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"""
Plot camera image using just TargetCalib and python
"""
import numpy as np
from matplotlib.collections import PatchCollection
from matplotlib.patches import Rectangle
from CHECLabPy.plotting.setup import Plotter
from CHECLabPy.utils.mapping import get_clp_mapping_from_tc_mapping
class CameraPlotter(Plotter):
def __init__(self, mapping, talk=False):
"""
Plot values in a camera image
Parameters
----------
mapping : pandas.DataFrame
The mapping for the pixels stored in a pandas DataFrame. Can be
obtained from either of these options:
CHECLabPy.io.Reader.mapping
CHECLabPy.io.ReaderR0.mapping
CHECLabPy.io.ReaderR1.mapping
CHECLabPy.io.DL1Reader.mapping
"""
super().__init__(talk=talk)
print("CameraPlotter is deprecated, consider switching to CameraImage")
self.mapping = mapping
self.row = self.mapping['row'].values
self.col = self.mapping['col'].values
self.n_rows = self.mapping.metadata['n_rows']
self.n_cols = self.mapping.metadata['n_columns']
self.data = np.ma.zeros((self.n_rows, self.n_cols))
self.image = self.ax.imshow(self.data, origin='lower')
self.colorbar = self.fig.colorbar(self.image)
self.ax.axis('off')
@staticmethod
def figsize(scale=1.5):
super(CameraPlotter, CameraPlotter).figsize(scale)
def set(self, data):
self.data = np.ma.zeros((self.n_rows, self.n_cols))
self.data[self.row, self.col] = data
if (self.n_rows == 48) & (self.n_cols == 48):
self.data[0:8, 40:48] = np.ma.masked
self.data[0:8, 0:8] = np.ma.masked
self.data[40:48, 0:8] = np.ma.masked
self.data[40:48, 40:48] = np.ma.masked
self.image.set_data(self.data)
self.image.autoscale()
def annotate(self):
axl = self.mapping.metadata['fOTUpCol_l']
ayl = self.mapping.metadata['fOTUpRow_l']
adx = self.mapping.metadata['fOTUpCol_u'] - axl
ady = self.mapping.metadata['fOTUpRow_u'] - ayl
self.ax.arrow(axl, ayl, adx, ady, head_width=1, head_length=1, fc='r',
ec='r')
text = "ON-Telescope UP"
self.ax.text(axl, ayl, text, fontsize=8, color='r', ha='center',
va='bottom')
def plot(self, data):
self.set(data)
class CameraImage(Plotter):
def __init__(self, xpix, ypix, size, **kwargs):
"""
Create a camera-image plot
Parameters
----------
xpix : ndarray
The X positions of the pixels/superpixels/TMs
ypix : ndarray
The Y positions of the pixels/superpixels/TMs
size : float
The size of the pixels/superpixels/TMs
kwargs
Arguments passed to `CHECLabPy.plottong.setup.Plotter`
"""
super().__init__(**kwargs)
self._image = None
self._mapping = None
self.colorbar = None
self.autoscale = True
self.xpix = xpix
self.ypix = ypix
assert self.xpix.size == self.ypix.size
self.n_pixels = self.xpix.size
patches = []
for xx, yy in zip(self.xpix, self.ypix):
rr = size + 0.0001 # extra size to pixels to avoid aliasing
poly = Rectangle(
(xx - rr / 2., yy - rr / 2.),
width=rr,
height=rr,
fill=True,
)
patches.append(poly)
self.pixels = PatchCollection(patches, linewidth=0)
self.ax.add_collection(self.pixels)
self.pixels.set_array(np.zeros(self.n_pixels))
self.ax.set_aspect('equal', 'datalim')
self.ax.set_xlabel("X position (m)")
self.ax.set_ylabel("Y position (m)")
self.ax.autoscale_view()
self.ax.axis('off')
@staticmethod
def figsize(scale=1.5):
super(CameraPlotter, CameraPlotter).figsize(scale)
@property
def image(self):
return self._image
@image.setter
def image(self, val):
assert val.size == self.n_pixels
self.pixels.set_array(val)
self.pixels.changed()
if self.autoscale:
self.pixels.autoscale() # Updates the colorbar
self.ax.figure.canvas.draw()
def add_colorbar(self, label=''):
self.colorbar = self.ax.figure.colorbar(self.pixels, label=label)
def set_limits_minmax(self, zmin, zmax):
"""
Set the color scale limits from min to max
"""
self.pixels.set_clim(zmin, zmax)
self.autoscale = False
def reset_limits(self):
"""
Reset to auto color scale limits
"""
self.autoscale = True
self.pixels.autoscale()
def annotate_on_telescope_up(self):
"""
Add an arrow indicating where "ON-Telescope-UP" is
"""
if self._mapping is not None:
axl = self._mapping.metadata['fOTUpX_l']
ayl = self._mapping.metadata['fOTUpY_l']
adx = self._mapping.metadata['fOTUpX_u'] - axl
ady = self._mapping.metadata['fOTUpY_u'] - ayl
text = "ON-Telescope UP"
self.ax.arrow(axl, ayl, adx, ady, head_width=0.01,
head_length=0.01, fc='r', ec='r')
self.ax.text(axl, ayl, text, fontsize=4, color='r',
ha='center', va='bottom')
else:
print("Cannot annotate, no mapping attached to class")
def add_text_to_pixel(self, pixel, value, fmt=None, size=3):
"""
Add a text label to a single pixel
Parameters
----------
pixel : int
value : str or float
fmt : str
String/float formatting expression
size : int
Font size
"""
pos_x = self.xpix[pixel]
pos_y = self.ypix[pixel]
if fmt:
val = fmt.format(value)
self.ax.text(pos_x, pos_y, value, fontsize=size,
color='w', ha='center')
def add_pixel_text(self, values, fmt=None, size=3):
"""
Add a text label to each pixel
Parameters
----------
values : ndarray
fmt : str
String/float formatting expression
size : int
Font size
"""
assert values.size == self.n_pixels
for pixel in range(self.n_pixels):
self.add_text_to_pixel(pixel, values[pixel], fmt, size)
def annotate_tm_edge_label(self):
"""
Annotate each of the TMs on the top and bottom of the camera
"""
if self._mapping is not None:
kw = dict(fontsize=6, color='black', ha='center')
m = self._mapping
pix_size = self._mapping.metadata['size']
f_tm_top = lambda g: m.ix[m.ix[g.index]['row'].idxmax(), 'slot']
f_tm_bottom = lambda g: m.ix[m.ix[g.index]['row'].idxmin(), 'slot']
tm_top = np.unique(m.groupby('col').agg(f_tm_top)['slot'])
tm_bottom = np.unique(m.groupby('col').agg(f_tm_bottom)['slot'])
for tm in tm_top:
df = m.loc[m['slot'] == tm]
ypix = df['ypix'].max() + pix_size * 0.7
xpix = df['xpix'].mean()
tm_txt = "TM{:02d}".format(tm)
self.ax.text(xpix, ypix, tm_txt, va='bottom', **kw)
for tm in tm_bottom:
df = m.loc[m['slot'] == tm]
ypix = df['ypix'].min() - pix_size * 0.7
xpix = df['xpix'].mean()
tm_txt = "TM{:02d}".format(tm)
self.ax.text(xpix, ypix, tm_txt, va='top', **kw)
else:
print("Cannot annotate, no mapping attached to class")
@classmethod
def from_mapping(cls, mapping, **kwargs):
"""
Generate the class from a CHECLabPy mapping dataframe
Parameters
----------
mapping : `pandas.DataFrame`
The mapping for the pixels stored in a pandas DataFrame. Can be
obtained from either of these options:
CHECLabPy.io.TIOReader.mapping
CHECLabPy.io.ReaderR0.mapping
CHECLabPy.io.ReaderR1.mapping
CHECLabPy.io.DL1Reader.mapping
CHECLabPy.utils.mapping.get_clp_mapping_from_tc_mapping
kwargs
Arguments passed to `CHECLabPy.plottong.setup.Plotter`
Returns
-------
`CameraImage`
"""
xpix = mapping['xpix'].values
ypix = mapping['ypix'].values
size = mapping.metadata['size']
image = cls(xpix, ypix, size, **kwargs)
image._mapping = mapping
return image
@classmethod
def from_tc_mapping(cls, tc_mapping, **kwargs):
"""
Generate the class using the TargetCalib Mapping Class
Parameters
----------
tc_mapping : `target_calib.Mapping`
kwargs
Arguments passed to `CHECLabPy.plottong.setup.Plotter`
Returns
-------
`CameraImage`
"""
mapping = get_clp_mapping_from_tc_mapping(tc_mapping)
return cls.from_mapping(mapping, **kwargs)
@classmethod
def from_camera_version(cls, camera_version, single=False, **kwargs):
"""
Generate the class using the camera version (required TargetCalib)
Parameters
----------
camera_version : str
Version of the camera (e.g. "1.0.1" corresponds to CHEC-S)
single : bool
Designate if it is just a single module you wish to plot
kwargs
Arguments passed to `CHECLabPy.plottong.setup.Plotter`
Returns
-------
`CameraImage`
"""
from target_calib import CameraConfiguration
config = CameraConfiguration(camera_version)
tc_mapping = config.GetMapping(single)
return cls.from_tc_mapping(tc_mapping, **kwargs)
class CameraImageImshow(Plotter):
def __init__(self, row, col, n_rows, n_cols, **kwargs):
"""
Create a camera-image plot using imshow (essentially a 2D histogram,
therefore missing module gaps)
Parameters
----------
row : ndarray
The row for each pixel/superpixel/TM
col : ndarray
The row for each pixel/superpixel/TM
n_rows : int
The number of rows of pixel/superpixel/TM on the camera
n_cols : int
The number of rows of pixel/superpixel/TM on the camera
kwargs
Arguments passed to `CHECLabPy.plottong.setup.Plotter`
"""
super().__init__(**kwargs)
self._image = None
self._mapping = None
self.colorbar = None
self.autoscale = True
self.row = row
self.col = col
self.n_rows = n_rows
self.n_cols = n_cols
self.n_pixels = self.row.size
assert self.row.size == self.col.size
data = np.ma.zeros((self.n_rows, self.n_cols))
self.mask(data)
self.camera = self.ax.imshow(data, origin='lower')
self.ax.axis('off')
@staticmethod
def figsize(scale=1.5):
super(CameraPlotter, CameraPlotter).figsize(scale)
@property
def image(self):
return self._image
@image.setter
def image(self, val):
assert val.size == self.n_pixels
self._image = val
data = np.ma.zeros((self.n_rows, self.n_cols))
self.mask(data)
data[self.row, self.col] = val
self.camera.set_data(data)
if self.autoscale:
self.camera.autoscale()
def add_colorbar(self, label=''):
self.colorbar = self.fig.colorbar(self.camera, label=label)
def set_limits_minmax(self, zmin, zmax):
"""
Set the color scale limits from min to max
"""
self.camera.set_clim(zmin, zmax)
self.autoscale = False
def reset_limits(self):
"""
Reset to auto color scale limits
"""
self.autoscale = True
self.camera.autoscale()
def mask(self, data):
if (self.n_rows == 48) & (self.n_cols == 48):
data[0:8, 40:48] = np.ma.masked
data[0:8, 0:8] = np.ma.masked
data[40:48, 0:8] = np.ma.masked
data[40:48, 40:48] = np.ma.masked
elif (self.n_rows == 24) & (self.n_cols == 24):
data[0:4, 20:24] = np.ma.masked
data[0:4, 0:4] = np.ma.masked
data[20:24, 0:4] = np.ma.masked
data[20:24, 20:24] = np.ma.masked
elif (self.n_rows == 6) & (self.n_cols == 6):
data[0:1, 5:6] = np.ma.masked
data[0:1, 0:1] = np.ma.masked
data[5:6, 0:1] = np.ma.masked
data[5:6, 5:6] = np.ma.masked
def annotate_on_telescope_up(self):
"""
Add an arrow indicating where "ON-Telescope-UP" is
"""
if self._mapping is not None:
axl = self._mapping.metadata['fOTUpCol_l']
ayl = self._mapping.metadata['fOTUpRow_l']
adx = self._mapping.metadata['fOTUpCol_u'] - axl
ady = self._mapping.metadata['fOTUpRow_u'] - ayl
text = "ON-Telescope UP"
self.ax.arrow(axl, ayl, adx, ady, head_width=1, head_length=1,
fc='r', ec='r')
self.ax.text(axl, ayl, text, fontsize=8, color='r',
ha='center', va='bottom')
else:
print("Cannot annotate, no mapping attached to class")
def add_text_to_pixel(self, pixel, value, fmt=None, size=3):
"""
Add a text label to a single pixel
Parameters
----------
pixel : int
value : str or float
fmt : str
String/float formatting expression
size : int
Font size
"""
pos_x = self.col[pixel]
pos_y = self.row[pixel]
if fmt:
val = fmt.format(value)
self.ax.text(pos_x, pos_y, value, fontsize=size,
color='w', ha='center')
def add_pixel_text(self, values, fmt=None, size=3):
"""
Add a text label to each pixel
Parameters
----------
values : ndarray
fmt : str
String/float formatting expression
size : int
Font size
"""
assert values.size == self.n_pixels
for pixel in range(self.n_pixels):
self.add_text_to_pixel(pixel, values[pixel], fmt, size)
def annotate_tm_edge_label(self):
"""
Annotate each of the TMs on the top and bottom of the camera
"""
if self._mapping is not None:
kw = dict(fontsize=6, color='black', ha='center')
m = self._mapping
n_rows = m.metadata['n_rows']
f_tm_top = lambda g: m.ix[m.ix[g.index]['row'].idxmax(), 'slot']
f_tm_bottom = lambda g: m.ix[m.ix[g.index]['row'].idxmin(), 'slot']
tm_top = np.unique(m.groupby('col').agg(f_tm_top)['slot'])
tm_bottom = np.unique(m.groupby('col').agg(f_tm_bottom)['slot'])
for tm in tm_top:
df = m.loc[m['slot'] == tm]
row = df['row'].max() + n_rows/48 + 0.4
col = df['col'].mean()
tm_txt = "TM{:02d}".format(tm)
self.ax.text(col, row, tm_txt, va='bottom', **kw)
for tm in tm_bottom:
df = m.loc[m['slot'] == tm]
row = df['row'].min() - n_rows/48 - 0.4
col = df['col'].mean()
tm_txt = "TM{:02d}".format(tm)
self.ax.text(col, row, tm_txt, va='top', **kw)
else:
print("Cannot annotate, no mapping attached to class")
@classmethod
def from_mapping(cls, mapping, **kwargs):
"""
Generate the class using a CHECLabPy mapping dataframe
Parameters
----------
mapping : `pandas.DataFrame`
The mapping for the pixels stored in a pandas DataFrame. Can be
obtained from either of these options:
CHECLabPy.io.TIOReader.mapping
CHECLabPy.io.ReaderR0.mapping
CHECLabPy.io.ReaderR1.mapping
CHECLabPy.io.DL1Reader.mapping
CHECLabPy.utils.mapping.get_clp_mapping_from_tc_mapping
kwargs
Arguments passed to `CHECLabPy.plottong.setup.Plotter`
Returns
-------
`CameraImage`
"""
row = mapping['row'].values
col = mapping['col'].values
n_rows = mapping.metadata['n_rows']
n_cols = mapping.metadata['n_columns']
image = cls(row, col, n_rows, n_cols, **kwargs)
image._mapping = mapping
return image
@classmethod
def from_tc_mapping(cls, tc_mapping, **kwargs):
"""
Generate the class using the TargetCalib Mapping Class
Parameters
----------
tc_mapping : `target_calib.Mapping`
kwargs
Arguments passed to `CHECLabPy.plottong.setup.Plotter`
Returns
-------
`CameraImage`
"""
mapping = get_clp_mapping_from_tc_mapping(tc_mapping)
return cls.from_mapping(mapping, **kwargs)
@classmethod
def from_camera_version(cls, camera_version, single=False, **kwargs):
"""
Generate the class using the camera version (required TargetCalib)
Parameters
----------
camera_version : str
Version of the camera (e.g. "1.0.1" corresponds to CHEC-S)
single : bool
Designate if it is just a single module you wish to plot
kwargs
Arguments passed to `CHECLabPy.plottong.setup.Plotter`
Returns
-------
`CameraImage`
"""
from target_calib import CameraConfiguration
config = CameraConfiguration(camera_version)
tc_mapping = config.GetMapping(single)
return cls.from_tc_mapping(tc_mapping, **kwargs)