# # Precipitation at International Falls 1970-2010
# * Load Precipitation Data for International Falls
# * Comparison of 1970-1999 to 2000-2010
# * Correlation of Precipitation and Rainy River Flow
# ## Initialize Notebook
# In[217]:
# Display graphics inline with the notebook
get_ipython().run_line_magic('matplotlib', 'inline')
# Standard Python modules
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
import datetime
# use seaborn for statistical plotting
import seaborn as sns
sns.set_context('talk')
# Data directory
dir = '../data/'
img = '../images'
# ## Load Precipitation Data for International Falls
# Precipitation data for International Falls was obtained from the Global Historical Climatology network and stored as a Pandas data series in `./data/KINL.pkl` in standard metric units of mm. In the following cell reads the data series
# In[218]:
KINL = pd.read_pickle(dir+'KINL.pkl')
KINL = KINL['1970':'2010']
# ### Annual Precipitation
# In[219]:
plt.figure(figsize=(10,4))
KINL.groupby(KINL.index.year).sum().plot(kind='bar')
plt.title('Total Annual Precipitation')
plt.ylabel('mm')
meanPrcp = KINL.groupby(KINL.index.year).sum().mean()
plt.plot(plt.xlim(),(meanPrcp,meanPrcp),'r--');
# ### Distribution of Annual Precipitation
# In[220]:
import warnings
warnings.simplefilter("ignore")
sns.distplot(KINL.groupby(KINL.index.year).sum(),bins=10,rug=True)
plt.title('Distribution of Total Annual Precipitation [mm], 1970-2014')
# ### Monthly Precipitation
# In[221]:
(KINL.groupby(KINL.index.month).sum()/41).plot(kind='bar')
plt.gca().set_xticks(range(0,13))
plt.gca().set_xticklabels(('Jan','Feb','Mar','Apr','May','Jun',
'Jul','Aug','Sep','Oct','Nov','Dec'));
plt.title('Mean Month Precipitation 1970-2010')
plt.ylabel('mm')
plt.ylim(0,110)
# ### Distribution of Monthy Precipitation
# In[224]:
KINL = pd.read_pickle(dir+'KINL.pkl')
prcp = []
for (mo,yr),p in KINL.groupby([KINL.index.month,KINL.index.year]):
prcp.append([yr,mo,p.sum()])
prcp = pd.DataFrame(prcp)
prcp.columns=['Year','Month','PRCP']
sns.boxplot(x='Month',y='PRCP',data=prcp,color='y')
plt.gca().set_xticks(np.arange(0.0,12.0,1.0))
plt.gca().set_xticklabels(('Jan','Feb','Mar','Apr','May','Jun',
'Jul','Aug','Sep','Oct','Nov','Dec'));
plt.title('Distribution of Monthly Precipitation 1970-2010')
plt.ylabel('mm')
#plt.ylim(0,280)
#plt.gca().set_yticks(np.arange(0,280,25.4))
#plt.gca().set_yticklabels(np.arange(0,12,1))
#plt.ylabel('Inches')
# In[227]:
KINL = pd.read_pickle(dir+'KINL.pkl')
# create list of precip data consisting of year, month, and month precip
prcp = []
for (mo,yr),p in KINL.groupby([KINL.index.month,KINL.index.year]):
prcp.append([yr,mo,p.sum()])
# convert to data frame
prcp = pd.DataFrame(prcp)
prcp.columns=['Year','Month','PRCP']
# create violinplots
sns.violinplot(x='Month',y='PRCP',data=prcp,color='y')
plt.gca().set_xticks(np.arange(0.0,12.0,1.0))
plt.gca().set_xticklabels(('Jan','Feb','Mar','Apr','May','Jun',
'Jul','Aug','Sep','Oct','Nov','Dec'));
plt.title('Distribution of Monthly Precipitation 1970-2010')
plt.ylabel('mm')
# ## Comparison of 1970-1999 to 2000-2010
# In[261]:
KINL = pd.read_pickle(dir+'KINL.pkl')
yrA = 1970
yrB = 2000
# create list of data entries with yr, mo, period, and monthly precip
prcp = []
for (mo,yr),p in KINL.groupby([KINL.index.month,KINL.index.year]):
prcp.append([yr,mo,p.sum()])
# convert to data frame
prcp = pd.DataFrame(prcp)
prcp.columns=['Year','Month','PRCP']
# assign period
strA = "{0:d}-{1:d}".format(yrA,yrB-1)
strB = "{0:d}-".format(yrB)
prcp['Period'] = np.where(prcp['Year']