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plotting.py
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plotting.py
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# Import Necessary Functions
from dateutil.relativedelta import *
import logging
import pandas as pd
import matplotlib.dates as md
import matplotlib.pyplot as plt
from utils import *
# Plotting Functions
# Basic line plot for each site/fuelType/fuelVariation
#
# @ param dataFrame - pandas dataframe with the data to plot from get_data function in the FMR.py script
#
def plot_lines(dataFrame):
# Quit plotting if no data is available
if len(dataFrame.head(1)["date"]) < 1:
print("dataFrame has no data")
return
dataFrame['fuel_type'] = dataFrame['fuel_type'].fillna('None').str.lower()
dataFrame['fuel_variation'] = dataFrame['fuel_variation'].fillna('None').str.lower()
df = dataFrame.groupby(['site_number','fuel_type','fuel_variation','date']).mean()
mids = df.index
# Dropping one level from multi index (date) and find all the unique combinations of the other levels
combos = mids.droplevel('date').unique()
if len(combos) < 50:
fig, ax = plt.subplots(figsize=(14,9))
plt.subplots_adjust(top=.9, bottom=0.15)
legend = []
for combo in combos:
x = pd.to_datetime(df.loc[combo,:].index)
y = df.loc[combo,:].values
ax.plot(x,y,'.-')
legend.append('{} - {} - {}'.format(*combo))
ax.set_ylabel("Fuel Mositure (%)", fontsize = 20)
ax.set_xlabel("Time (Years)", fontsize = 20)
ax.tick_params(axis='both', length = 10, width = 1, labelsize=15)
ax.tick_params(axis='both', which='minor', labelsize=15)
ax.xaxis.set_major_locator(md.YearLocator())
ax.xaxis.set_minor_locator(md.MonthLocator(bymonth=[7]))
ax.xaxis.set_major_formatter(md.DateFormatter("\n%Y"))
ax.xaxis.set_minor_formatter(md.DateFormatter("%b"))
plt.xticks(rotation = 45)
plt.grid(True)
plt.legend(legend,loc='upper left')
plt.show()
else:
logging.error('Too many plots. Consider filtering data using get_data parameters.')
# Standard deviation plot for each fuelType/fuelVariation (averaging all sites)
#
# @ param dataFrame - pandas dataframe with the data to plot from get_data function in the FMR.py script
#
def plot_lines_mean(dataFrame):
# Quit plotting if no data is available
if len(dataFrame.head(1)["date"]) < 1:
print("dataFrame has no data")
return
dataFrame['fuel_type'] = dataFrame['fuel_type'].fillna('None').str.lower()
dataFrame['fuel_variation'] = dataFrame['fuel_variation'].fillna('None').str.lower()
df = dataFrame.groupby(['fuel_type','fuel_variation',dataFrame.date.dt.year, dataFrame.date.dt.month]).agg(['mean','std'])
df.index.names = ['fuel_type','fuel_variation','year','month']
mid = df.index
year_combos = mid.droplevel('month').unique()
combos = year_combos.droplevel('year').unique()
fig, ax = plt.subplots(figsize=(14,9))
plt.subplots_adjust(top=.9, bottom=0.15)
legend = []
if len(combos) >= 50:
logging.error('Too many plots. Consider filtering data using get_data parameters.')
return
for combo in combos:
combo_data = df.loc[combo,'percent']
dates = pd.to_datetime(['{:04d}-{:02d}'.format(y,m) for y,m in df.loc[combo].index.to_numpy()])
means = combo_data.values[:,0]
stds = combo_data.values[:,1]
ax.plot(dates, means, '.-')
ax.fill_between(dates, means - stds, means + stds, alpha=0.2)
legend.append('{} - {}'.format(*combo))
ax.set_ylabel("Fuel Mositure (%)", fontsize = 20)
ax.set_xlabel("Time (Years)", fontsize = 20)
ax.tick_params(axis='both', length = 10, width = 1, labelsize=15)
ax.tick_params(axis='both', which='minor', labelsize=15)
ax.xaxis.set_major_locator(md.YearLocator())
ax.xaxis.set_minor_locator(md.MonthLocator(bymonth=[7]))
ax.xaxis.set_major_formatter(md.DateFormatter("\n%Y"))
ax.xaxis.set_minor_formatter(md.DateFormatter("%b"))
plt.grid(True)
plt.legend(legend,loc='upper left')
plt.xticks(rotation = 45)
plt.show()
# Bar plot that shows mean and standard devaition values for all the data each year unless monthly paramter is set to True.
#
# @ param dataFrame - pandas dataframe with the data to plot from get_data function in the FMR.py script
# @ param monthly - boolean to change from yearly to monthly bars
#
def plot_bars_mean(dataFrame, monthly=False):
# Quit plotting if no data is available
if len(dataFrame.head(1)["date"]) < 1:
print("dataFrame has no data")
return
dataFrame['fuel_type'] = dataFrame['fuel_type'].fillna('None').str.lower()
dataFrame['fuel_variation'] = dataFrame['fuel_variation'].fillna('None').str.lower()
if monthly:
df = dataFrame.groupby([dataFrame.date.dt.year,dataFrame.date.dt.month]).agg(['mean','std'])
df.index.names = ['year','month']
dates = pd.to_datetime(['{:04d}-{:02d}'.format(y,m) for y,m in df.index.to_numpy()])
width = len(dates)*.1
else:
df = dataFrame.groupby([dataFrame.date.dt.year],dropna=False).agg(['mean','std'])
df.index.names = ['year']
dates = pd.to_datetime(['{:04d}-01'.format(y) for y in df.index.to_numpy()])
width = len(dates)*52*.2
means = df['percent']['mean']
stds = df['percent']['std']
fig, ax = plt.subplots(figsize=(14,9))
plt.subplots_adjust(top=.9, bottom=0.15)
plt.bar(dates,means,width=width,alpha=.5)
plt.bar(dates,stds,width=width,alpha=.5)
ax.set_ylabel("Fuel Mositure (%)", fontsize = 20)
ax.set_xlabel("Time (Years)", fontsize = 20)
ax.tick_params(axis='both', length = 10, width = 1, labelsize=15)
ax.tick_params(axis='both', which='minor', labelsize=15)
ax.xaxis.set_major_locator(md.YearLocator())
ax.xaxis.set_minor_locator(md.MonthLocator(bymonth=[7]))
ax.xaxis.set_major_formatter(md.DateFormatter("\n%Y"))
ax.xaxis.set_minor_formatter(md.DateFormatter("%b"))
plt.legend(['Mean','Std'],loc='upper left',prop={'size': 13})
plt.xticks(rotation = 45)
plt.show()
# Bar plot that shows the number of observations over the time period found in the dataFrame
#
# @param dataFrame - pandas dataframe with the data to plot from get_data function in the FMR.py script
#
def plot_yearly_obs(dataFrame):
# Quit plotting if no data is available
if len(dataFrame.head(1)["date"]) < 1:
print("dataFrame has no data")
return
years = []
minYear = min(dataFrame.date.dt.year)
maxYear = max(dataFrame.date.dt.year)+1
for i in range(minYear,maxYear):
tempLFMC = dataFrame[dataFrame.date.dt.year == i].reset_index(drop=True)
years.append(len(tempLFMC.percent))
fig, ax = plt.subplots(figsize=(14,9))
plt.subplots_adjust(top=.9, bottom=0.15)
ax.bar(range(minYear,maxYear),years)
plt.xticks(range(minYear,maxYear),rotation=45,fontsize=15)
plt.yticks(fontsize=15)
plt.ylabel("Observations",fontsize=20)
plt.xlabel("Time (Years)",fontsize=20)
plt.ticklabel_format(style='plain', axis='y')
plt.title(f"Number of Observations from {min(dataFrame.date.dt.year.unique())} - {max(dataFrame.date.dt.year.unique())}",fontsize=25)
plt.show()
# Bar plot that shows the fuel types and number of observations of each fuel type found in the dataFrame
#
# @param dataFrame - pandas dataframe with the data to plot from get_data function in the FMR.py script
#
def plot_fuel_types(dataFrame):
# Quit plotting if no data is available
if len(dataFrame.head(1)["date"]) < 1:
print("dataFrame has no data")
return
fig, ax = plt.subplots(figsize=(14,9))
plt.subplots_adjust(top=.9, bottom=0.15)
obs = []
ftype = []
for fuel in dataFrame.fuel_type.unique():
obs.append(len(dataFrame[dataFrame.fuel_type == fuel]))
ftype.append(fuel)
obsDf = pd.DataFrame({"fuel_type":ftype,"obs":obs})
obsDf = obsDf.sort_values(by="obs",ascending=False).reset_index(drop=True)
if len(obsDf["fuel_type"].unique()) > 25:
obsDf = obsDf.head(25)
ax.bar(range(len(obsDf.obs)),obsDf.obs)
plt.xticks(range(len(obsDf.obs)),obsDf.fuel_type,rotation=35,horizontalalignment='right',fontsize=15)
plt.yticks(fontsize=15)
ax.set_xlabel("Vegetation Types",fontsize=20)
ax.set_ylabel("Number of Observations",fontsize=20)
for label in ax.yaxis.get_majorticklabels():
label.set_fontsize(15)
for label in ax.xaxis.get_majorticklabels():
label.set_fontsize(15)
plt.title(f"Fuel Type Sampling Observations from {min(dataFrame.date.dt.year.unique())} - {max(dataFrame.date.dt.year.unique())}",fontsize=25)
plt.show()