/usr/share/pyshared/mpl_toolkits/axisartist/grid_finder.py is in python-matplotlib 1.1.1~rc1+git20120423-0ubuntu1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 | import numpy as np
import matplotlib.cbook as mcbook
from matplotlib.transforms import Bbox
import clip_path
clip_line_to_rect = clip_path.clip_line_to_rect
import matplotlib.ticker as mticker
from matplotlib.transforms import Transform
# extremes finder
class ExtremeFinderSimple(object):
def __init__(self, nx, ny):
self.nx, self.ny = nx, ny
def __call__(self, transform_xy, x1, y1, x2, y2):
"""
get extreme values.
x1, y1, x2, y2 in image coordinates (0-based)
nx, ny : number of division in each axis
"""
x_, y_ = np.linspace(x1, x2, self.nx), np.linspace(y1, y2, self.ny)
x, y = np.meshgrid(x_, y_)
lon, lat = transform_xy(np.ravel(x), np.ravel(y))
lon_min, lon_max = lon.min(), lon.max()
lat_min, lat_max = lat.min(), lat.max()
return self._add_pad(lon_min, lon_max, lat_min, lat_max)
def _add_pad(self, lon_min, lon_max, lat_min, lat_max):
""" a small amount of padding is added because the current
clipping algorithms seems to fail when the gridline ends at
the bbox boundary.
"""
dlon = (lon_max - lon_min) / self.nx
dlat = (lat_max - lat_min) / self.ny
lon_min, lon_max = lon_min - dlon, lon_max + dlon
lat_min, lat_max = lat_min - dlat, lat_max + dlat
return lon_min, lon_max, lat_min, lat_max
class GridFinderBase(object):
def __init__(self,
extreme_finder,
grid_locator1,
grid_locator2,
tick_formatter1=None,
tick_formatter2=None):
"""
the transData of the axes to the world coordinate.
locator1, locator2 : grid locator for 1st and 2nd axis.
Derived must define "transform_xy, inv_transform_xy"
(may use update_transform)
"""
super(GridFinderBase, self).__init__()
self.extreme_finder = extreme_finder
self.grid_locator1 = grid_locator1
self.grid_locator2 = grid_locator2
self.tick_formatter1 = tick_formatter1
self.tick_formatter2 = tick_formatter2
def get_grid_info(self,
x1, y1, x2, y2):
"""
lon_values, lat_values : list of grid values. if integer is given,
rough number of grids in each direction.
"""
extremes = self.extreme_finder(self.inv_transform_xy, x1, y1, x2, y2)
# min & max rage of lat (or lon) for each grid line will be drawn.
# i.e., gridline of lon=0 will be drawn from lat_min to lat_max.
lon_min, lon_max, lat_min, lat_max = extremes
lon_levs, lon_n, lon_factor = \
self.grid_locator1(lon_min, lon_max)
lat_levs, lat_n, lat_factor = \
self.grid_locator2(lat_min, lat_max)
if lon_factor is None:
lon_values = np.asarray(lon_levs[:lon_n])
else:
lon_values = np.asarray(lon_levs[:lon_n]/lon_factor)
if lat_factor is None:
lat_values = np.asarray(lat_levs[:lat_n])
else:
lat_values = np.asarray(lat_levs[:lat_n]/lat_factor)
lon_lines, lat_lines = self._get_raw_grid_lines(lon_values,
lat_values,
lon_min, lon_max,
lat_min, lat_max)
ddx = (x2-x1)*1.e-10
ddy = (y2-y1)*1.e-10
bb = Bbox.from_extents(x1-ddx, y1-ddy, x2+ddx, y2+ddy)
grid_info = {}
grid_info["extremes"] = extremes
grid_info["lon_lines"] = lon_lines
grid_info["lat_lines"] = lat_lines
grid_info["lon"] = self._clip_grid_lines_and_find_ticks(lon_lines,
lon_values,
lon_levs,
bb)
grid_info["lat"] = self._clip_grid_lines_and_find_ticks(lat_lines,
lat_values,
lat_levs,
bb)
tck_labels = grid_info["lon"]["tick_labels"] = dict()
for direction in ["left", "bottom", "right", "top"]:
levs = grid_info["lon"]["tick_levels"][direction]
tck_labels[direction] = self.tick_formatter1(direction,
lon_factor, levs)
tck_labels = grid_info["lat"]["tick_labels"] = dict()
for direction in ["left", "bottom", "right", "top"]:
levs = grid_info["lat"]["tick_levels"][direction]
tck_labels[direction] = self.tick_formatter2(direction,
lat_factor, levs)
return grid_info
def _get_raw_grid_lines(self,
lon_values, lat_values,
lon_min, lon_max, lat_min, lat_max):
lons_i = np.linspace(lon_min, lon_max, 100) # for interpolation
lats_i = np.linspace(lat_min, lat_max, 100)
lon_lines = [self.transform_xy(np.zeros_like(lats_i)+lon, lats_i) \
for lon in lon_values]
lat_lines = [self.transform_xy(lons_i, np.zeros_like(lons_i)+lat) \
for lat in lat_values]
return lon_lines, lat_lines
def _clip_grid_lines_and_find_ticks(self, lines, values, levs, bb):
gi = dict()
gi["values"] = []
gi["levels"] = []
gi["tick_levels"] = dict(left=[], bottom=[], right=[], top=[])
gi["tick_locs"] = dict(left=[], bottom=[], right=[], top=[])
gi["lines"] = []
tck_levels = gi["tick_levels"]
tck_locs = gi["tick_locs"]
for (lx, ly), v, lev in zip(lines, values, levs):
xy, tcks = clip_line_to_rect(lx, ly, bb)
if not xy:
continue
gi["levels"].append(v)
gi["lines"].append(xy)
for tck, direction in zip(tcks, ["left", "bottom", "right", "top"]):
for t in tck:
tck_levels[direction].append(lev)
tck_locs[direction].append(t)
return gi
def update_transform(self, aux_trans):
if isinstance(aux_trans, Transform):
def transform_xy(x, y):
x, y = np.asarray(x), np.asarray(y)
ll1 = np.concatenate((x[:,np.newaxis], y[:,np.newaxis]), 1)
ll2 = aux_trans.transform(ll1)
lon, lat = ll2[:,0], ll2[:,1]
return lon, lat
def inv_transform_xy(x, y):
x, y = np.asarray(x), np.asarray(y)
ll1 = np.concatenate((x[:,np.newaxis], y[:,np.newaxis]), 1)
ll2 = aux_trans.inverted().transform(ll1)
lon, lat = ll2[:,0], ll2[:,1]
return lon, lat
else:
transform_xy, inv_transform_xy = aux_trans
self.transform_xy = transform_xy
self.inv_transform_xy = inv_transform_xy
def update(self, **kw):
for k in kw:
if k in ["extreme_finder",
"grid_locator1",
"grid_locator2",
"tick_formatter1",
"tick_formatter2"]:
setattr(self, k, kw[k])
else:
raise ValueError("unknown update property '%s'" % k)
class GridFinder(GridFinderBase):
def __init__(self,
transform,
extreme_finder=None,
grid_locator1=None,
grid_locator2=None,
tick_formatter1=None,
tick_formatter2=None):
"""
transform : transform from the image coordinate (which will be
the transData of the axes to the world coordinate.
or transform = (transform_xy, inv_transform_xy)
locator1, locator2 : grid locator for 1st and 2nd axis.
"""
if extreme_finder is None:
extreme_finder = ExtremeFinderSimple(20, 20)
if grid_locator1 is None:
grid_locator1 = MaxNLocator()
if grid_locator2 is None:
grid_locator2 = MaxNLocator()
if tick_formatter1 is None:
tick_formatter1 = FormatterPrettyPrint()
if tick_formatter2 is None:
tick_formatter2 = FormatterPrettyPrint()
super(GridFinder, self).__init__( \
extreme_finder,
grid_locator1,
grid_locator2,
tick_formatter1,
tick_formatter2)
self.update_transform(transform)
class MaxNLocator(mticker.MaxNLocator):
def __init__(self, nbins = 10, steps = None,
trim = True,
integer=False,
symmetric=False,
prune=None):
mticker.MaxNLocator.__init__(self, nbins, steps=steps,
trim=trim, integer=integer,
symmetric=symmetric, prune=prune)
self.create_dummy_axis()
self._factor = None
def __call__(self, v1, v2):
if self._factor is not None:
self.set_bounds(v1*self._factor, v2*self._factor)
locs = mticker.MaxNLocator.__call__(self)
return np.array(locs), len(locs), self._factor
else:
self.set_bounds(v1, v2)
locs = mticker.MaxNLocator.__call__(self)
return np.array(locs), len(locs), None
def set_factor(self, f):
self._factor = f
class FixedLocator(object):
def __init__(self, locs):
self._locs = locs
self._factor = None
def __call__(self, v1, v2):
if self._factor is None:
v1, v2 = sorted([v1, v2])
else:
v1, v2 = sorted([v1*self._factor, v2*self._factor])
locs = np.array([l for l in self._locs if ((v1 <= l) and (l <= v2))])
return locs, len(locs), self._factor
def set_factor(self, f):
self._factor = f
# Tick Formatter
class FormatterPrettyPrint(object):
def __init__(self, useMathText=True):
self._fmt = mticker.ScalarFormatter(useMathText=useMathText, useOffset=False)
self._fmt.create_dummy_axis()
self._ignore_factor = True
def __call__(self, direction, factor, values):
if not self._ignore_factor:
if factor is None:
factor = 1.
values = [v/factor for v in values]
#values = [v for v in values]
self._fmt.set_locs(values)
return [self._fmt(v) for v in values]
class DictFormatter(object):
def __init__(self, format_dict, formatter=None):
"""
format_dict : dictionary for format strings to be used.
formatter : fall-back formatter
"""
super(DictFormatter, self).__init__()
self._format_dict = format_dict
self._fallback_formatter = formatter
def __call__(self, direction, factor, values):
"""
factor is ignored if value is found in the dictionary
"""
if self._fallback_formatter:
fallback_strings = self._fallback_formatter(direction, factor, values)
else:
fallback_strings = [""]*len(values)
r = [self._format_dict.get(k, v) for k, v in zip(values,
fallback_strings)]
return r
if __name__ == "__main__":
locator = MaxNLocator()
locs, nloc, factor = locator(0, 100)
fmt = FormatterPrettyPrint()
print fmt("left", None, locs)
|