highly active days in a row. Instead, it seems that after a long day, I usually take the next one easier.


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The lazy one is from those days spent in bed, writing stories such as this one. The following one represents a typical, city-walking day, while the last category is all about the hardcore, and tough days in which I probably went hiking, took a tour or went somewhere exotic and breathtaking; these days constitute the outliers.

To complement this histogram, I want to draw a boxplot to corroborate if any of these points is an actual outlier. Just as I thought. For those of you who are curious, the exact number is On this day, I was hiking through the tropical, lush, and rainy jungle of the Penang National Park.

follow in someone’s footsteps synonyms with definition | Macmillan Thesaurus

As expected, the activities one does in each of these places vary significantly from others, e. Likewise, the methods of transportation are also distinct. For example, KL has a convenient metro, while moving around in Langwaki requires a bike or motorcycle. To corroborate this information, I grouped my dataset by location and plotted them. The first region the plots shows, KL in red , shows two days with over steps, and another two in which I barely got out of bed.

But how much did I exactly walked? So, we need the mean to have a better understanding of where, on average, I walked the most. But beware! Keep in mind the outlier, and the effects it has on the distribution :. Back when I showed the first graph, I pointed out that it has a zigzag pattern, indicating that there are no two consecutive days with a similar number of steps. Honestly, I do believe this is the most exciting part of the dataset.

For starters, it says something about me, and regarding how I led my days; have a nice walk today, take it easy tomorrow. However, the coolest part about it is that it gives me a legit reason to apply time series analysis to the dataset. In layman terms, the idea behind a time series analysis is to learn the patterns and trends from a sequence of events ordered by time to ultimately forecast future outcomes. In this analysis, there are three things I want to focus on: the general trend , weekly and hourly seasonality. The general trend component describes the overall evolution of the series.

To fit our model in Prophet, first, we have to call the prophet function using as a parameter the desired dataset. This input has to be a data frame with two columns: ds and y.

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In this case, our columns are all the 15 minutes intervals elapsed from July 9 , until August 2 e. The following code snippet shows how you can do it in Python. Let me explain this a little bit. Lastly, we need plt.

FOOTSTEPS Emerald Castle ENGSUB + Romanization + Hangul

To simplify these three graphs, and for better visibility purposes, I post-processed the returned image to separate each figure and to title them. Instead, we can interpret them as the incremental effect on y of that seasonal component as stated here. The first element I want to illustrate is the overall trend of the data. On the initial days, I was all hyped up and hungry for adventures in busy Kuala Lumpur. Then, after these hectic and rush days, I moved to the Cameron Highlands. There, high in the mountains, I slowed down a bit Stranger Things season 3 is the culprit.


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    • Lastly, at Langwaki, once again, I decided to slow down and enjoy the ocean breeze. This line summarizes the cyclic pattern I showed during my weeks in Malaysia. To start with, Wednesdays seems to be my least-active day. And no, this is not on purpose. Could it be that most of my Netflix days were on this particular day?

    But what surprises me is that Friday, the king of days, is the highest one. Finally, to end this article, I want to show what, in my opinion, is the best part of the whole analysis: the daily component. Like the previous one, this graph also presents a cyclic pattern.