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%matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
from matplotlib import pyplot as plt
import seaborn as sns
from datetime import datetime
import datetime
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# load the floats data
# ********************
# *** CSV files ***
# ********************
# load CSV output
#plt = plt.figure(8,6)
plt.close('all')
plt.cla() # axis
plt.clf() # figure
plt.show()
freq = 2
pref = 'df_chl_dist_out_'
surf = '_modisa.csv'
infile = pref + str(freq) + 'D' + surf
folder = '../data_collector_modisa_chla9km/df_multidimInterpolation/'
direc = folder + infile
direc
print('in_filename and path:', direc)
df_tmp = pd.read_csv(direc, index_col='index')
df_tmp.sort_values(['id', 'time'])[:20]
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df_tmp['time'] = pd.to_datetime(df_tmp['time']) # ,format='%m/%d/%y %I:%M%p'
df_timed = df_tmp.set_index('time')
# add a new column to the dataset
df_timed['year'] = df_timed.index.year
df_timed[:20]
year_max = df_timed.index.year.max()
year_min = df_timed.index.year.min()
print('year_max', year_max)
print('year_min', year_min)
#print(df_timed)
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### Part 1: Plot for the mean of the weekly trends
### Mean
start = year_min
for i in range(0,3):
#plt.subplot(311+i)
plt.figure(figsize=(8,6))
plt.title("mean of the weekly binned Lagrangian rate of change of Chl-a", fontsize=10)
for current_year in range(start, start+5):
mask = df_timed.year == current_year
df_yearly = df_timed[mask]
#print('\n summary statistics of chl_rate_week for the year %d \n' % (current_year) )
#print(df_yearly.chl_rate_week.describe())
axes1=df_yearly.groupby(['week_rotate'])['chl_rate_week'].mean().plot(linestyle="-", linewidth=0.8, label='%d' % current_year )
start = start + 4
axes1.set_ylim(-10, 10)
#axes1.set_title("Year " + str(current_year) + ": Line plot of the weekly data on the rate of change per week of the $Chl_a$ Concentration", fontsize=10)
plt.xlabel('week', fontsize=10)
plt.ylabel('rate of change of the $Chl_a$ in $mg/(m^3 \cdot 7days)$', fontsize=10)
plt.yticks(np.arange(-10, 10, 0.5))
plt.xticks(np.arange(1, 25, 1))
axes1.legend(bbox_to_anchor=(1.15, 1.05))
plt.savefig(str(freq)+"D_weekly_mean_" + str(i) +".pdf")
plt.show()
plt.close()
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### Part 2: Plot for the median of the weekly trends
### Median
start = year_min
for i in range(0,3):
#plt.subplot(311+i)
plt.figure(figsize=(8,6))
plt.title("median of the weekly binned Lagrangian rate of change of Chl-a", fontsize=10)
for current_year in range(start, start+5):
mask = df_timed.year == current_year
df_yearly = df_timed[mask]
#print('\n summary statistics of chl_rate_week for the year %d \n' % (current_year) )
#print(df_yearly.chl_rate_week.describe())
axes1=df_yearly.groupby(['week_rotate'])['chl_rate_week'].quantile(.50).plot(linestyle="-", linewidth=0.8, label='%d' % current_year )
start = start + 4
axes1.set_ylim(-10, 10)
#axes1.set_title("Year " + str(current_year) + ": Line plot of the weekly data on the rate of change per week of the $Chl_a$ Concentration", fontsize=10)
plt.xlabel('week', fontsize=10)
plt.ylabel('rate of change of the $Chl_a$ in $mg/(m^3 \cdot 7days)$', fontsize=10)
plt.yticks(np.arange(-10, 10, 0.5))
plt.xticks(np.arange(1, 25, 1))
axes1.legend(bbox_to_anchor=(1.15, 1.05))
#plt.savefig(str(freq)+"D_weekly_median_" + str(i) +".pdf")
plt.show()
plt.close()
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### Part 3: Plot for the 25% quantile of the weekly trends
### 25% quantile
start = year_min
for i in range(0,3):
#plt.subplot(311+i)
plt.figure(figsize=(8,6))
plt.title("25% quantile of the weekly binned Lagrangian rate of change of Chl-a", fontsize=10)
for current_year in range(start, start+5):
mask = df_timed.year == current_year
df_yearly = df_timed[mask]
#print('\n summary statistics of chl_rate_week for the year %d \n' % (current_year) )
#print(df_yearly.chl_rate_week.describe())
axes1=df_yearly.groupby(['week_rotate'])['chl_rate_week'].quantile(.25).plot(linestyle="-", linewidth=0.8, label='%d' % current_year )
start = start + 4
axes1.set_ylim(-10, 10)
#axes1.set_title("Year " + str(current_year) + ": Line plot of the weekly data on the rate of change per week of the $Chl_a$ Concentration", fontsize=10)
plt.xlabel('week', fontsize=10)
plt.ylabel('rate of change of the $Chl_a$ in $mg/(m^3 \cdot 7days)$', fontsize=10)
plt.yticks(np.arange(-10, 10, 0.5))
plt.xticks(np.arange(1, 25, 1))
axes1.legend(bbox_to_anchor=(1.15, 1.05))
#plt.savefig(str(freq)+"D_weekly_quantile(25)_" + str(i) +".pdf")
plt.show()
plt.close()
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### Part 4: Plot for the 75% quantile of the weekly trends
### 75% quantile
start = year_min
for i in range(0,3):
#plt.subplot(311+i)
plt.figure(figsize=(8,6))
plt.title("75% of the weekly binned Lagrangian rate of change of Chl-a", fontsize=10)
for current_year in range(start, start+5):
mask = df_timed.year == current_year
df_yearly = df_timed[mask]
#print('\n summary statistics of chl_rate_week for the year %d \n' % (current_year) )
#print(df_yearly.chl_rate_week.describe())
axes1=df_yearly.groupby(['week_rotate'])['chl_rate_week'].quantile(.75).plot(linestyle="-", linewidth=0.8, label='%d' % current_year )
start = start + 4
axes1.set_ylim(-10, 10)
#axes1.set_title("Year " + str(current_year) + ": Line plot of the weekly data on the rate of change per week of the $Chl_a$ Concentration", fontsize=10)
plt.xlabel('week', fontsize=10)
plt.ylabel('rate of change of the $Chl_a$ in $mg/(m^3 \cdot 7days)$', fontsize=10)
plt.yticks(np.arange(-10, 10, 0.5))
plt.xticks(np.arange(1, 25, 1))
axes1.legend(bbox_to_anchor=(1.15, 1.05))
#plt.savefig(str(freq)+"D_weekly_quantile(75)_" + str(i) +".pdf")
plt.show()
plt.close()
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#matplotlib.pyplot.close("all")
plt.close('all')
plt.cla() # axis
plt.clf() # figure
plt.show()
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