def plot_trend_season(dates, ndf_domain, x, x_trend, season, my_domain):
# ---------------------- Prepare Data Frame ----------------------- #
df_domain = pd.DataFrame(ndf_domain, columns=['Date', 'Volume'])
df_domain['Date'] = dates
x_lbl = ['Observed Volume' for i in xrange(len(x))]
xt_lbl = ['Overall Trend' for i in xrange(len(x_trend))]
xs_lbl = ['Repeat Sending Trend' for i in xrange(len(season))]
col3 = pd.DataFrame(x_lbl+xt_lbl+xs_lbl)
df_plot = pd.concat( (df_domain, col3), axis=1)
df_plot.columns = ['Date', 'Volume', 'Data']
# ---------------------- Plot Decomposition ----------------------- #
p = ggplot.ggplot(aes(x='Date', y='Volume', color='Data'), data=df_plot) + \
ggplot.geom_line(color='blue', size=2) + \
ggplot.scale_x_date(labels = date_format("%Y-%m-%d"), breaks="1 week") + \
ggplot.xlab("Week (Marked on Mondays)") + \
ggplot.ylab("Message Vol") + \
ggplot.ggtitle("%s Message Volume by Week" % my_domain) + \
ggplot.facet_grid('Data', scales='free_y') + \
ggplot.theme_seaborn()
return p
def plot_matches(df_in,
date,
filename_out,
x_var='date_time',
y_var="shorthand_search_vol"):
"""
Plot y-var and save based on specified variables.
Assumes that df has already been filtered using dplyr's sift mechanism.
Also assumes that a date has been passed in.
"""
# basic data processing for viz
df_in['date_time'] = date + " " + df_in['time'].astype(str)
df_in['date_time'] = pd.to_datetime(df_in['date_time'],
errors="coerce",
infer_datetime_format=True)
# build layers for plot
p = ggplot(aes(x=x_var, y=y_var, group="match_id", color="match_id"),
data=df_in)
p += geom_line(size=2)
# informative
p += labs(x="time (gmt)", y="search volume (scaled to 100)")
# p += ggtitle("man. city (h) vs. chelsea (a)\naug. 8 '16, etihad stadium")
p += scale_x_date(labels=date_format("%H:%M:%S"), date_breaks="30 minutes")
# visual
t = theme_gray()
t._rcParams['font.size'] = 8
t._rcParams['font.family'] = 'monospace'
p += t
# done
p.save(filename_out, width=16, height=8)
def main(file_path):
# Validate raw data path
if not os.path.exists(file_path):
LOG_ERROR('Could not find file: {}'.format(file_path))
return
# Validate raw data file type
if not file_path.endswith('.pkl'):
LOG_ERROR('File path must be a pickle file')
return
with open(file_path, 'rb') as f:
LOG_INFO('Parsing pickle file: {}'.format(file_path))
conversation = pickle.load(f)
LOG_INFO('Found conversation: {}'.format(conversation['conversation_name']))
df = pd.DataFrame(conversation['messages'])
df.columns = ['Timestamp', 'Type', 'Participant']
# df['Datetime'] = pd.to_datetime(df['Timestamp'])
df['Datetime'] = df['Timestamp'].apply(lambda x:
datetime.datetime.fromtimestamp(float(x)).toordinal())
histogram = ggplot.ggplot(df, ggplot.aes(x='Datetime', fill='Participant')) \
+ ggplot.geom_histogram(alpha=0.6, binwidth=2) \
+ ggplot.scale_x_date(labels='%b %Y') \
+ ggplot.ggtitle(conversation['conversation_name']) \
+ ggplot.ylab('Number of messages') \
+ ggplot.xlab('Date')
print(histogram)
def plot_update_frequency(result):
import pandas as pd
import numpy
#turns query results into timeseries of chnages
d = []
v = []
for res in result:
d.append(pd.Timestamp(res['_id']['timestamp']).to_datetime())
v.append(res['count'])
ts = pd.DataFrame(v, index = d, columns = ['changes'])
ts = ts.resample('W', how='sum')
ts.index.names = ['date']
import ggplot
#plots timeseries of changes
p = ggplot.ggplot(ts, ggplot.aes(x = ts.index, y=ts['changes'])) +\
ggplot.geom_point(color = 'blue') +\
ggplot.xlab('Period') +\
ggplot.ylab('Changes') +\
ggplot.geom_smooth() +\
ggplot.ylim(low = 0) +\
ggplot.scale_x_date(breaks = ggplot.date_breaks("12 months"), labels = ggplot.date_format('%Y-%m')) +\
ggplot.ggtitle('OpenStreetMaps Denver-Boulder\nChanges per Week')
return p
def render(data, bin_width, plot_density=False):
if plot_density:
plot = ggplot.ggplot(data, ggplot.aes(x='datetime', color='conversationWithName')) \
+ ggplot.geom_density() \
+ ggplot.scale_x_date(labels='%b %Y') \
+ ggplot.ggtitle('Conversation Densities') \
+ ggplot.ylab('Density') \
+ ggplot.xlab('Date')
else:
plot = ggplot.ggplot(data, ggplot.aes(x='datetime', fill='conversationWithName')) \
+ ggplot.geom_histogram(alpha=0.6, binwidth=bin_width) \
+ ggplot.scale_x_date(labels='%b %Y', breaks='6 months') \
+ ggplot.ggtitle('Message Breakdown') \
+ ggplot.ylab('Number of Messages') \
+ ggplot.xlab('Date')
print(plot)
def data_output(data, chart_title):
print "Good News! You're data has been returned. I'm happy to show it to you."
print "Just tell me how you want it - Table or Line Graph?"
data_output = raw_input("Choose table or line > ")
if data_output[0].lower() == "t":
print "Ok, here's your data."
print data
elif data_output[0] == "l" or data_output[0].lower() =="g":
import ggplot as gg
plot = gg.ggplot(gg.aes(x='Month, Year', y='Value'), data=data) + \
gg.geom_point(color='black') + \
gg.geom_line(color='green') + \
gg.ggtitle(chart_title) + \
gg.xlab("Month, Year") + \
gg.ylab("Value")
gg.scale_x_date(breaks = gg.date_breaks('1 month'), labels= gg.date_format("%B"))
print (plot + gg.theme_xkcd())
def plot_predictions(date_times, actual_values, predictions, match_id,
feature_set_in, filename):
"""
Plot y-var and save based on specified variables.
Assumes that df has already been filtered using dplyr's sift mechanism.
Also assumes that a date has been passed in.
"""
actual_df = pd.DataFrame()
actual_df['date_time'] = pd.to_datetime(date_times,
errors="coerce",
infer_datetime_format=True)
actual_df['search_vol'] = actual_values
actual_df['match_id'] = "actual" + match_id
predict_df = pd.DataFrame()
predict_df['date_time'] = pd.to_datetime(date_times,
errors="coerce",
infer_datetime_format=True)
predict_df['search_vol'] = list(predictions)
predict_df['match_id'] = "predictedby_" + str(feature_set_in) + match_id
plotting_df = pd.concat([actual_df, predict_df], axis=0, ignore_index=True)
# build layers for plot
p = ggplot(aes(x='date_time',
y='search_vol',
group="match_id",
color="match_id"),
data=plotting_df)
p += geom_line(size=2)
# informative
p += labs(x="time (gmt)", y="search volume (scaled to 100)")
# p += ggtitle("man. city (h) vs. chelsea (a)\naug. 8 '16, etihad stadium")
p += scale_x_date(labels=date_format("%H:%M:%S"), date_breaks="30 minutes")
# visual
t = theme_gray()
t._rcParams['font.size'] = 8
t._rcParams['font.family'] = 'monospace'
p += t
# done
p.save(filename, width=16, height=8)
print("#######################################")
print("打印所挖掘的文本文件 text-movie.xls 前几行")
print(df.head())
#text = df.comments.iloc[0] 单个影评情感分析实验， iloc中的index值表示第几个应用，编号从0开始
#s = SnowNLP(text)
#
#print(s.sentiments)
def get_sentiment_cn(text):
s = SnowNLP(text)
return s.sentiments
df["sentiment"] = df.comments.apply(get_sentiment_cn)
print("#######################################")
print("打印所挖掘的文本文件 text-movie.xls 部分影评及其情感分析值")
print(df)
print("#######################################")
print("重要信息")
print("所有影评的平均值为:", df.sentiment.mean())
print("所有影评的中位数为:", df.sentiment.median())
ggplot.ggplot(ggplot.aes(x="date", y="sentiment"),
data=df) + ggplot.geom_point() + ggplot.geom_line(
color='blue') + ggplot.scale_x_date(
labels=ggplot.date_format("%Y-%m-%d"))
df.sort_values(['sentiment'])[:5]
def render_png(self, buffer):
"""
Render timeseries plots as PNG images.
"""
bucket = self.bucket
import matplotlib.font_manager
matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')
import matplotlib
try:
matplotlib.use('agg')
except:
pass
import matplotlib.pyplot as plt
df = self.dataframe
#df = df.set_index(['time'])
# Compute datetime range boundaries
datetime_min = min(df.time)
datetime_max = max(df.time)
datetime_delta = datetime_max - datetime_min
#xmin = pd.to_datetime('2016-05-01')
#xmax = pd.to_datetime('2016-08-01')
renderer = bucket.tdata.get('renderer', 'matplotlib')
if renderer == 'matplotlib':
# Bring DataFrame into appropriate format
df = dataframe_index_and_sort(df, 'time')
# Propagate non-null values forward or backward, otherwise
# matplotlib would not plot the sparse data frame properly.
# With time series data, using pad/ffill is extremely common so that the “last known value” is available at every time point.
# http://pandas.pydata.org/pandas-docs/stable/missing_data.html#filling-missing-values-fillna
df.fillna(method='pad', inplace=True)
# Make plots of DataFrame using matplotlib / pylab.
# http://matplotlib.org/
# http://pandas.pydata.org/pandas-docs/version/0.13.1/visualization.html
# http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html
# https://markthegraph.blogspot.de/2015/05/plotting-time-series-dataframes-in.html
if 'style' in bucket.tdata and bucket.tdata.style:
try:
plt.style.use(bucket.tdata.style)
except Exception:
error_message = u'# Unknown style "{style_name}", available styles: {available}'.format(
style_name=bucket.tdata.style,
available=plt.style.available)
log.error(error_message)
return self.request.error_response(bucket, error_message)
# Basic plotting
#df.plot()
#plt.savefig(buffer)
# Advanced plotting
ax = df.plot()
fig = ax.get_figure()
# Figure heading
title = fig.suptitle(bucket.title.human, fontsize=12)
#fig.tight_layout(pad=1.5)
# Axis and tick labels
ax.set_xlabel('Time')
ax.set_ylabel('Value')
ax.tick_params(axis='x', labelsize='smaller')
# Grid and legend
# http://matplotlib.org/users/legend_guide.html
# http://matplotlib.org/examples/pylab_examples/legend_demo3.html
ax.grid(True)
legend_params = dict(ncol=1,
loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize='small',
shadow=True,
fancybox=True)
legend = ax.legend(**legend_params) # title='Origin'
#ax.legend(**legend_params) # title='Origin'
# Sort list of legend labels
# http://stackoverflow.com/questions/22263807/how-is-order-of-items-in-matplotlib-legend-determined/27512450#27512450
# Axis formatting
#ax.xaxis_date()
#ax.autoscale_view()
# Compute appropriate locator and formatter
locator, formatter = matplotlib_locator_formatter(datetime_delta,
span=1)
#ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
# Figure formatting
fig.autofmt_xdate()
# http://stackoverflow.com/questions/10101700/moving-matplotlib-legend-outside-of-the-axis-makes-it-cutoff-by-the-figure-box/10154763#10154763
fig.savefig(buffer,
bbox_extra_artists=(title, legend),
bbox_inches='tight')
# TODO: Add annotations
"""
# https://stackoverflow.com/questions/11067368/annotate-time-series-plot-in-matplotlib
# https://stackoverflow.com/questions/17891493/annotating-points-from-a-pandas-dataframe-in-matplotlib-plot
import matplotlib.dates as mdates
fig = plot.draw()
ax = fig.axes[0]
ax.annotate('Test', (mdates.date2num(x[1]), y[1]), xytext=(15, 15),
textcoords='offset points', arrowprops=dict(arrowstyle='-|>'))
"""
elif renderer == 'ggplot':
# https://yhat.github.io/ggplot/notebook.html?page=build/docs/examples/Multiple%20Line%20Plot.html
# https://stackoverflow.com/questions/23541497/is-there-a-way-to-plot-a-pandas-series-in-ggplot
# https://stackoverflow.com/questions/24478925/is-it-possible-to-plot-multiline-chart-on-python-ggplot/24479513#24479513
# https://github.com/yhat/ggplot/blob/master/docs/how-to/Building%20Faceted%20(or%20Trellised)%20Plots.ipynb
# https://github.com/yhat/ggplot/blob/master/docs/how-to/Annotating%20Plots%20-%20Titles%20and%20Labels.ipynb
# https://github.com/yhat/ggplot/blob/master/docs/how-to/How%20to%20make%20xkcd%20style%20graphs.ipynb
from ggplot import ggplot, aes, qplot, geom_line, geom_text, ggtitle, stat_smooth, scale_x_date, date_format, date_breaks
from ggplot import theme_538, theme_bw, theme_gray, theme_xkcd
# https://stackoverflow.com/questions/24478925/is-it-possible-to-plot-multiline-chart-on-python-ggplot/24479513#24479513
# https://stackoverflow.com/questions/23541497/is-there-a-way-to-plot-a-pandas-series-in-ggplot
# Convert DataFrame from wide to long format, retaining "time" as visible column
df = dataframe_wide_to_long_indexed(df, 'time')
dataframe_index_to_column(df, 'time')
# Compute appropriate locator and formatter
locator, formatter = matplotlib_locator_formatter(datetime_delta,
span=2)
plot = ggplot(df, aes(x='time', y='value', color='variable'))\
+ geom_line()\
+ scale_x_date(limits=(datetime_min, datetime_max), breaks=locator, labels=formatter)\
+ ggtitle(bucket.title.human)
# Axis labels
plot.xlab = 'Time'
plot.ylab = 'Value'
# Labs
#+ stat_smooth(colour='blue', span=0.2) \
#+ geom_text(aes(x='x', y='y'), label='hello world')
#+ scale_x_date(limits=(xmin, xmax), breaks=date_breaks('1 hour'), labels=date_format('%Y-%m-%d\n%H:%M'))
theme_name = bucket.tdata.get('theme')
# TODO: Switching themes will leak some matplotlib/pyplot properties, postpone to future versions
if theme_name:
if isinstance(theme_name, float):
theme_name = str(int(theme_name))
try:
theme = eval('theme_' + theme_name)
plot += theme()
except Exception:
error_message = u'# Unknown theme "{theme_name}"'.format(
theme_name=theme_name)
log.error(error_message)
return self.request.error_response(bucket, error_message)
plot.save(buffer)
# Attempt to reset global matplotlib parameters to get rid of xkcd theme style
"""
import matplotlib as mpl
#mpl.rcParams = mpl.rc_params()
#del mpl.rcParams['path.sketch']
#del mpl.rcParams['path.effects']
#mpl.rcParams = mpl.defaultParams.copy()
#mpl.rcParams.clear()
#mpl.rcdefaults()
#mpl.rcParams = mpl.rcParamsOrig
if 'axes.prop_cycle' in mpl.rcParams:
del mpl.rcParams['axes.prop_cycle']
mpl.rcParams.update({'path.sketch': None, 'path.effects': []})
mpl.rcParams.update(mpl.rc_params())
"""
elif renderer == 'seaborn':
# TODO: We don't do statistical plotting yet.
# https://stanford.edu/~mwaskom/software/seaborn/examples/timeseries_from_dataframe.html
# https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.tsplot.html
import seaborn as sns
sns.set(style="darkgrid")
#sns.tsplot(data=gammas, time="timepoint", unit="subject", condition="ROI", value="BOLD signal")
#print dir(df)
#df['time'] = pandas.to_datetime(df['time'])
#df = df.set_index(df.time)
pprint(df)
sns.tsplot(data=df, time="time")
#sns.tsplot(data=df)
plt.savefig(buffer)
else:
error_message = u'# Unknown renderer "{renderer_name}"'.format(
renderer_name=renderer)
log.error(error_message)
return self.request.error_response(bucket, error_message)
def plot_vol(dates, x, cp, my_domain):
# -------------------- Prepare for Plotting -------------------------- #
# Prepare DataFrame objects for graphing
#Add a column for the label to show in the legend in the graph
#Need to reshape it, from (124,) to (124,1) for exmple, so that it
#will concatenate. This gives a df with [date, vol_data, 'Volume']
v = ['Volume' for i in xrange(x.shape[0])]
#df_domain = np.concatenate((x, v), axis=1)
ndf_vol = np.transpose(np.array([dates, x, v]))
df_vol = pd.DataFrame(ndf_vol, columns=['Date', 'Volume', 'Data'])
#Create pre-allocated lists for plotting means and cp
xmin_list = [0 for i in xrange(len(cp))] #hold lft pt of vol_mean
xmax_list = [0 for i in xrange(len(cp))] #hold rt pt of vol_mean
yint_list = [0 for i in xrange(len(cp))] #holds vol_means
cp_date_list = [0 for i in xrange(len(cp))] #holds date for cp
cp_value_list = [0 for i in xrange(len(cp))] #holds cp value
ref_idx = 0 #used to keep track of vol_means
#collect list data for plotting
for i in xrange(len(cp)):
cp_idx = cp[i][0] - 1 #-1 b/c 1-indexed (includes cp itself)
xmin_list[i] = dates[ref_idx].toordinal() #convert to match ggplot
xmax_list[i] = dates[cp_idx].toordinal() #convert to match ggplot
yint_list[i] = cp[i][2] #use value from_mean for vol_mean
cp_date_list[i] = dates[cp_idx] #date of cp
#cp_value_list[i] = x[cp_idx] #value of cp
cp_value_list[i] = cp[i][2]
ref_idx = cp_idx + 1 #+1 b/c moving to next point
#Reform lists into a data frame and attach to df_domains. The first two
#lists can be created together since they are both numeric, but if I try
#to create all three together all types will be downgraded to strings.
#np.concatenate avoids this conversion. The transpose is needed to take
#an item from each to form a single row.
cp_lbl = ['Change Point' for i in xrange(len(yint_list))]
#Need to create a dummy entry to put 'Volume Mean' into legend
cp_date_list.append(dates[0])
yint_list.append(x[0])
cp_lbl.append('Volume Mean')
ndf_cp = np.transpose(np.array([cp_date_list, yint_list, cp_lbl]))
yint_list.pop(-1)
cp_date_list.pop(-1)
df_cp = pd.DataFrame(ndf_cp, columns=['Date', 'Volume', 'Data'])
df_plot = pd.concat((df_vol, df_cp), axis=0)
#Need to create a dummy entry to put 'Volume Mean' into legend
#dummy = np.array([dates[0], x[0], 'Volume Mean']).reshape(1,-1)
#df_cp = np.concatenate( (df_cp, dummy), axis=0) #add to bottom df_cp
#df_domain = np.concatenate( (df_domain, df_cp), axis=0 ) #add df_domains
#convert final array into a pd.DataFrame for printing and plotting
#df_domain = pd.DataFrame(df_domain, columns=['Date','Volume','Data'])
#df_domain.to_html(open('out.html','w'))
#os.system('sudo cp out.html /usr/local/www/analytics/rwing')
margin = 0.10 * (np.max(x) - np.min(x))
p = ggplot.ggplot(aes(x='Date', y='Volume', color='Data'), data=df_plot) + \
ggplot.geom_line(color='blue',size=2) + \
ggplot.geom_point(x=xmax_list, y=cp_value_list, color='black', \
shape='D', size=50) + \
ggplot.geom_hline(xmin=xmin_list, \
xmax=xmax_list, \
yintercept=yint_list, color="red", size=3) + \
ggplot.scale_x_date(labels = date_format("%Y-%m-%d"), breaks="1 week") + \
ggplot.scale_colour_manual(values = ["black", "blue", "red"]) + \
ggplot.scale_y_continuous(labels='comma') + \
ggplot.ylim(low=np.min(x)-margin/4.0, high=np.max(x)+margin) + \
ggplot.xlab("Week (Marked on Mondays)") + \
ggplot.ylab("Message Vol") + \
ggplot.ggtitle("%s\nMessage Volume by Week" % my_domain) + \
ggplot.theme_seaborn()
return p
