In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import xarray as xr
from datetime import datetime
import datetime
In [2]:
# load the floats data
# ********************
# *** CSV files ***
# ********************
# load the floats data, take the lon and lat as list out and calculate the distance
# load CSV output
# some how the CSV Format has some compatibility issues here
# see readme file for the file convetion in the experiments,
# for instance "3" indicates distance is addd to the dataset
plt.close('all')
plt.cla() # axis
plt.clf() # figure
plt.show()
# freqency
freq = 5
suf = 'D'
in_filename = 'df_chl_out_'+str(freq)+ suf +'_modisa_3.csv'
out_filename = 'df_chl_out_'+str(freq)+ suf +'_modisa_4.csv'
folder = './data_collector_modisa_chla9km/'
direc = folder + in_filename
direc
print('in_filename and path:', direc)
print('out_filename:', out_filename)
df_chl_out_3 = pd.read_csv(direc, index_col='index')
df_chl_out_3
Out[2]:
In [3]:
# All the rates on the same time frequency
check1 = df_chl_out_3.chl_rate/ df_chl_out_3.chlor_a
check1 = check1/ freq
check2 = df_chl_out_3.chl_rate.divide(freq *df_chl_out_3.chlor_a, axis = 'index')
# an check
np.sum(abs(check1 - check2))
Out[3]:
In [4]:
# add the column to the dataframe and output the dataset
df_chl_out_3['chlor_a_logE_rate'] = pd.Series(np.array(check2), index=df_chl_out_3.index)
df_chl_out_3.head()
Out[4]:
In [5]:
df_chl_out_3.chlor_a_logE_rate.describe() # more scattered on the left hand side
Out[5]:
In [6]:
# visualize the ROC of log(chl_a) around the arabian sea region
fig, ax = plt.subplots(figsize=(12,10))
df_chl_out_3.plot(kind='scatter', x='lon', y='lat', c='chlor_a_logE_rate', cmap='RdBu_r', vmin=check2.median()-0.5*check2.std(), vmax=check2.max(), edgecolor='none', ax=ax, title = 'rate of change of the log-scale chl-a')
Out[6]:
In [7]:
# histogram for non standarized data
axdf_chl = df_chl_out_3.chlor_a_logE_rate.dropna().hist(bins=100,range=[-1.5,0.5]) # there are very a few small values on the left
axdf_chl.set_title('histogram of the rate of change of the log-scale chl-a')
Out[7]:
In [8]:
# standarized series
tmp = df_chl_out_3.chlor_a_logE_rate.dropna()
tmp = (tmp - tmp.mean())/tmp.std()
axdf_chl_stdan = tmp.hist(bins=100,range=[-1.5,0.5]) # there are very a few small values on the left
axdf_chl_stdan.set_title('histogram of the standardized rate of change of the log-scale chl-a')
Out[8]:
In [13]:
(np.log(0.140332)-np.log(0.125101)) / freq
Out[13]:
In [10]:
###########################
# On 2D-subsampling Dataset
###########################
# Val 1:
# id:10206, time:2002-11-16"
# (0.140332 - 0.125101) / (freq*0.140332) == 0.0217070946042243
#########
# Val 2:
# id:10206, time:2002-11-16"
# (np.log(0.140332)-np.log(0.125101)) / freq == 0.022977926547090809 # very close to the value above
df_chl_out_3.sort_values(by=['id', 'time']).head()
Out[10]:
In [14]:
# convert into datetime
df_chl_out_3['time'] = pd.to_datetime(df_chl_out_3['time']) # ,format='%m/%d/%y %I:%M%p'
df_chl_out_3.sort_values(by=['id', 'time']).head() # a check
Out[14]:
In [15]:
# CSV CSV CSV CSV with specfic index
# df_chl_out_3.csv -- {lat, lon, temp, chl_rate, dist}
# df_chl_out_3.csv -- {lat, lon, temp, chl_rate, dist, chlor_a_log10_rate}
# 3 represents 3 features: {temp, chl_rate, dist}
# 4 represents 4 features: {temp, chl_rate, dist, chlor_a_log10_rate }
print('out_filename:', out_filename)
df_chl_out_3.to_csv(out_filename, sep=',', index_label = 'index')
# load CSV output
test = pd.read_csv(out_filename, index_col='index')
# a check
test.sort_values(by=['id', 'time']).head()
Out[15]:
In [16]:
## check the week numbers of the range from Nov-01-01 to Mar-01-01
for year in range(2002, 2017):
print(str(year)+'-11-01 is week', datetime.datetime(year, 11, 1).isocalendar()[1]) # 44, 45,
print('----')
for year in range(2002, 2017):
print(str(year)+'-3-31 is week', datetime.datetime(year, 3, 31).isocalendar()[1]) # 13, 14
plt.show()
In [17]:
##### weekly plot on the Lagrangian rate of change of the chl-a
#sns.set(style="white")
#sns.set(color_codes=True)
###
# Approach 1 depreciated
#grouped = df_timed.chl_rate.groupby(df_timed.index.week)
#grouped.plot.box()
###
# Approach 2
# prepare data a. use index or columns to group
###
# select the corresponding weeks, prepare the data
df_timed = df_chl_out_3.set_index('time')
df_timed['week'] = df_timed.index.week
mask_NovMar = (df_timed.week<=14) | (df_timed.week >=44)
df_timed_NovMar = df_timed[mask_NovMar]
#df_timed_NovMar.head()
# now rotate the index to make Nov-01-01 the first month
print('the min and max of the week index is %d, %d :' % (df_timed_NovMar.week.min(), df_timed_NovMar.week.max()) )
# make the 44th week the 1st week
df_timed_NovMar['week_rotate'] = (df_timed_NovMar.week + 10 ) % 53
df_timed_NovMar.week_rotate.describe() # now from 1 to 24
axes1=df_timed_NovMar.groupby(['week_rotate'])['chl_rate'].mean().plot(linestyle="-",color='b', linewidth=1)
df_timed_NovMar.groupby(['week_rotate'])['chl_rate'].quantile(.75).plot(linestyle="--",color='g', linewidth=0.35)
df_timed_NovMar.groupby(['week_rotate'])['chl_rate'].quantile(.50).plot(linestyle="--",color='r', linewidth=0.75)
df_timed_NovMar.groupby(['week_rotate'])['chl_rate'].quantile(.25).plot(linestyle="--",color='g', linewidth=0.35)
axes1.set_ylim(-3,2)
axes1.set_title("Line plot of the weekly data on the rate of change of the $Chl_a$ Concentration", fontsize=10)
plt.xlabel('week', fontsize=10)
plt.ylabel('rate of change of the $Chl_a$ in $mg/(m^3 day)$', fontsize=10)
plt.yticks(np.arange(-3, 2, 0.5))
plt.xticks(np.arange(1, 25, 1))
plt.show()
# http://pandas.pydata.org/pandas-docs/version/0.19.1/visualization.html
#http://blog.bharatbhole.com/creating-boxplots-with-matplotlib/
axes2 = df_timed_NovMar.boxplot(column='chl_rate', by='week_rotate')
plt.suptitle("") # equivalent
axes2.set_ylim(-1.6,1.6)
axes2.set_title("Box plot of the weekly data on the rate of change of the $Chl_a$ Concentration", fontsize=10)
plt.xlabel('week', fontsize=10)
plt.ylabel('rate of change of the $Chl_a$ in $mg/(m^3 day)$', fontsize=10)
plt.show()
# the rate of change is slower on the regular scale
#matplotlib.pyplot.close("all")
In [18]:
# weekly plot on the Lagrangian rate of change of the log-scale chl-a
# This is the rate of change on the exponential scale
axes1=df_timed_NovMar.groupby(['week_rotate'])['chlor_a_logE_rate'].mean().plot(linestyle="-",color='b', linewidth=1)
df_timed_NovMar.groupby(['week_rotate'])['chlor_a_logE_rate'].quantile(.75).plot(linestyle="--",color='g', linewidth=0.35)
df_timed_NovMar.groupby(['week_rotate'])['chlor_a_logE_rate'].quantile(.50).plot(linestyle="--",color='r', linewidth=0.75)
df_timed_NovMar.groupby(['week_rotate'])['chlor_a_logE_rate'].quantile(.25).plot(linestyle="--",color='g', linewidth=0.35)
axes1.set_ylim(-1,0.5)
axes1.set_title("Line plot of the weekly data on the rate of change of the log-scale $Chl_a$ Concentration", fontsize=10)
plt.xlabel('week', fontsize=10)
plt.ylabel('rate of change of the log-scale $Chl_a$ in $mg/(m^3 day)$', fontsize=10)
plt.yticks(np.arange(-1, 0.5, 0.25))
plt.xticks(np.arange(1, 25, 1))
#plt.show()
# http://pandas.pydata.org/pandas-docs/version/0.19.1/visualization.html
#http://blog.bharatbhole.com/creating-boxplots-with-matplotlib/
axes2 = df_timed_NovMar.boxplot(column='chlor_a_logE_rate', by='week_rotate')
plt.suptitle("") # equivalent
axes2.set_ylim(-1,0.5)
axes2.set_title("Box plot of the weekly data on the rate of change of the log-scale $Chl_a$ Concentration", fontsize=10)
plt.xlabel('week', fontsize=10)
plt.ylabel('rate of change of the log-scale $Chl_a$ in $mg/(m^3 day)$', fontsize=10)
#plt.show()
#plt.close('all')
Out[18]:
In [19]:
plt.close('all')
plt.cla() # axis
plt.clf() # figure
plt.show()
In [20]:
# spatial plot for different months -- totally five months 1, 2, 3, 11, 12,
for i in range(0,5,1):
month_ind = np.array([11,12,1,2,3])
month_names = ['November', 'December','January','February', 'March']
aa = df_timed_NovMar[df_timed_NovMar.index.month == month_ind[i]]
fig, ax = plt.subplots(figsize=(8,6))
##aa.plot(kind='scatter', x='lon', y='lat', c='chl_rate', cmap='RdBu_r', vmin=aa.chl_rate.median()-0.5*aa.chl_rate.std(), vmax=aa.chl_rate.median()-0.5*aa.chl_rate.std(), edgecolor='none', ax=ax, title = 'rate of change of the $Chl_a$')
##aa.plot(kind='scatter', x='lon', y='lat', c='chl_rate', cmap='RdBu_r', vmin=aa.chl_rate.mean()-0.5*aa.chl_rate.std(), vmax=aa.chl_rate.mean()+0.5*aa.chl_rate.std(), edgecolor='none', ax=ax, title = 'rate of change of the $Chl_a$')
print('\n\n summary of the Chl_rate \n', aa.chl_rate.describe())
aa.plot(kind='scatter', x='lon', y='lat', c='chl_rate', cmap='RdBu_r', vmin=-0.6, vmax=0.6, edgecolor='none', ax=ax, title = 'Rate of change of the $Chl_a$ in %s' % (month_names[i]))
plt.xticks(np.arange(45, 80, 2.5))
plt.yticks(np.arange(0, 28, 2.5))
plt.show()
In [21]:
print("test")
In [ ]: