def read_field_information(self, trimester=None):
"""Read in all the FLD files."""
self.fldPar = queueTools.readAllFLDfiles(trimester)
# Read in the colormap
colormap = tableau_colormap.tableau20(raw=True)
color = [tuple_to_hex(colormap[x % len(colormap)])
for x in range(len(self.fldPar))]
self.fldPar['color'] = color
def read_field_information(self, trimester=None):
"""Read in all the FLD files."""
self.fldPar = queueTools.readAllFLDfiles(trimester)
# Read in the colormap
colormap = tableau_colormap.tableau20(raw=True)
color = [
tuple_to_hex(colormap[x % len(colormap)])
for x in range(len(self.fldPar))
]
self.fldPar['color'] = color
def insfocus_vs_temp(df):
"""Plot the spectrograph focus versus ambient temperature."""
fig, ax = plt.subplots(figsize=(6, 7))
tableau_colors = tableau20()
# Group data by spectrograph
groupby_spec = df.groupby(df['INSTRUME'])
ax.set_ylim(2.0, 4.2)
ax.set_xlim(-5, 22)
for inst, group in groupby_spec:
# Temperatures are integers, so we can take the mean at
# each value with a groupby
if inst == 'mmtbluechan':
cc = 1
label = "Blue Channel"
elif inst == 'mmtredchan':
cc = 7
label = "Red Channel"
groupby_temp = group.groupby(np.floor(group['TELTEMP']/2)*2)
focus = [x["INSFOCUS"].mean() for _, x in groupby_temp]
focus_std = [x["INSFOCUS"].std() for _, x in groupby_temp]
temp = [x for x, _ in groupby_temp]
ax.plot(group["TELTEMP"], group["INSFOCUS"],
'o', alpha=0.2, markersize=2, color=tableau_colors[cc],
label='')
ax.errorbar(temp, focus, yerr=focus_std, fmt='D', alpha=1,
color=tableau_colors[cc-1],
label=label, markersize=5)
# Quick linear regression
fit = np.polyfit(group['TELTEMP'], group['INSFOCUS'], 1)
xx = np.linspace(-5, 22, 2)
ax.plot(xx, fit[0]*xx+fit[1], color=tableau_colors[cc-1])
cc += 1
ax.grid()
ax.set_xlabel("Ambient Temperature ($^o$C)")
ax.set_ylabel("Spectrograph Focus (V)")
ax.legend(loc=3, prop={'size': 11}, frameon=False)
plt.savefig("insfocus_vs_temp.pdf")
def gratingUsage(df):
"""Plot a histogram showing how often each of the gratings are used."""
# First aggregate the data by grating
groupby_disp = df.groupby(df['INSTDISP'])
# Create some placeholder list
disp = []
count = []
plotcolor = []
for name, group in groupby_disp:
disp.append(name[2:])
count.append(len(group))
if name[0] == 'B':
plotcolor.append(1) # Dark Blue in tableau20
elif name[0] == 'R':
plotcolor.append(7) # Dark Red in tableau20
# Create the histogram
fig, ax = plt.subplots()
tableau_color = tableau20()
xpos = np.linspace(0, len(disp), len(disp))
barplot = ax.barh(xpos, count, align='center')
# Color the barplot according to instrument
for idx, color in enumerate(plotcolor):
barplot[idx].set_color(tableau_color[color])
plt.xlabel("Number of Uses in Archive")
# Hide Top and Right axis
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
# Shade the x labels (since they aren't really vital)
ax.spines['bottom'].set_visible(False)
ax.xaxis.label.set_color('grey')
ax.tick_params(axis='x', colors='grey')
plt.yticks(xpos, disp)
plt.savefig("grating_usage.pdf")
def grating_versus_cenwave(df):
"""Plot Central wavelength versus disperser.
Display frequency of how often each central
wavelength is used for each disperser.
"""
tableau_color = tableau20()
disperse_num = []
displist = sorted(list(df['INSTDISP'].unique()))
for disp in df['INSTDISP']:
disperse_num.append(displist.index(disp))
cenwaves = [df[df["INSTDISP"] == disp]["CENWAVE"] for disp in displist]
xpos = np.arange(len(displist))
fig, ax = plt.subplots()
violins = ax.violinplot(cenwaves,
xpos,
showmedians=False, showmeans=False,
showextrema=False)
for idx, pc in enumerate(violins['bodies']):
if displist[idx][0] == 'B':
cc = 0
else:
cc = 6
pc.set_facecolor(tableau_color[cc])
pc.set_edgecolor('none')
ax.set_xticks(xpos)
ax.set_xticklabels([x[2:] for x in displist], rotation=-90)
ax.set_xlim(-1, 13)
ax.plot(disperse_num, df['CENWAVE'], '.',
color='grey', markersize=3)
# Hide Top and Right axis
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_ylabel("Central Wavelength")
plt.savefig("centralwavelength_per_grating.pdf")
h, m, s = map(float, time.split(':'))
seconds = h*3600.0 + m*60.0 + s
return seconds / 3600.
def tuple_to_hex(rgb):
return '#%02x%02x%02x' % rgb
fldPar = MMTQueue.read_all_fld_files('2016a')
# The ephemeris kills things later, drop it
fldPar.drop('ephem', 1, inplace=True)
schedule_df = pd.read_csv('schedule.csv', parse_dates=['start_time'])
# We will use these colors to color each group by a different color
colormap = tableau20(raw=True)
color = [tuple_to_hex(colormap[x % len(colormap)]) for x in range(len(fldPar))]
fldPar['color'] = color
schedule_df['end_time'] = [x + datetime.timedelta(seconds=float(y))
for x, y in zip(schedule_df['start_time'],
schedule_df['duration'])]
schedule_df['mid_time'] = [x + datetime.timedelta(seconds=float(0.5*y))
for x, y in zip(schedule_df['start_time'],
schedule_df['duration'])]
schedule_df['mid_time'] = [x.hour + x.minute/60.
for x in schedule_df['mid_time']]
print(schedule_df['mid_time'])
mindate = schedule_df['start_time'].min()
schedule_df['start_date'] = [
math.floor((x - mindate).total_seconds()/24.0/3600.0)
h, m, s = map(float, time.split(':'))
seconds = h * 3600.0 + m * 60.0 + s
return seconds / 3600.
def tuple_to_hex(rgb):
return '#%02x%02x%02x' % rgb
fldPar = MMTQueue.read_all_fld_files('2016a')
# The ephemeris kills things later, drop it
fldPar.drop('ephem', 1, inplace=True)
schedule_df = pd.read_csv('schedule.csv', parse_dates=['start_time'])
# We will use these colors to color each group by a different color
colormap = tableau20(raw=True)
color = [tuple_to_hex(colormap[x % len(colormap)]) for x in range(len(fldPar))]
fldPar['color'] = color
schedule_df['end_time'] = [
x + datetime.timedelta(seconds=float(y))
for x, y in zip(schedule_df['start_time'], schedule_df['duration'])
]
schedule_df['mid_time'] = [
x + datetime.timedelta(seconds=float(0.5 * y))
for x, y in zip(schedule_df['start_time'], schedule_df['duration'])
]
schedule_df['mid_time'] = [
x.hour + x.minute / 60. for x in schedule_df['mid_time']
]
print(schedule_df['mid_time'])