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plotting_stereo.py
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plotting_stereo.py
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from __future__ import division
import os
import sys
import glob
import fnmatch
import numpy as np
import pandas as pd
import PIL.Image as Image
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import scipy.stats as sps
from datetime import datetime, timedelta
from matplotlib.patches import Polygon
sys.path.insert(0, r'C:\\Users\\shann\\OneDrive\\Documents\\Research\\Workspace\\Code\\useful_code')
import misc
class STEREOPlot:
def __init__(self, data_loc, fig_loc):
self.data_loc = data_loc
self.fig_loc = fig_loc
self.sta_name = 'STEREO-A'
self.stb_name = 'STEREO-B'
self.sta_colour = 'pink'
self.stb_colour = 'lightskyblue'
self.sta_means_colour = 'crimson'
self.stb_means_colour = 'navy'
self.figsize = [14, 9]
self.small_font = 12
self.big_font = 20
def add_corr_text(self, ax, df, col_x, col_y, corr):
dfa = df[df.craft.values == 'sta']
dfb = df[df.craft.values == 'stb']
new_line = (np.nanmax(df[col_y]) - np.nanmin(df[col_y]))/15
a_corr, a_pval = sps.spearmanr(dfa[col_x].values, dfa[col_y].values,
nan_policy='omit')
b_corr, b_pval = sps.spearmanr(dfb[col_x].values, dfb[col_y].values,
nan_policy='omit')
ax.text(corr[0], corr[1], 'Spearman Rank Corr. Coeffs.',
fontsize=16)
ax.text(corr[0], corr[1]-new_line,
' STEREO-A: ' + str('{:+.2f}'.format(round(a_corr, 2))),
fontsize=16)
ax.text(corr[0], corr[1]-2*new_line,
' STEREO-B: ' + str('{:+.2f}'.format(round(b_corr, 2))),
fontsize=16)
return ax
def scatter_x_vs_y(self, ax, df, col_x, col_y):
for craft in ['sta', 'stb']:
dfc = df[df.craft.values == craft]
ax.scatter(dfc[col_x].values, dfc[col_y].values,
color=getattr(self, craft + '_colour'),
label=getattr(self, craft + '_name'))
return ax
def scatter_x_vs_y_with_z_markers(self, ax, df, col_x, col_y, col_z):
markers = ['^', 'x', '*', 'o', 'd', '1', '2', '3']
for n, z in enumerate(np.unique(df[col_z])):
if z != 'nan':
for craft in ['sta', 'stb']:
dfc = df[df.craft.values == craft]
dfc_z = dfc[dfc[col_z] == z]
ax.scatter(dfc_z[col_x].values, dfc_z[col_y].values,
color=getattr(self, craft + '_colour'),
label=getattr(self, craft + '_name') + ' ' + z,
marker=markers[n])
return ax
def add_time_means(self, ax, df, col, means_type='independent', means_t=12,
err='std'):
for craft in ['sta', 'stb']:
dfc = df[df.craft.values == craft]
if means_type == 'independent':
dates, means, errs = misc.find_time_means(dfc[col].values,
dfc['time'],
t=means_t, err=err)
elif means_type == 'running':
# NB RUNNING MEANS DOES NOT WORK
dates, means, errs = misc.find_time_running_means(dfc[col].values,
dfc['time'],
t=means_t,
err=err)
if means_t == 12:
label = getattr(self, craft + '_name') + ' yearly means'
else:
label = getattr(self, craft + '_name') + ' ' + str(means[1]) + '-month means'
ax.plot(dates, means,
color=getattr(self, craft + '_means_colour'),
label=label, marker='s', linestyle='dashed')
ax.errorbar(dates, means, yerr=errs,
color=getattr(self, craft + '_means_colour'),
capsize=3, linestyle='None')
ax.legend(loc=0, fontsize=12)
return ax
def plot_x_vs_y_ax(self, ax, df, col_x, col_y, label_x, label_y, col_z=None,
lim_x=False, lim_y=False, oneone=False, corr=False):
"""
:param: corr: prints correlation coefficients on the image. This should
be [X,Y] coords for top left corner of text.
add_discrete: changes the markers according to data
"""
for col in ['craft', col_x, col_y]:
if col not in df.columns:
raise ValueError("no " + col + " column in df")
if col_z != None:
ax = self.scatter_x_vs_y_with_z_markers(ax, df, col_x, col_y,
col_z)
else:
ax = self.scatter_x_vs_y(ax, df, col_x, col_y)
ax.tick_params(axis="x", labelsize=self.small_font)
ax.tick_params(axis="y", labelsize=self.small_font)
if lim_x:
ax.set_xlim(lim_x)
if lim_y:
ax.set_ylim(lim_y)
if oneone:
ax.plot((plt.gca().get_xlim()), (plt.gca().get_ylim()), ls="--",
c=".3")
if corr:
ax = self.add_corr_text(ax, df, col_x, col_y, corr)
ax.legend(loc=0, fontsize=self.small_font)
# plt.savefig(os.path.join(self.fig_loc, col_x + ' vs ' + col_y))
return ax
def plot_x_vs_y(self, df, col_x, col_y, label_x, label_y, col_z=None,
lim_x=False, lim_y=False, oneone=False, corr=False):
fig, ax = plt.subplots(figsize=self.figsize)
ax = self.plot_x_vs_y_ax(ax, df, col_x, col_y, label_x, label_y,
col_z=col_z, lim_x=lim_x, lim_y=lim_y,
oneone=oneone, corr=corr)
ax.set_xlabel(label_x, fontsize=self.big_font)
ax.set_ylabel(label_y, fontsize=self.big_font)
plt.savefig(os.path.join(self.fig_loc, col_x + ' vs ' + col_y))
def __extract_boxplot_vals(self, df, col_x, col_y):
data = []
labels = []
nums = []
for craft in ['sta', 'stb']:
dfc = df[df.craft == craft]
# append average values
data.append(dfc[col_y])
nums.append(str(len(dfc)))
labels.append('All Events')
# append data for each col_x category
for m in np.unique(df[col_x]):
dfcm = dfc[dfc[col_x] == m]
if len(dfcm) > 1:
data.append(dfc[col_y])
nums.append(str(len(dfcm)))
labels.append(str(m))
return data, labels, nums
def boxplot(self, df, col_x, col_y, label_x, label_y, medians=False):
"""
median: bool, if True plot median values onto median line
"""
fig, ax = plt.subplots(figsize=self.figsize)
data, labels, nums = self.__extract_boxplot_vals(self, df, col_x, col_y)
bp = ax.boxplot(data, medianprops=dict(color='k'), widths=0.6)
# format y-axis
ax.tick_params(axis="y", labelsize=self.small_font)
plt.ylabel(label_y, fontsize=self.big_font)
# sort x-axis
plt.xlabel(label_x, fontsize=self.big_font)
xticks = np.unique(labels)
lowlim = ax.get_xlim()[0]
hilim = ax.get_xlim()[1]
spacing = (hilim - lowlim) / len(xticks)
ax.set_xticklabels(xticks, fontsize=16)
ax.set_xticks(np.arange(lowlim + spacing/2, hilim, spacing))
if medians:
spacing2 = (hilim - lowlim) / len(data)
locs = np.arange(lowlim + spacing2/2, hilim, spacing2)
for n in range(len(data)):
if n < 2:
color = 'black'
else:
color = 'black'
ax.text(locs[n], bp['medians'][n]._y[0]+0.1, nums[n],
fontsize=16, ha='center', color=color)
# add line to separate average from individual columns
ax.axvline(lowlim+spacing, color='k', ls='-', lw=1)
# Now fill the boxes with desired color
box_colors = [self.sta_colour, self.stb_colour]
num_boxes = len(data)
for i in range(num_boxes):
box = bp['boxes'][i]
boxX = []
boxY = []
for j in range(5):
boxX.append(box.get_xdata()[j])
boxY.append(box.get_ydata()[j])
box_coords = np.column_stack([boxX, boxY])
ax.add_patch(Polygon(box_coords, facecolor=box_colors[i % 2]))
patches = []
for craft in ['sta', 'stb']:
patch = mpatches.Patch(facecolor=getattr(self, craft + '_colour'),
label=getattr(self, craft + '_name'),
edgecolor='k')
patches.append(patch)
plt.legend(handles=patches, loc=0, frameon=False,
fontsize=self.small_font)
plt.savefig(os.path.join(self.fig_loc,
'boxplot complexity vs ' + col_x))
def histogram(self, ax, sta_vals, stb_vals, label):
vals = dict({'sta' : sta_vals, 'stb' : stb_vals})
for craft in ['sta', 'stb']:
ax.hist(vals[craft], bins=np.arange(-5, 4, 0.5), alpha=0.5,
label=getattr(self, craft + '_name'),
color=getattr(self, craft + '_colour'))
ax.set_xlabel(label, fontsize=16)
ax.set_ylabel('Frequency', fontsize=16)
ax.legend(loc=0, frameon=False, fontsize=12)
return ax
def plot_a_vs_b(self, ax, sta_vals, stb_vals, label):
ax.scatter(sta_vals, stb_vals)
ax.set_xlabel(self.sta_name + ' ' + label, fontsize=16)
ax.set_ylabel(self.stb_name + ' ' + label, fontsize=16)
# Add 1:1 line
ax.plot((plt.gca().get_xlim()), (plt.gca().get_ylim()), ls="--", c=".3")
# plt.savefig(os.path.join(self.fig_loc, label + 'A vs B'))
return ax
def __find_ratio(self, sta_vals, stb_vals):
ratios = []
for i in range(len(sta_vals)):
ratios.append(sta_vals[i] / stb_vals[i])
return ratios
def plot_ab_ratio_dist(self, ax, sta_vals, stb_vals, label):
ratios = self.__find_ratio(sta_vals, stb_vals)
ax.hist(ratios)
ax.set_xlabel(self.sta_name + ' / ' + self.stb_name + ' ' + label,
fontsize=self.fontsize)
ax.set_ylabel('Frequency', fontsize=self.fontsize)
# plt.savefig(os.path.join(self.fig_loc, label + 'AB ratio hist'))
return ax
def __sort_deg_diff(diffs):
# convert to 0-180 not 0-360
new_diffs = []
for n, i in enumerate(diffs):
if abs(i) > 180:
new_diffs.append(360 - abs(i))
else:
new_diffs.append(i)
return new_diffs
def __find_ab_diff(self, sta_vals, stb_vals, abs_col=False, deg=False):
diffs = np.array(sta_vals) - np.array(stb_vals)
if abs_col == True:
diffs = abs(diffs)
if deg == True:
diffs = self.__sort_deg_diff(diffs)
return diffs
def plot_ab_diff_dist(self, ax, sta_vals, stb_vals, label, abs_col=False,
deg=False, bins=70):
diffs = self.__find_ab_diff(sta_vals, stb_vals, abs_col=abs_col,
deg=deg)
ax.hist(diffs, bins=bins)
ax.set_xlabel(self.sta_name + ' - ' + self.stb_name + ' ' + label,
fontsize=self.fontsize)
ax.set_ylabel('Frequency', fontsize=self.fontsize)
# ax.savefig(os.path.join(self.fig_loc, label + 'A-B hist'))
return ax
def plot_ab_diff_vs_x(self, ax, sta_vals, stb_vals, x_vals,
label_x, label_y, abs_col=False, deg=False):
diffs = self.__find_ab_diff(sta_vals, stb_vals, abs_col=abs_col,
deg=deg)
ax.scatter(diffs, x_vals)
ax.set_xlabel(label_x, fontsize=self.fontsize)
ax.set_ylabel(label_y + ' A-B', fontsize=self.fontsize)
ax.axhline(y=0, ls='--', color='.3')
# plt.savefig(os.path.join(self.fig_loc, label_x + 'A-B vs ' + label_y))
return ax
def plot_ab_diff_vs_ab_diff(self, ax,
sta_vals_x, stb_vals_x, label_x,
sta_vals_y, stb_vals_y, label_y,
abs_x=False, abs_y=False,
deg_x=False, deg_y=False):
diffs_x = self.__find_ab_diff(sta_vals_x, stb_vals_x, abs_col=abs_x,
deg=deg_x)
diffs_y = self.__find_ab_diff(sta_vals_y, stb_vals_y, abs_col=abs_y,
deg=deg_y)
if abs_x == False:
plt.axvline(x=0, ls='--', color='.3')
if abs_y == False:
plt.axhline(y=0, ls='--', color='.3')
ax.scatter(diffs_x, diffs_y)
ax.set_xlabel(label_x + ' A-B', fontsize=self.fontsize)
ax.set_ylabel(label_y + ' A-B', fontsize=self.fontsize)
# plt.savefig(os.path.join(self.fig_loc, label_x + ' A-B vs ' + label_y + ' A-B'))
return ax