def draw_timeplan(rule, labels=None, blank_color=0.9):
duration = np.shape(rule)[1]
time_labels = [label +1 for label in range(duration)]
cMap = add_grey(cm.viridis, blank_color)
fig, ax = plt.subplots(figsize=np.shape(rule)[::-1] , facecolor='#eeeeee', tight_layout=True)
im = ax.pcolorfast(rule, cmap=cMap)
#place and null major ticks (we still need them for the grid)
ax.xaxis.set_major_locator(ticker.LinearLocator(duration+1))
ax.xaxis.set_major_formatter(ticker.NullFormatter())
#place and format minor ticks (used to substitute for centered major tick labels)
ax.xaxis.set_minor_locator(ticker.IndexLocator(1,0.5))
ax.xaxis.set_minor_formatter(ticker.FixedFormatter(time_labels))
#remove all major tick lines
plt.setp(ax.get_xticklines(),visible=False)
#remove bottom and top small tick lines
# for tick in ax.xaxis.get_minor_ticks():
# tick.tick1line.set_markersize(0)
# tick.tick2line.set_markersize(0)
plt.tick_params(axis='x', which='both', bottom='off', top='off')
#Set only 7th minor tick label to visible
ax.set_yticklabels(labels)
for label in ax.xaxis.get_minorticklabels():
label.set_visible(False)
for label in ax.xaxis.get_minorticklabels()[::7]:
label.set_visible(True)
#Set yticks to labels and add 0.5 cell height offset
ax.yaxis.set(ticks=np.arange(0.5, len(labels)))
#Set yticks to point outside the figure
ax.get_yaxis().set_tick_params(direction='out')
#Rotate ytick labels
plt.setp(ax.yaxis.get_majorticklabels(), rotation=30)
#Constrain yticks to left side of figure
ax.yaxis.set_ticks_position('left')
# create grid
ax.xaxis.grid(True, which='major', color='1', linestyle='-')
#delete all spines
for spine in ["right", "left", "top", "bottom"]:
ax.spines[spine].set_visible(False)
# recolour yticks in blank image color
[t.set_color(str(blank_color)) for t in ax.yaxis.get_ticklines()]
def multi_plot(reference_df, x_key, shade, saturate, padding=4, saturate_cmap="Pastel1_r", window_start="", window_end=""):
"""Plotting tool
Mandatory Arguments:
db_path -- path of db to query for data (can use tilde for home dir)
category -- main category to plot on x axis (must be a class from the db)
select -- list of lists which must be either 2 (for join) or 3 (for filter) elements long
padding -- number of entries to padd timeplan with
saturate_cmap -- string indicating the matplotlib cmap to use (http://matplotlib.org/examples/color/colormaps_reference.html)
"""
#truncate dates
for col in reference_df.columns:
if "date" in col:
#the following catches None entries:
try:
reference_df[col] = reference_df[col].apply(lambda x: x.date())
except AttributeError:
pass
#GET FIRST AND LAST DATE
dates = get_dates(reference_df, [shade, saturate])
if not window_start:
window_start = min(dates) - timedelta(days=padding)
else:
window_start = datetime.strptime(window_start, "%Y,%m,%d").date()
if not window_end:
window_end = max(dates) + timedelta(days=padding)
else:
window_end = datetime.strptime(window_end, "%Y,%m,%d").date()
print window_end
#create generic plotting dataframe
x_vals = list(set(reference_df[x_key]))
datetime_index = [i for i in perdelta(window_start,window_end,timedelta(days=1))]
df = pd.DataFrame(index=datetime_index, columns=x_vals)
df = df.fillna(0)
#set plotting params
cMap = add_grey(cm.viridis, 0.9)
fig_shape = (df.shape[0],df.shape[1]/1.5) #1.5 seems like a good scaling value to make cells not-too-tall and not-too-short
fig, ax = plt.subplots(figsize=fig_shape , facecolor='#eeeeee', tight_layout=True)
#populate frames
df_ = df.copy(deep=True)
for c_step, entry in enumerate(shade):
for x_val in x_vals:
if isinstance(entry, dict):
for key in entry:
start=False #unless the code below is succesful, no attempt is made to add an entry for the x_val
filtered_df = reference_df[(reference_df[key] == entry[key][0])&(reference_df[x_key] == x_val)]
try:
start = list(set(filtered_df[entry[key][1]]))[0]
except IndexError:
pass
if len(entry[key]) == 3:
end = list(set(filtered_df[entry[key][2]]))[0]
active_dates = [i for i in perdelta(start,end+timedelta(days=1),timedelta(days=1))]
for active_date in active_dates:
df_.set_value(active_date, x_val, df_.get_value(active_date, x_val)+c_step+1)
elif start:
df_.set_value(start, x_val, df_.get_value(start, x_val)+c_step+1)
elif isinstance(entry, str):
filtered_df = reference_df[reference_df[x_key] == x_val]
active_dates = list(set(filtered_df[entry]))
for active_date in active_dates:
#escaping dates which are outside the date range (e.g. when specifying tighter window_end and window_start contraints)
try:
df_.set_value(active_date, x_val, df_.get_value(active_date, x_val)+c_step+1)
except KeyError:
pass
im = ax.pcolorfast(df_.T, cmap=add_grey(cm.gray_r, 0.8), alpha=.5)
plt.hold(True)
#populate frames
df_ = df.copy(deep=True)
for c_step, entry in enumerate(saturate):
for x_val in x_vals:
if isinstance(entry, dict):
for key in entry:
filtered_df = reference_df[(reference_df[key] == entry[key][0])&(reference_df[x_key] == x_val)]
try:
start = list(set(filtered_df[entry[key][1]]))[0]
try:
start = start.date()
except AttributeError:
pass
except IndexError:
pass
if len(entry[key]) == 3:
try:
end = list(set(filtered_df[entry[key][2]]))[0]
active_dates = [i for i in perdelta(start,end+timedelta(days=1),timedelta(days=1))]
for active_date in active_dates:
df_.set_value(active_date, x_val, df_.get_value(active_date, x_val)+c_step+1)
except IndexError:
pass
elif start:
df_.set_value(start, x_val, df_.get_value(start, x_val)+c_step+1)
# we need this to make sure start does not remain set for the next iteration:
start=False
elif isinstance(entry, str):
filtered_df = reference_df[reference_df[x_key] == x_val]
active_dates = list(set(filtered_df[entry]))
df_.set_value(active_dates, x_val, 1)
im = ax.pcolorfast(df_.T, cmap=add_grey(getattr(cm,saturate_cmap), 0.9), alpha=.5)
plt.hold(True)
ax = ttp_style(ax, df_)
plt.ylabel(" ".join(x_key.split("_")).replace("id","ID"))
