def plot_post_disp_decomposition(
self,
site,
cmpt,
loc=2,
leg_fs=7,
marker_for_obs='x',
):
y = self.plot_post_obs_linres(site,
cmpt,
label='obs.',
marker=marker_for_obs)
y += self.plot_post_disp_pred_added(site, cmpt, label='pred.')
y += self.plot_R_co(site,
cmpt,
style='-^',
label='Rco',
color='orange')
y += self.plot_E_aslip(site, cmpt, color='green')
y += self.plot_R_aslip(site, cmpt, color='black')
plt.grid('on')
plt.legend(loc=loc, prop={'size': leg_fs})
plt.ylabel(r'meter')
plt.gcf().autofmt_xdate()
plt.title('Postseismic Disp. : {site} - {cmpt}'.format(
site=get_site_true_name(site_id=site), cmpt=cmpt))
def plot_cumu_disp_decomposition(self,
site,
cmpt,
loc=2,
leg_fs=7,
if_ylim=False):
self.plot_cumu_obs_linres(site, cmpt)
y = self.plot_cumu_disp_pred_added(site, cmpt, label='pred.')
y += self.plot_R_co(site,
cmpt,
style='-^',
label='Rco',
color='orange')
y += self.plot_E_cumu_slip(site, cmpt, color='green')
y += self.plot_R_aslip(site, cmpt, color='black')
plt.grid('on')
if if_ylim:
plt.ylim(calculate_lim(y))
plt.ylabel(r'meter')
plt.legend(loc=loc, prop={'size': leg_fs})
plt.gcf().autofmt_xdate()
plt.title('Cumulative Disp.: {site} - {cmpt}'.format(
site=get_site_true_name(site_id=site), cmpt=cmpt))
def plot_cf(cf, color):
t = cf.data.t
y0 = cf.data.y0
plt.plot_date(t+_adj_dates,y0,'x',label=cf.SITE+cf.CMPT,
color='light'+color)
plt.plot_date(cf.data._t+_adj_dates,
cf.data._y0,'x',label=cf.SITE+cf.CMPT,
color=color)
t1 = min(t)
t2 = max(t)
ls=200
plot_func(cf.func,linspace(t1,t2,ls))
plt.title(cf.SITE+'-'+cf.CMPT)
plt.gcf().autofmt_xdate()
def plot2(self,
figNum,
time1,
data1,
time2,
data2,
title='',
units='',
options=''):
plt.figure(figNum)
# plt.hold(True);
plt.grid(True)
if title:
self.title = title
if not units:
self.units = units
# plt.cla()
if self.preTitle:
fig = plt.gcf()
fig.canvas.set_window_title("Figure %d - %s" %
(figNum, self.preTitle))
plt.title("%s" % (self.title))
plt.plot(time1, data1, options)
plt.plot(time2, data2, options)
plt.ylabel('(%s)' % (self.units))
plt.xlabel('Time (s)')
plt.margins(0.04)
def macdStretagy(companyname, begin):
end = datetime.date.today()
#begin=end-pd.DateOffset(365*backtestyear)
#timestamp format and get apple stock.
st = dt.strptime(begin, '%Y-%m-%d')
#st=begin.strftime('%Y-%m-%d')
ed = end.strftime('%Y-%m-%d')
symbol = companyname + ".NS"
datanifty = nift50Indices.objects.filter(Ticker=symbol,
Date__gte=st).values_list(
'Close', 'Open', 'High',
'Low')
#data = pdr.get_data_yahoo(company,st,ed)
dataframe = pd.DataFrame(list(datanifty),
columns=['Close', 'Open', 'High', 'Low'])
print(dataframe)
data = dataframe.astype(float)
#data = pdr.get_data_yahoo('RELIANCE.NS',st,ed)
#MACD
data['macd_talib'], data['signal'], data['diff_macd_signal'] = talib.MACD(
np.asarray(data['Close']),
fastperiod=12,
slowperiod=26,
signalperiod=9)
graph = data.plot(y=['macd_talib', 'signal', 'diff_macd_signal'],
title=companyname + ' MACD')
graph = plt.gcf()
fig_html = mpld3.fig_to_html(graph)
return fig_html
def subplotSingle2x(self,
figNum,
plotNum,
numRows,
numCols,
time,
data,
title='',
units='',
options=''):
print("subplotSingle2x")
plt.figure(figNum)
if title:
self.title = title
if not units:
self.units = units
if self.preTitle:
fig = plt.gcf()
fig.canvas.set_window_title("%s" % (figNum, self.preTitle))
if not figNum in self.sharex.keys():
self.sharex[figNum] = plt.subplot(numRows, numCols, plotNum)
plt.plot(time, data, options)
plt.subplot(numRows, numCols, plotNum, sharex=self.sharex[figNum])
# plt.hold(True);
plt.grid(True)
plt.title("%s" % (self.title))
plt.plot(time, data, options)
plt.ylabel('(%s)' % (self.units))
plt.margins(0.04)
def soStretagy(companyname, begin):
end = datetime.date.today()
#begin=end-pd.DateOffset(365*backtestyear)
#timestamp format and get apple stock.
st = dt.strptime(begin, '%Y-%m-%d')
#st=begin.strftime('%Y-%m-%d')
ed = end.strftime('%Y-%m-%d')
symbol = companyname + ".NS"
datanifty = nift50Indices.objects.filter(Ticker=symbol,
Date__gte=st).values_list(
'Close', 'Open', 'High',
'Low')
#data = pdr.get_data_yahoo(company,st,ed)
dataframe = pd.DataFrame(list(datanifty),
columns=['Close', 'Open', 'High', 'Low'])
print(dataframe)
data = dataframe.astype(float)
#data = pdr.get_data_yahoo('RELIANCE.NS',st,ed)
#Stochastic Oscillator
# stock oversold if below 10 , overbought if greater than 90
data['slowk'], data['slowd'] = talib.STOCH(np.asarray(data['High']),
np.asarray(data['Low']),
np.asarray(data['Close']),
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
graph = data.plot(y=['slowk', 'slowd'],
title=companyname + ' Stochastic Oscillator')
graph = plt.gcf()
fig_html = mpld3.fig_to_html(graph)
return fig_html
def rocStretagy(companyname, begin):
end = datetime.date.today()
#begin=end-pd.DateOffset(365*backtestyear)
#timestamp format and get apple stock.
st = dt.strptime(begin, '%Y-%m-%d')
#st=begin.strftime('%Y-%m-%d')
ed = end.strftime('%Y-%m-%d')
symbol = companyname + ".NS"
datanifty = nift50Indices.objects.filter(Ticker=symbol,
Date__gte=st).values_list(
'Close', 'Open', 'High',
'Low')
#data = pdr.get_data_yahoo(company,st,ed)
dataframe = pd.DataFrame(list(datanifty),
columns=['Close', 'Open', 'High', 'Low'])
print(dataframe)
data = dataframe.astype(float)
#data = pdr.get_data_yahoo('RELIANCE.NS',st,ed)
#Rate of change ROC
data['ROC'] = talib.ROC(np.asarray(data['Close']), 20)
graph = data.plot(y=['ROC'], title=companyname + ' ROC ')
graph = plt.gcf()
fig_html = mpld3.fig_to_html(graph)
return fig_html
def plot(site):
tp = np.loadtxt('../post_offsets/%s.post' % site)
t = dc.asmjd([ii[0] for ii in tp]) + dc.adjust_mjd_for_plot_date
e = [ii[1] for ii in tp]
n = [ii[2] for ii in tp]
u = [ii[3] for ii in tp]
plt.plot_date(t, e, 'x-', label='eastings')
plt.plot(t, n, 'x-', label='northings')
plt.plot(t, u, 'x-', label='upings')
plt.gcf().autofmt_xdate()
plt.legend(loc=0)
plt.title(site)
plt.savefig('%s.png' % site)
#plt.show()
plt.close()
def plot(site):
tp = np.loadtxt('../post_offsets/%s.post'%site)
t = dc.asmjd([ii[0] for ii in tp]) + dc.adjust_mjd_for_plot_date
e = [ii[1] for ii in tp]
n = [ii[2] for ii in tp]
u = [ii[3] for ii in tp]
plt.plot_date(t,e,'x-', label = 'eastings')
plt.plot(t,n,'x-', label = 'northings')
plt.plot(t,u,'x-', label = 'upings')
plt.gcf().autofmt_xdate()
plt.legend(loc=0)
plt.title(site)
plt.savefig('%s.png'%site)
#plt.show()
plt.close()
def maximize_figure(fig=None):
if fig is None:
fig = _plt.gcf()
mng = _plt.get_current_fig_manager()
try:
mng.frame.Maximize(True)
except AttributrError:
print("Failed to maximize figure.")
def maximize_figure(fig=None):
if fig is None:
fig = _plt.gcf()
mng = _plt.get_current_fig_manager()
try:
mng.frame.Maximize(True)
except AttributeError:
print "Failed to maximize figure."
def dynamic_img_show(img,title_str='',fig_size=[14,8],hide_axes=True):
'''Show image <img>. If called repeatedly within a cycle will dynamically redraw image.
#DEMO
import time
for i in range(10):
img = np.zeros([50,50])
img[:i*5]=1
dynamic_img_show(img,'iter=%s'%i)
time.sleep(0.1)
'''
plt.clf()
plt.title(title_str)
plt.imshow(img)
plt.xticks([]); plt.yticks([]);
plt.gcf().set_size_inches(fig_size)
display.display(plt.gcf())
display.clear_output(wait=True)
def plot_vel_decomposition(self, site, cmpt, loc=0, leg_fs=7,
if_ylim=False
):
y = self.plot_pred_vel_added(site, cmpt, label='total')
y += self.plot_vel_R_co(site, cmpt,
style='-^', label='Rco', color='orange')
y += self.plot_vel_E_cumu_slip(site, cmpt, color='green')
y += self.plot_vel_R_aslip(site, cmpt, color='black')
plt.grid('on')
if if_ylim:
plt.ylim(calculate_lim(y))
plt.ylabel(r'mm/yr')
plt.legend(loc=loc, prop={'size':leg_fs})
plt.gcf().autofmt_xdate()
plt.title('Cumulative Disp.: {site} - {cmpt}'.format(
site = get_site_true_name(site_id=site),
cmpt = cmpt
))
def plot_post_disp_decomposition(self, site, cmpt, loc=2, leg_fs=7,
added_label = None,
marker_for_obs = 'x',
):
y = self.plot_post_obs_linres(site,cmpt, label='obs.', marker=marker_for_obs)
y += self.plot_post_disp_pred_from_result_file(site,cmpt, label='pred.')
y += self.plot_R_co(site, cmpt,
style = '-^', label='Rco', color='orange')
y += self.plot_E_aslip(site, cmpt, color='green')
plt.grid('on')
self.plot_post_disp_pred_added(site, cmpt, label=added_label)
plt.legend(loc=loc, prop={'size':leg_fs})
plt.ylabel(r'm')
plt.gcf().autofmt_xdate()
plt.title('Postseismic Disp. : {site} - {cmpt}'.format(
site = get_site_true_name(site_id = site),
cmpt = cmpt
))
def plot_cumu_disp_decomposition(self, site, cmpt, loc=2, leg_fs=7,
if_ylim=False,
added_label = None,
):
self.plot_cumu_obs_linres(site, cmpt)
y = self.plot_cumu_disp_pred_from_result_file(site, cmpt, label='pred.')
y += self.plot_R_co(site, cmpt,
style='-^', label='Rco', color='orange')
y += self.plot_E_cumu_slip(site, cmpt, color='green')
plt.grid('on')
if if_ylim:
plt.ylim(calculate_lim(y))
self.plot_cumu_disp_pred_added(site, cmpt, label=added_label)
plt.ylabel(r'm')
plt.legend(loc=loc, prop={'size':leg_fs})
plt.gcf().autofmt_xdate()
plt.title('Cumulative Disp.: {site} - {cmpt}'.format(
site = get_site_true_name(site_id=site),
cmpt = cmpt
))
def plotNE(self, figNum, north, east, title='', units='', options=''):
plt.figure(figNum)
# plt.cla()
# plt.hold(True);
plt.grid(True)
if title:
self.title = title
if not units:
self.units = units
if self.preTitle:
fig = plt.gcf()
fig.canvas.set_window_title("%s" % (self.preTitle))
plt.title("%s" % (self.title))
plt.plot(east, north, options)
plt.xlabel('East (%s)' % (self.units))
plt.ylabel('North (%s)' % (self.units))
def pca_2component_scatter(data_df,
predictors,
legend,
sav_fig=0,
savefname=r'c:\data\temp\ans.png'):
"""
outlook of data set by decomposing data to only 2 pca components.
do: scaling --> either maxmin or stdscaler
"""
print 'PCA plotting'
data_df[predictors] = StandardScaler().fit_transform(data_df[predictors])
pca_components = ['PCA1',
'PCA2'] #make this exist then insert the fit transform
pca = PCA(n_components=2)
for n in pca_components:
data_df[n] = ''
data_df[pca_components] = pca.fit_transform(data_df[predictors])
sns.lmplot('PCA1',
'PCA2',
data=data_df,
fit_reg=False,
hue=legend,
scatter_kws={
"marker": "D",
"s": 100
})
fig = plt.gcf()
if sav_fig:
plt.savefig(savefname)
plt.show()
bulk = double_y(bulk)
## begin to plot
def axhspan_for_viscosity(ax):
deps = [0, -51, -220, -670, -2000]
cols = ['.95', '0.83', '0.7', '0.5']
alpha = .8
for dep1, dep2, col in zip(deps[0:], deps[1:], cols):
ax.axhspan(dep2, dep1, color=col, alpha=alpha)
gs = gridspec.GridSpec(1, 3, width_ratios=[1.5, 1.7, 1.1])
fig = plt.gcf()
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1], sharey=ax1)
ax3 = plt.subplot(gs[2], sharey=ax1)
# plot ax1 - Maxwell viscosity
axhspan_for_viscosity(ax1)
ax1.tick_params(axis='x', bottom='off', top='off', labelbottom='off')
ax1.text(0.2, -40, s='crust $H_e=?$', fontsize=12)
ax1.text(0.2, -170, s='asthenosphere \n$ \\eta = ?$', fontsize=12)
ax1.text(0.2,
-450,
s='uppermantle \n$ \\eta = 1 \\times 10^{20}$',
fontsize=12)
ax1.text(0.2,
from database import session, Datapoint
session.query(Datapoint).all()
def format_timestamp(timestamp):
real_timestamp = datetime.fromtimestamp(float(timestamp.timestamp))
now = datetime.now()
return real_timestamp.replace(year=now.year, day=now.day, month=now.month)
datapoints = [(datapoint.user.name, datapoint.timestamp) for datapoint in session.query(Datapoint)]
grouped_data = defaultdict(int)
for datapoint in session.query(Datapoint):
if datapoint.status == 2:
grouped_data[datapoint.timestamp] += 1
print('Here')
x = list(map(format_timestamp, grouped_data.keys()))
plt.plot(x, list(grouped_data.values()), 'go')
formatter = DateFormatter('%H:%M')
plt.gcf().axes[0].xaxis.set_major_formatter(formatter)
plt.show()
def mkplots(self):
# run to make plots of the resulting posteriors. Modified from marginal_plots.py
# from pymultinest. Produces basename+marg.pdf and basename+marge.png files
prefix = self.basename
parameters = json.load(file(prefix + 'params.json'))
n_params = len(parameters)
a = pymultinest.Analyzer(n_params=n_params,
outputfiles_basename=prefix)
s = a.get_stats()
p = pymultinest.PlotMarginal(a)
try:
values = a.get_equal_weighted_posterior()
except IOError as e:
print 'Unable to open: %s' % e
return
assert n_params == len(s['marginals'])
modes = s['modes']
dim2 = os.environ.get('D', '1' if n_params > 20 else '2') == '2'
nbins = 100 if n_params < 3 else 20
if dim2:
plt.figure(figsize=(5.1 * n_params, 5 * n_params))
for i in range(n_params):
plt.subplot(n_params, n_params, i + 1)
plt.xlabel(parameters[i])
m = s['marginals'][i]
plt.xlim(m['5sigma'])
oldax = plt.gca()
x, w, patches = oldax.hist(values[:, i],
bins=nbins,
edgecolor='grey',
color='grey',
histtype='stepfilled',
alpha=0.2)
oldax.set_ylim(0, x.max())
newax = plt.gcf().add_axes(oldax.get_position(),
sharex=oldax,
frameon=False)
p.plot_marginal(i, ls='-', color='blue', linewidth=3)
newax.set_ylim(0, 1)
ylim = newax.get_ylim()
y = ylim[0] + 0.05 * (ylim[1] - ylim[0])
center = m['median']
low1, high1 = m['1sigma']
print center, low1, high1
newax.errorbar(x=center,
y=y,
xerr=np.transpose(
[[center - low1, high1 - center]]),
color='blue',
linewidth=2,
marker='s')
oldax.set_yticks([])
#newax.set_yticks([])
newax.set_ylabel("Probability")
ylim = oldax.get_ylim()
newax.set_xlim(m['5sigma'])
oldax.set_xlim(m['5sigma'])
#plt.close()
for j in range(i):
plt.subplot(n_params, n_params, n_params * (j + 1) + i + 1)
p.plot_conditional(i, j, bins=20, cmap=plt.cm.gray_r)
for m in modes:
plt.errorbar(x=m['mean'][i],
y=m['mean'][j],
xerr=m['sigma'][i],
yerr=m['sigma'][j])
ax = plt.gca()
if j == i - 1:
plt.xlabel(parameters[i])
plt.ylabel(parameters[j])
[l.set_rotation(45) for l in ax.get_xticklabels()]
else:
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.xlim([
m['mean'][i] - 5 * m['sigma'][i],
m['mean'][i] + 5 * m['sigma'][i]
])
plt.ylim([
m['mean'][j] - 5 * m['sigma'][j],
m['mean'][j] + 5 * m['sigma'][j]
])
#plt.savefig('cond_%s_%s.pdf' % (params[i], params[j]), bbox_tight=True)
#plt.close()
plt.tight_layout()
plt.savefig(prefix + 'marg.pdf')
plt.savefig(prefix + 'marg.png')
plt.close()
else:
from matplotlib.backends.backend_pdf import PdfPages
print '1dimensional only. Set the D environment variable D=2 to force'
print '2d marginal plots.'
pp = PdfPages(prefix + 'marg1d.pdf')
for i in range(n_params):
plt.figure(figsize=(5, 5))
plt.xlabel(parameters[i])
m = s['marginals'][i]
plt.xlim(m['5sigma'])
oldax = plt.gca()
x, w, patches = oldax.hist(values[:, i],
bins=20,
edgecolor='grey',
color='grey',
histtype='stepfilled',
alpha=0.2)
oldax.set_ylim(0, x.max())
newax = plt.gcf().add_axes(oldax.get_position(),
sharex=oldax,
frameon=False)
p.plot_marginal(i, ls='-', color='blue', linewidth=3)
newax.set_ylim(0, 1)
ylim = newax.get_ylim()
y = ylim[0] + 0.05 * (ylim[1] - ylim[0])
center = m['median']
low1, high1 = m['1sigma']
print center, low1, high1
newax.errorbar(x=center,
y=y,
xerr=np.transpose(
[[center - low1, high1 - center]]),
color='blue',
linewidth=2,
marker='s')
oldax.set_yticks([])
newax.set_ylabel("Probability")
ylim = oldax.get_ylim()
newax.set_xlim(m['5sigma'])
oldax.set_xlim(m['5sigma'])
plt.savefig(pp, format='pdf', bbox_inches='tight')
plt.close()
pp.close()
def plot_plotly(self,
plotly_filename=None,
mpl_type=False,
xlim=None,
ylim=None,
title=None,
figsize=None,
xlabel=None,
ylabel=None,
fontsize=None,
show_legend=True,
grid=False):
"""
Plot data using plotly library in IPython
:param plotly_filename: name for resulting plot file on server (use unique name, else the same plot will be showen)
:type plotly_filename: None or str
:param bool mpl_type: use or not plotly converter from matplotlib (experimental parameter)
:param xlim: x-axis range
:param ylim: y-axis range
:type xlim: None or tuple(x_min, x_max)
:type ylim: None or tuple(y_min, y_max)
:param title: title
:type title: None or str
:param figsize: figure size
:type figsize: None or tuple(weight, height)
:param xlabel: x-axis name
:type xlabel: None or str
:param ylabel: y-axis name
:type ylabel: None or str
:param fontsize: font size
:type fontsize: None or int
:param bool show_legend: show or not labels for plots
:param bool grid: show grid or not
"""
import plotly.plotly as py
from plotly import graph_objs
from IPython.kernel import connect
plotly_filename = self.plotly_filename if plotly_filename is None else plotly_filename
connection_file_path = connect.get_connection_file()
connection_file = os.path.basename(connection_file_path)
if '-' in connection_file:
kernel_id = connection_file.split('-', 1)[1].split('.')[0]
else:
kernel_id = connection_file.split('.')[0]
PLOTLY_API_USER, PLOTLY_API_KEY, PLOTLY_USER = self._plotly_config()
save_name = '{user}_{id}:{name}'.format(user=PLOTLY_USER,
id=kernel_id,
name=plotly_filename)
py.sign_in(PLOTLY_API_USER, PLOTLY_API_KEY)
if mpl_type:
self.plot(new_plot=True,
xlim=xlim,
ylim=ylim,
title=title,
figsize=figsize,
xlabel=xlabel,
ylabel=ylabel,
fontsize=fontsize,
grid=grid)
mpl_fig = plt.gcf()
update = dict(layout=dict(showlegend=show_legend),
data=[dict(name=leg) for leg in mpl_fig.legends])
return py.iplot_mpl(mpl_fig,
width=self.figsize[0] * 60,
update=update,
height=self.figsize[1] * 60,
filename=save_name,
fileopt='overwrite')
xlabel = self.xlabel if xlabel is None else xlabel
ylabel = self.ylabel if ylabel is None else ylabel
title = self.title if title is None else title
figsize = self.figsize if figsize is None else figsize
fontsize = self.fontsize if fontsize is None else fontsize
layout = graph_objs.Layout(yaxis={
'title': ylabel,
'ticks': ''
},
xaxis={
'title': xlabel,
'ticks': ''
},
showlegend=show_legend,
title=title,
font=graph_objs.Font(
family='Courier New, monospace',
size=fontsize),
width=figsize[0] * self.PLOTLY_RESIZE,
height=figsize[1] * self.PLOTLY_RESIZE)
fig = self._plot_plotly(layout)
return py.iplot(fig,
width=figsize[0] * self.PLOTLY_RESIZE,
height=figsize[1] * self.PLOTLY_RESIZE,
filename=save_name)
from datetime import date
import numpy as np
from pylab import plt
import viscojapan as vj
site = 'J550'
cmpt = 'e'
tp = np.loadtxt('../../tsana/pre_fit/linres/{site}.{cmpt}.lres'.\
format(site=site, cmpt=cmpt))
days = tp[:,0]
yres = tp[:,2]
plt.plot_date(days + vj.adjust_mjd_for_plot_date, yres, 'x')
plt.grid('on')
plt.axvline(date(2011,3,11),color='r', ls='--')
plt.ylim([-1, 7])
plt.xlim((date(2010,1,1), date(2015,1,1)))
plt.gcf().autofmt_xdate()
plt.ylabel('m')
plt.title('%s - %s'%(site, cmpt))
plt.savefig('%s_%s.pdf'%(site, cmpt))
plt.show()
def multiple_subplots(attribute_list,
group,
dataset,
plot_type='hist',
row_category_split=0,
row_category_split_col='dummy',
sav_fig=0,
savefname=r'c:\data\temp\ans.png',
**kwargs):
""" Create multiple subplots within a figure ( currently box, hist) based on the attribute list. May purpose is to ensure y-axis auto scaling for each subplot and does not share y axis.
By default, it create n by column (default 3) number of subplot within a single figure. n is based on len(attribute_list)/column.
Args:
attribute_list (list): (row_category_split = 0)list of column names to plot. Each column name is put under one plot
(row_category_split = 1)list of sub category to plot. Each sub cat is put under one plot
group ('str'): category option within a plot. Only avaliable for box plot. where the x-axis is group.
dataset ('dataframe'): attribute_list items, group and row_category_split_col must be present in the dataset.
Kwargs:
plot_type(str): hist (histogram) or box (boxplot) option.
row_category_split: default 0. Applicable for boxplot. allow sub plot by splitting the plots based on target row (row_category_split_col)
sav_fig (bool): default 0, if sav_fg = 1, will save figure beside showing it, will use savefname as the save filename.
Additional parameters:
column_default (int): default 3. change the layout of the figure.
Normally for box plot each plot is based on each column group by "group" (if row_category_split = 0)
if row_category_split = 1
"""
if kwargs.has_key('column_default'):
column_default = float(kwargs['column_default'])
else:
column_default = 3.0
num_rows = int(math.ceil(len(attribute_list) / column_default))
gs = gridspec.GridSpec(num_rows, int(column_default))
row_index = 0
temp_col = 0
for n in attribute_list:
ax = plt.subplot(gs[row_index, temp_col])
if plot_type == 'hist':
dataset[n].hist(ax=ax) #may need to add title
ax.set_title(n)
if plot_type == 'box':
if not row_category_split:
dataset[[n, group]].boxplot(
by=group, sym='k.', vert=True, whis=1.5,
ax=ax) #symbol is as a result of seaborn with pylab issue
else:
dataset[dataset[row_category_split_col] == n].boxplot(
by=group, sym='k.', vert=True, whis=1.5,
ax=ax) #symbol is as a result of seaborn with pylab issue
ax.set_title(n)
temp_col = temp_col + 1
if temp_col == 3:
row_index = row_index + 1
temp_col = 0
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
plt.tight_layout()
if sav_fig:
plt.savefig(savefname)
plt.show()
def plot_plotly(self, plotly_filename=None, mpl_type=False, xlim=None, ylim=None, title=None, figsize=None,
xlabel=None, ylabel=None, fontsize=None, show_legend=True, grid=False):
"""
Plot data using plotly library in IPython
:param plotly_filename: name for resulting plot file on server (use unique name, else the same plot will be showen)
:type plotly_filename: None or str
:param bool mpl_type: use or not plotly converter from matplotlib (experimental parameter)
:param xlim: x-axis range
:param ylim: y-axis range
:type xlim: None or tuple(x_min, x_max)
:type ylim: None or tuple(y_min, y_max)
:param title: title
:type title: None or str
:param figsize: figure size
:type figsize: None or tuple(weight, height)
:param xlabel: x-axis name
:type xlabel: None or str
:param ylabel: y-axis name
:type ylabel: None or str
:param fontsize: font size
:type fontsize: None or int
:param bool show_legend: show or not labels for plots
:param bool grid: show grid or not
"""
import plotly.plotly as py
from plotly import graph_objs
from IPython.kernel import connect
plotly_filename = self.plotly_filename if plotly_filename is None else plotly_filename
connection_file_path = connect.get_connection_file()
connection_file = os.path.basename(connection_file_path)
if '-' in connection_file:
kernel_id = connection_file.split('-', 1)[1].split('.')[0]
else:
kernel_id = connection_file.split('.')[0]
PLOTLY_API_USER, PLOTLY_API_KEY, PLOTLY_USER = self._plotly_config()
save_name = '{user}_{id}:{name}'.format(user=PLOTLY_USER, id=kernel_id, name=plotly_filename)
py.sign_in(PLOTLY_API_USER, PLOTLY_API_KEY)
if mpl_type:
self.plot(new_plot=True, xlim=xlim, ylim=ylim, title=title, figsize=figsize, xlabel=xlabel, ylabel=ylabel,
fontsize=fontsize, grid=grid)
mpl_fig = plt.gcf()
update = dict(
layout=dict(
showlegend=show_legend
),
data=[dict(name=leg) for leg in mpl_fig.legends]
)
return py.iplot_mpl(mpl_fig, width=self.figsize[0] * 60,
update=update,
height=self.figsize[1] * 60,
filename=save_name,
fileopt='overwrite')
xlabel = self.xlabel if xlabel is None else xlabel
ylabel = self.ylabel if ylabel is None else ylabel
title = self.title if title is None else title
figsize = self.figsize if figsize is None else figsize
fontsize = self.fontsize if fontsize is None else fontsize
layout = graph_objs.Layout(yaxis={'title': ylabel, 'ticks': ''}, xaxis={'title': xlabel, 'ticks': ''},
showlegend=show_legend, title=title,
font=graph_objs.Font(family='Courier New, monospace', size=fontsize),
width=figsize[0] * self.PLOTLY_RESIZE,
height=figsize[1] * self.PLOTLY_RESIZE
)
fig = self._plot_plotly(layout)
return py.iplot(fig, width=figsize[0] * self.PLOTLY_RESIZE, height=figsize[1] * self.PLOTLY_RESIZE,
filename=save_name)
def mkplots(self):
# run to make plots of the resulting posteriors. Modified from marginal_plots.py
# from pymultinest. Produces basename+marg.pdf and basename+marge.png files
prefix = self.basename
parameters = json.load(file(prefix + 'params.json'))
n_params = len(parameters)
a = pymultinest.Analyzer(n_params = n_params, outputfiles_basename = prefix)
s = a.get_stats()
p = pymultinest.PlotMarginal(a)
try:
values = a.get_equal_weighted_posterior()
except IOError as e:
print 'Unable to open: %s' % e
return
assert n_params == len(s['marginals'])
modes = s['modes']
dim2 = os.environ.get('D', '1' if n_params > 20 else '2') == '2'
nbins = 100 if n_params < 3 else 20
if dim2:
plt.figure(figsize=(5.1*n_params, 5*n_params))
for i in range(n_params):
plt.subplot(n_params, n_params, i + 1)
plt.xlabel(parameters[i])
m = s['marginals'][i]
plt.xlim(m['5sigma'])
oldax = plt.gca()
x,w,patches = oldax.hist(values[:,i], bins=nbins, edgecolor='grey', color='grey', histtype='stepfilled', alpha=0.2)
oldax.set_ylim(0, x.max())
newax = plt.gcf().add_axes(oldax.get_position(), sharex=oldax, frameon=False)
p.plot_marginal(i, ls='-', color='blue', linewidth=3)
newax.set_ylim(0, 1)
ylim = newax.get_ylim()
y = ylim[0] + 0.05*(ylim[1] - ylim[0])
center = m['median']
low1, high1 = m['1sigma']
print center, low1, high1
newax.errorbar(x=center, y=y,
xerr=np.transpose([[center - low1, high1 - center]]),
color='blue', linewidth=2, marker='s')
oldax.set_yticks([])
#newax.set_yticks([])
newax.set_ylabel("Probability")
ylim = oldax.get_ylim()
newax.set_xlim(m['5sigma'])
oldax.set_xlim(m['5sigma'])
#plt.close()
for j in range(i):
plt.subplot(n_params, n_params, n_params * (j + 1) + i + 1)
p.plot_conditional(i, j, bins=20, cmap = plt.cm.gray_r)
for m in modes:
plt.errorbar(x=m['mean'][i], y=m['mean'][j], xerr=m['sigma'][i], yerr=m['sigma'][j])
ax = plt.gca()
if j == i-1:
plt.xlabel(parameters[i])
plt.ylabel(parameters[j])
[l.set_rotation(45) for l in ax.get_xticklabels()]
else:
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.xlim([m['mean'][i]-5*m['sigma'][i],m['mean'][i]+5*m['sigma'][i]])
plt.ylim([m['mean'][j]-5*m['sigma'][j],m['mean'][j]+5*m['sigma'][j]])
#plt.savefig('cond_%s_%s.pdf' % (params[i], params[j]), bbox_tight=True)
#plt.close()
plt.tight_layout()
plt.savefig(prefix + 'marg.pdf')
plt.savefig(prefix + 'marg.png')
plt.close()
else:
from matplotlib.backends.backend_pdf import PdfPages
print '1dimensional only. Set the D environment variable D=2 to force'
print '2d marginal plots.'
pp = PdfPages(prefix + 'marg1d.pdf')
for i in range(n_params):
plt.figure(figsize=(5, 5))
plt.xlabel(parameters[i])
m = s['marginals'][i]
plt.xlim(m['5sigma'])
oldax = plt.gca()
x,w,patches = oldax.hist(values[:,i], bins=20, edgecolor='grey', color='grey', histtype='stepfilled', alpha=0.2)
oldax.set_ylim(0, x.max())
newax = plt.gcf().add_axes(oldax.get_position(), sharex=oldax, frameon=False)
p.plot_marginal(i, ls='-', color='blue', linewidth=3)
newax.set_ylim(0, 1)
ylim = newax.get_ylim()
y = ylim[0] + 0.05*(ylim[1] - ylim[0])
center = m['median']
low1, high1 = m['1sigma']
print center, low1, high1
newax.errorbar(x=center, y=y,
xerr=np.transpose([[center - low1, high1 - center]]),
color='blue', linewidth=2, marker='s')
oldax.set_yticks([])
newax.set_ylabel("Probability")
ylim = oldax.get_ylim()
newax.set_xlim(m['5sigma'])
oldax.set_xlim(m['5sigma'])
plt.savefig(pp, format='pdf', bbox_inches='tight')
plt.close()
pp.close()
def main(filename, ylim = (0,15), xlim = (-40,20)):
# construct experiment from header
import spk2_mp
conds, stims, mps, cells = spk2_mp.main(filename)
conds = on_off_evnts(conds['name'], conds['times'])
stims = on_off_evnts(stims['name'], stims['times'])
prtrct = on_off_evnts('prtrct', mps['prtrct'])
retrct = on_off_evnts('retrct', mps['retrct'])
mps = motor_programs(prtrct, retrct)
expt = experiment()
expt.add_motor_programs(mps)
expt.add_cbi2_stims(stims)
expt.add_condition(conds)
for cell_name in cells.keys():
tmpcell = ap_cell(cell_name)
tmpcell.add_spk_times(cells[cell_name])
expt.add_cell(tmpcell)
# now getting into plotting and the like, have to split this off somehow.
# figure out max num prgs in any one condition
max_prgs = max([cnd._num_prgs for cnd in expt])
import matplotlib.gridspec as gridspec
nrow = max_prgs
ncol = 3
plt_counter = 0
gs = gridspec.GridSpec(nrow,ncol)
# plot b48
# save the bottom row for the averages (row-1)
from pylab import plt
for col_num, cond in enumerate(expt):
if col_num == 0: left_most = True
else: left_most = False
for row_num, prog in enumerate(cond):
if row_num == (cond._num_prgs - 1): bottom_most = True
else: bottom_most = False
ax = plt.subplot(gs[row_num,col_num])
quick_plot(expt, prog, 'b48', ax,
left_most = left_most, bottom_most = bottom_most,
ylim = ylim, xlim = xlim)
if row_num == nrow - 1:
break
expt.b48_fig = plt.gcf()
expt.b48_fig.set_size_inches((7.5,10))
# plot both b48 and b8
# save the bottom row for the averages (row-1)
for col_num, cond in enumerate(expt):
if col_num == 0: left_most = True
else: left_most = False
for row_num, prog in enumerate(cond):
if row_num == (cond._num_prgs - 1): bottom_most = True
else: bottom_most = False
ax = plt.subplot(gs[row_num,col_num])
quick_plot(expt, prog, 'b48', ax,
left_most = left_most, bottom_most = bottom_most,
ylim = ylim, xlim = xlim)
quick_plot(expt, prog, 'b8', ax,
left_most = left_most, bottom_most = bottom_most,
ylim = ylim, xlim = xlim)
# if row_num == nrow:
# break
expt.b8_b48_fig = plt.gcf()
expt.b8_b48_fig.set_size_inches((7.5,10))
return (expt)
bulk = double_y(bulk)
## begin to plot
def axhspan_for_viscosity(ax):
deps = [0,-51,-220,-670,-2000]
cols = ['.95', '0.83', '0.7', '0.5']
alpha = .8
for dep1, dep2, col in zip(deps[0:], deps[1:], cols):
ax.axhspan(dep2, dep1, color=col, alpha=alpha)
gs = gridspec.GridSpec(1, 3,
width_ratios=[1.5,1.7,1.1]
)
fig = plt.gcf()
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1], sharey=ax1)
ax3 = plt.subplot(gs[2], sharey=ax1)
# plot ax1 - Maxwell viscosity
axhspan_for_viscosity(ax1)
ax1.tick_params(axis='x', bottom='off', top='off', labelbottom='off')
ax1.text(0.2, -40,
s='crust $H_e=?$',
fontsize=12)
ax1.text(0.2, -170,
s='asthenosphere \n$ \\eta = ?$',
fontsize=12)