def volume_unit_scroll(anchor, *colors):
clear_strip()
strip_center = strip.numPixels() / 2
meter_length = strip.numPixels() if anchor == 's' else strip_center
color_map = ([Color(0, 0, 0)] * len(colors)) + color.get_color_map(
len(colors) * 10, list(colors))
strip_pixels = np.zeros((strip.numPixels(), ), dtype=int)
while (visual_flag == 3 and anchor == 's') or (visual_flag == 4
and anchor == 'c'):
cur_data = np.fromstring(config.data, np.int16)
peak = np.average(np.abs(cur_data) / 2)
for i in range(strip.numPixels() - 1, 0, -1):
if anchor == 's' or i >= strip_center:
strip_pixels[i] = strip_pixels[i - 1]
strip.setPixelColor(i, strip_pixels[i])
elif i > 0:
strip_pixels[strip_center - i -
1] = strip_pixels[strip_center - i]
strip.setPixelColor(strip_center - i - 1,
strip_pixels[strip_center - i - 1])
cur_color = color_map[int(
np.interp(peak, [0, 13000], [0, len(color_map)]))]
if meter_length == strip.numPixels():
strip_pixels[0] = cur_color
strip.setPixelColor(0, strip_pixels[0])
else:
strip_pixels[strip_center] = cur_color
strip_pixels[strip_center - 1] = cur_color
strip.setPixelColor(strip_center, strip_pixels[strip_center])
strip.setPixelColor(strip_center - 1,
strip_pixels[strip_center - 1])
strip.show()
time.sleep(refresh_rate / 10)
def volume_unit_meter(anchor, *colors):
clear_strip()
strip_center = strip.numPixels() / 2
meter_length = strip.numPixels() if anchor == 's' else strip_center
color_map = color.get_color_map(meter_length, list(colors))
prev_num_bars = 0
while (visual_flag == 1 and anchor == 's') or (visual_flag == 2
and anchor == 'c'):
cur_data = np.fromstring(config.data, np.int16)
peak = np.average(np.abs(cur_data) / 2)
num_bars = int(np.interp(peak, [0, 13000], [0, meter_length]))
if num_bars <= prev_num_bars:
for i in range(prev_num_bars - 1, num_bars - 1, -1):
if anchor == 's':
strip.setPixelColor(i, Color(0, 0, 0))
else:
strip.setPixelColor(i + strip_center, Color(0, 0, 0))
strip.setPixelColor(strip_center - i - 1, Color(0, 0, 0))
strip.show()
time.sleep(refresh_rate / (prev_num_bars - num_bars))
else:
for i in range(prev_num_bars, num_bars):
if anchor == 's':
strip.setPixelColor(i, color_map[i])
else:
strip.setPixelColor(i + strip_center, color_map[i])
strip.setPixelColor(strip_center - i - 1, color_map[i])
strip.show()
time.sleep(refresh_rate / (num_bars - prev_num_bars))
prev_num_bars = num_bars
def geoplot(db,
col=None,
palette='BuGn',
classi='Quantiles',
backend='mpl',
color=None,
facecolor='#4D4D4D',
edgecolor='#B3B3B3',
alpha=1.,
linewidth=0.2,
marker='o',
marker_size=20,
ax=None,
hover=True,
p=None,
tips=None,
figsize=(9, 9),
**kwargs):
'''
Higher level plotter for geotables
...
Parameters
----------
db : DataFrame
GeoTable with 'geometry' column and values to be plotted.
col : None/str
[Optional. Default=None] Column holding the values to encode
into the choropleth.
palette : str/palettable palette
String of the `palettable.colorbrewer` portfolio, or a
`palettable` palette to use
classi : str
[Optional. Default='mpl'] Backend to plot the
backend : str
[Optional. Default='mpl'] Backend to plot the
geometries. Available options include Matplotlib ('mpl') or
Bokeh ('bk').
color : str/tuple/Series
[Optional. Default=None] Wrapper that sets both `facecolor`
and `edgecolor` at the same time. If set, `facecolor` and
`edgecolor` are ignored. It allows for either a single color
or a Series of the same length as `gc` with colors, indexed
on `gc.index`.
facecolor : str/tuple/Series
[Optional. Default='#4D4D4D'] Color for polygons and points. It
allows for either a single color or a Series of the same
length as `gc` with colors, indexed on `gc.index`.
edgecolor : str/tuple/Series
[Optional. Default='#B3B3B3'] Color for the polygon and point
edges. It allows for either a single color or a Series of
the same length as `gc` with colors, indexed on `gc.index`.
alpha : float/Series
[Optional. Default=1.] Transparency. It allows for either a
single value or a Series of the same length as `gc` with
colors, indexed on `gc.index`.
linewidth : float/Series
[Optional. Default=0.2] Width(s) of the lines in polygon and
line plotting (not applicable to points). It allows for
either a single value or a Series of the same length as `gc`
with colors, indexed on `gc.index`.
marker : str
[Optional. `mpl` backend only. Default='o'] Marker for point
plotting.
marker_size : int/Series
[Optional. Default=0.15] Width(s) of the lines in polygon and
ax : AxesSubplot
[Optional. `mpl` backend only. Default=None] Pre-existing
axes to which append the geometries.
hover : Boolean
[Optional. `bk` backend only. Default=True] Include hover tool.
p : bokeh.plotting.figure
[Optional. `bk` backend only. Default=None] Pre-existing
bokeh figure to which append the collections and setup.
tips : list of strings
series names to add to hover tool
kwargs : Dict
Additional named vaues to be passed to the classifier of choice.
'''
if col:
if hasattr(palette, 'number') and 'k' in kwargs:
if kwargs['k'] > palette.number:
raise ValueError(
'The number of classes requested is greater than '
'the number of colors available in the palette.')
lbl, c = value_classifier(db[col], scheme=classi, **kwargs)
if type(palette) is not str:
palette = get_color_map(palette=palette, k=c.k)
else:
palette = get_color_map(name=palette, k=c.k)
facecolor = lbl.map({i: j for i, j in enumerate(palette)})
try:
kwargs.pop('k')
except KeyError:
pass
col = [(col, db[col])]
if tips:
col = col or []
for tip in tips:
col.append((tip, db[tip]))
if col or tips:
col.append(('index', db.index.values))
col = collections.OrderedDict(col) # put mapped variable at the top
if backend is 'mpl':
plot_geocol_mpl(db['geometry'],
facecolor=facecolor,
ax=ax,
color=color,
edgecolor=edgecolor,
alpha=alpha,
linewidth=linewidth,
marker=marker,
marker_size=marker_size,
figsize=figsize,
**kwargs)
elif backend is 'bk':
plot_geocol_bk(db['geometry'],
facecolor=facecolor,
color=color,
edgecolor=edgecolor,
alpha=alpha,
linewidth=linewidth,
marker_size=marker_size,
hover=hover,
p=p,
col=col,
**kwargs)
else:
warn("Please choose an available backend")
return None
def geoplot(db, col=None, palette='BuGn', classi='Quantiles',
backend='mpl', color=None, facecolor='#4D4D4D', edgecolor='#B3B3B3',
alpha=1., linewidth=0.2, marker='o', marker_size=20,
ax=None, hover=True, p=None, tips=None, figsize=(9,9), **kwargs):
'''
Higher level plotter for geotables
...
Arguments
---------
db : DataFrame
GeoTable with 'geometry' column and values to be plotted.
col : None/str
[Optional. Default=None] Column holding the values to encode
into the choropleth.
palette : str/palettable palette
String of the `palettable.colorbrewer` portfolio, or a
`palettable` palette to use
classi : str
[Optional. Default='mpl'] Backend to plot the
backend : str
[Optional. Default='mpl'] Backend to plot the
geometries. Available options include Matplotlib ('mpl') or
Bokeh ('bk').
color : str/tuple/Series
[Optional. Default=None] Wrapper that sets both `facecolor`
and `edgecolor` at the same time. If set, `facecolor` and
`edgecolor` are ignored. It allows for either a single color
or a Series of the same length as `gc` with colors, indexed
on `gc.index`.
facecolor : str/tuple/Series
[Optional. Default='#4D4D4D'] Color for polygons and points. It
allows for either a single color or a Series of the same
length as `gc` with colors, indexed on `gc.index`.
edgecolor : str/tuple/Series
[Optional. Default='#B3B3B3'] Color for the polygon and point
edges. It allows for either a single color or a Series of
the same length as `gc` with colors, indexed on `gc.index`.
alpha : float/Series
[Optional. Default=1.] Transparency. It allows for either a
single value or a Series of the same length as `gc` with
colors, indexed on `gc.index`.
linewidth : float/Series
[Optional. Default=0.2] Width(s) of the lines in polygon and
line plotting (not applicable to points). It allows for
either a single value or a Series of the same length as `gc`
with colors, indexed on `gc.index`.
marker : str
[Optional. `mpl` backend only. Default='o'] Marker for point
plotting.
marker_size : int/Series
[Optional. Default=0.15] Width(s) of the lines in polygon and
ax : AxesSubplot
[Optional. `mpl` backend only. Default=None] Pre-existing
axes to which append the geometries.
hover : Boolean
[Optional. `bk` backend only. Default=True] Include hover tool.
p : bokeh.plotting.figure
[Optional. `bk` backend only. Default=None] Pre-existing
bokeh figure to which append the collections and setup.
tips : list of strings
series names to add to hover tool
kwargs : Dict
Additional named vaues to be passed to the classifier of choice.
'''
if col:
if hasattr(palette, 'number') and 'k' in kwargs:
if kwargs['k'] > palette.number:
raise ValueError('The number of classes requested is greater than '
'the number of colors available in the palette.')
lbl,c = value_classifier(db[col], scheme=classi, **kwargs)
if type(palette) is not str:
palette = get_color_map(palette=palette, k=c.k)
else:
palette = get_color_map(name=palette, k=c.k)
facecolor = lbl.map({i:j for i,j in enumerate(palette)})
try:
kwargs.pop('k')
except KeyError:
pass
col = [(col, db[col])]
if tips:
for tip in tips:
col.append((tip, db[tip]))
col.append(('index', db.index.values))
col = collections.OrderedDict(col) # put mapped variable at the top
if backend is 'mpl':
plot_geocol_mpl(db['geometry'], facecolor=facecolor, ax=ax,
color=color, edgecolor=edgecolor, alpha=alpha,
linewidth=linewidth, marker=marker, marker_size=marker_size,
figsize=figsize,
**kwargs)
elif backend is 'bk':
plot_geocol_bk(db['geometry'], facecolor=facecolor,
color=color, edgecolor=edgecolor, alpha=alpha,
linewidth=linewidth, marker_size=marker_size,
hover=hover, p=p, col=col, **kwargs)
else:
warn("Please choose an available backend")
return None