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data-analysis project - 1 - bird_migration_cartopy/Dependencies.Cartopy/Cartopy-0.14.0-cp35-cp35m-win_amd64.whl

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+Track the movement of three gulls(birds) namely - Eric, Nico & Sanne : dataset : https://inbo.carto.com/u/lifewatch/datasets
+ 
+One fascinating area of research uses GPS to track movements of animals.
+It is now possible to manufacturer a small GPS device that is solar charged,
+so you don't need to change batteries, and use
+ it to track flight patterns of birds.
+The data for this case study comes from the LifeWatch INBO project.
+Several data sets have been released as part of this project.
+We will use a small data set that consists of migration data for three
+gulls named Eric, Nico, and Sanne.
+The csv file contains eight columns, and includes variables
+like latitude, longitude, altitude, and time stamps.
+In this case study, we will first load the data,
+visualize some simple flight trajectories,
+track flight speed, learn about  daytime and much, much more.
+
+
+
+Order of Project subparts :
+1.bird_migration_trajectories_lat.long.py
+2.bird_migration_speed.py
+3.bird_migration_date.time.py
+4.bird_migration_daily_mean_speed.py
+5.bird_migration_cartographic.py
+
+
+I did this project to accompany my learnings from HarvardX course - PH526x Using Python for Research.
+
+- Amartya Ranjan Saikia ([email protected]/[email protected])

data-analysis project - 1 - bird_migration_cartopy/Dependencies.Cartopy/Shapely-1.5.17-cp35-cp35m-win_amd64.whl

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+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import matplotlib.pyplot as plt
+
+proj = ccrs.Mercator() #To move forward, we need to specify a specific projection that we're interested in using.
+
+plt.figure(figsize=(10,10))
+ax = plt.axes(projections=proj)
+ax.set_extent((-25.0, 20.0, 52.0, 10.0))
+ax.add_feature(cfeature.LAND)
+ax.add_feature(cfeature.OCEAN)
+ax.add_feature(cfeature.COASTLINE)
+ax.add_feature(cfeature.BORDERS, linestyle=':')
+for name in bird_names:
+    ix = birddata['bird_name'] == name
+    x,y = birddata.longitude[ix], birddata.latitude[ix]
+    ax.plot(x,y,'.', transform=ccrs.Geodetic(), label=name)
+plt.legend(loc="upper left")
+plt.show()

data-analysis project - 1 - bird_migration_cartopy/READ_ME.txt

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+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime
+import numpy as np
+
+#code from bird_migration_date.time *************************************
+timestamps = []
+for k in range(len(birddata)):
+    timestamps.append(datetime.datetime.strptime(birddata.date_time.iloc[k][:-3], "%Y-%m-%d %H:%M:%S"))
+birddata["timestamp"] = pd.Series(timestamps, index = birddata.index)
+
+#code from bird_migration_date.time *************************************
+data =  birddata[birddata.bird_name == "Eric"]
+times = data.timestamp
+elapsed_time = [time-times[0] for time in times]
+elapsed_days = np.array(elapsed_time)/datetime.timedelta(days=1)
+
+next_day = 1
+inds = []
+daily_mean_speed = []
+for (i,t) in enumerate(elapsed_days):
+    if t < next_day:
+        inds.append(i)
+    else:
+        daily_mean_speed.append(np.mean(data.speed_2d[inds]))
+        next_day += 1
+        inds = []
+
+plt.figure(figsize = (8,6))
+plt.plot(daily_mean_speed, "rs-")
+plt.xlabel(" Day ")
+plt.ylabel(" Mean Speed (m/s) ");
+plt.show()

data-analysis project - 1 - bird_migration_cartopy/bird_migration_cartographic.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime
+
+# >>>birddata.column  #check the columns of dataset
+# >>>birddata.date_time[0:3] #check first few entries of date_time
+# >>>datetime.datetime.today() #returns the current Date (yy-mm-dd) & time (h:m:s)
+
+# >>>time_1 = datetime.datetime.today()
+# >>>time_2 = datetime.datetime.today() #enter this code waiting few seconds
+# >>>time_2-time_1 #you will get the measure of elapsed seconds as output,the resulting object is called date time time delta object
+
+# >>>date_str = birddata.date_time[0]
+# >>>date_str
+# >>>date_str[:-3] # slices/removes the UTC +00 coordinated time stamps
+# >>>datetime.datetime.strptime(date_str[:-3], "%Y-%m-%d %H:%M:%S") #the time stamp strings from date_str are converted to datetime object to be worked upon.
+
+timestamps = []
+for k in range(len(birddata)):
+    timestamps.append(datetime.datetime.strptime(birddata.date_time.iloc[k][:-3], "%Y-%m-%d %H:%M:%S"))
+'''
+    The next step for me is to construct a panda series object
+    and insert the timestamp from my Python list into that object.
+    I can then append the panda series as a new column in my bird data data frame.
+'''
+birddata["timestamp"] = pd.Series(timestamps, index = birddata.index)
+# >>>birddata.timestamp[4] - birddata.timestamp[3]         #measure time difference between row 4 & 3
+
+'''
+    What I'd like to do next is to create a list that captures the amount of time
+    that has elapsed since the beginning of data collection.
+'''
+times = birddata.timestamp[birddata.bird_name == "Eric"]
+elapsed_time = [time-times[0] for time in times]
+
+#But how can we measure time in certain units, like hours or days?
+# >>>elapsed_time[1000]/datetime.timedelta(days=1)  #output is the no of days have passed between these two points
+# >>>len(elapsed_time) # check the length of entries
+# >>>elapsed_time[19000]/datetime.timedelta(hours=1)  #output is the no of hours have passed between these two points
+
+plt.plot(np.array(elapsed_time)/datetime.timedelta(days=1))
+plt.xlabel(" Observation ")
+plt.ylabel(" Elapsed time (days) ")
+plt.show()

data-analysis project - 1 - bird_migration_cartopy/bird_migration_daily_mean_speed.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+ix = birddata.bird_name == "Eric" #storing the indices of the bird Eric
+speed = birddata.speed_2d[ix]
+# >>>plt.hist(speed) #[F.A.I.L.S] ,since we have non numeric numbers in the speed array
+# >>>plt.hist(speed[:10])  #plot a histogram using the first 10 observations of speed [works]
+# >>>np.isnan(speed) #>>>np.isnan(speed).any()  #I am looking for any non number objects in the speed column using numpy's isnan function.
+# >>>np.sum(np.isnan(speed)) # I find out the count of non numeric entries, False=0 & True =1 from isnan()
+
+# >>>ind = np.isnan(speed)
+# >>>plt.hist(speed[~ind]) #we will include only those entries for which ind != True
+
+plt.figure(figsize = (8,4))
+# >>>speed = birddata.speed_2d[birddata.bird_name == "Eric"] #step 5 & 6 combined
+ind = np.isnan(speed)
+plt.hist(speed[~ind], bins=np.linspace(0,30,20), normed=True)
+plt.xlabel(" 2D speed (m/s) ")
+plt.ylabel(" Frequency ")
+plt.show()
+
+
+'''
+    We can also plot a similar histogram using the pandas module instead of pyplot.
+    The benefit of using pandas is that we do not have to deal with NaNs explicitly.
+    Instead, all of that happens under the hood.
+
+    NaNs - Not-a-Number
+
+    >>>birddata.speed_2d.plot(kind='hist', range=[0,30])
+    >>>plt.xlabel("2D speed")
+    >>>plt.savefig("hist_birdmig_speed.pdf")
+'''

data-analysis project - 1 - bird_migration_cartopy/bird_migration_date.time.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+birddata = pd.read_csv("bird_tracking.csv") #make sure,you are in the right directory , check (>>>pwd)
+
+# >>>birddata.info() #look at basic info abot the data frame
+# >>>birddata.head() #look for first 5 rows in the data set
+# >>>birddata.tail() #look for last 5 rows in the data set
+
+bird_names = pd.unique(birddata.bird_name) #look at the unique names of the birds in the csv_file
+# >>>print(bird_name)
+
+ix = birddata.bird_name == "Eric" #storing the indices of the bird Eric
+x,y = birddata.longitude[ix], birddata.latitude[ix]
+plt.figure(figsize = (7,7))
+plt.plot(x,y,"b.")
+# >>>plt.show() #if you want to check trajectory of "Eric" only
+
+''' To look at all the birds trajectories, we plot each bird in the same plot '''
+plt.figure(figsize = (7,7))
+for bird_name in bird_names:
+    ix = birddata.bird_name == bird_name  #storing the indices of the bird Eric
+    x,y = birddata.longitude[ix], birddata.latitude[ix]
+    plt.plot(x,y,".", label=bird_name)
+plt.xlabel("Longitude")
+plt.ylabel("Latitude")
+plt.legend(loc="lower right")
+plt.show()