def components(self, start, window):
component_data = self.transformer.fit_transform(
self.data.iloc[start:(start + window), :].T)
if self.labels:
data_labels = reduce(
mul,
pd.DataFrame(
component_data,
index=self.data.columns.tolist(),
columns=["Component_1", "Component_2"],
).reset_index().apply(
lambda x: hv.Text(
x[1], x[2], " ".join(x[0].split()[:-1]), fontsize=8),
axis=1,
).tolist(),
)
else:
data_labels = hv.Text(0, 0, "")
return (pd.DataFrame(
component_data, columns=["Component_1", "Component_2"]
).hvplot.scatter(x="Component_1", y="Component_2").redim(
Component_2={
"range": (-0.1, 0.3)
},
Component_1={
"range": (-0.03, 0.05)
}).redim.label(
Component_1=
f"Component 1 {self.transformer.explained_variance_ratio_[0].round(4)}%",
Component_2=
f"Component 2 {self.transformer.explained_variance_ratio_[1].round(4)}%",
).options(alpha=1) * data_labels)
def scalebar_cropped(self, x_range, y_range):
if x_range and y_range:
x0, x1 = x_range
y0, y1 = y_range
x_span = x1 - x0
y_span = y1 - y0
y0 = y0 + int(y_span / 20)
y1 = y0 + int(y_span / 50)
ytext = y1 + int(y_span / 30)
x0 = x1 - self.scalebar_size_cropped - int(x_span / 30)
x1 = x1 - int(x_span / 30)
rect = hv.Rectangles((x0, y0, x1, y1)).opts(color="w")
text = hv.Text(x0, ytext, str(self.scalebar_size_cropped) + " um").opts(
text_color="w", text_align="left", text_font="Helvetica"
)
scalebar = rect * text
return scalebar
else:
x0 = self.x_range[0]
y0 = self.y_range[0]
x1 = self.x_range[1]
y1 = self.y_range[1]
x_span = x1 - x0
y_span = y1 - y0
y0 = y0 + int(y_span / 20)
y1 = y0 + int(y_span / 50)
x0 = x1 - self.scalebar_size_cropped - int(x_span / 40)
x1 = x1 - int(x_span / 30)
ytext = y1 + int(y_span / 30)
rect = hv.Rectangles((x0, y0, x1, y1)).opts(color="w")
text = hv.Text(x0, ytext, str(self.scalebar_size_cropped) + " um").opts(
text_color="w", text_align="left", text_font="Helvetica"
)
scalebar = rect * text
return scalebar
def scalebar(self, x_range, y_range):
if x_range and y_range:
x0, x1 = x_range
y0, y1 = y_range
x_span = x1 - x0
y_span = y1 - y0
y0 = y0 + int(y_span / 20)
y1 = y0 + int(y_span / 50)
ytext = y1 + int(y_span / 30)
x0 = x1 - self.scalebar_size - int(x_span / 30)
x1 = x1 - int(x_span / 30)
rect = hv.Rectangles((x0, y0, x1, y1)).opts(color="w")
text = hv.Text(x0, ytext, str(self.scalebar_size) + " um").opts(
text_color="w", text_align="left", text_font="Helvetica"
)
scalebar = rect * text
return scalebar
else:
x0 = 0
y0 = 0
x1 = self.rendered_image.data["x"].size
y1 = self.rendered_image.data["y"].size
x_span = x1 - x0
y_span = y1 - y0
y0 = y0 + int(y_span / 20)
y1 = y0 + int(y_span / 50)
x0 = x1 - self.scalebar_size - int(x_span / 40)
x1 = x1 - int(x_span / 30)
ytext = y1 + int(y_span / 30)
rect = hv.Rectangles((x0, y0, x1, y1)).opts(color="w")
text = hv.Text(x0, ytext, str(self.scalebar_size) + " um").opts(
text_color="w", text_align="left", text_font="Helvetica"
)
scalebar = rect * text
return scalebar
def h_text(data): #function to write ycrop value
center=frame.shape[1]//2
try:
y=int(np.around(data['y'][0]))
htext=hv.Text(center,y+10,'ycrop: {x}'.format(x=y))
return htext
except:
htext=hv.Text(center,10, 'ycrop: 0')
return htext
def cross_hair_info(x, y):
text = hv.Text(x + 0.05,
y,
"%.3f %.3f %.3f" % (x, y, img[x, y]),
halign="left",
valign="bottom")
return hv.HLine(y) * hv.VLine(x) * text
def __init__(self, data, configs):
'''removes wrong data'''
for file in list(data.keys()):
if "contact chain" not in data[file]["header"][3].lower():
data.pop(file)
self.log = logging.getLogger(__name__)
self.data = convert_to_df(data, abs=False)
self.config = configs
self.df = []
self.basePlots = None
self.analysisname = "Contact_Chain"
self.PlotDict = {"Name": self.analysisname}
self.measurements = self.data["columns"]
self.sort_parameter = self.config["Contact_Chain"]["Bar_chart"][
"CreateBarChart"]
self.Substrate_Type = ["Polysilicon", "N+", "P+"]
self.filename_df = pd.DataFrame(columns=[
"Filename", "Substrate Type", "_", "Batch", "Wafer No.", "_",
"HM location", "Test structure", "Resistance", "Standard deviation"
])
self.PlotDict["All"] = None
self.limits = {"Polysilicon": 4 * 10**7, "N+": 10**5, "P+": 8 * 10**4}
self.files_to_fit = self.config["files_to_fit"]
hvtext = hv.Text(0, 0, self.analysisname,
fontsize=20).opts(color="black", xlabel='', ylabel='')
box = hv.Polygons(hv.Box(0, 0,
1).opts(color="black")).opts(color="white")
self.PlotDict["All"] = box * hvtext
def integrated_charge(limit_a, limit_b, y, iteration):
# compute 1D histogram
energy_hist, bin_edges, nbins = particle_energy_histogram(
tseries=time_series,
it=iteration,
cutoff=np.inf, # no cutoff
energy_max=e_max,
)
histogram = hv.Histogram((bin_edges, energy_hist),
kdims=energy,
vdims=count)
curve = hv.Curve(histogram)
e_min = histogram.edges[0]
limit_a = e_min if limit_a is None else np.clip(limit_a, e_min, e_max)
limit_b = e_max if limit_b is None else np.clip(limit_b, e_min, e_max)
area = hv.Area((curve.dimension_values('energy'),
curve.dimension_values('frequency')))[limit_a:limit_b]
charge = np.sum(
np.diff(histogram[limit_a:limit_b].edges) *
histogram[limit_a:limit_b].values)
return curve * area * hv.VLine(limit_a) * hv.VLine(limit_b) * hv.Text(
limit_b - 2., 5, 'Q = %.0f pC' % charge)
def plot_ana(self, x, y, dy, file, ana_type):
curve = hv.Curve(zip(x, y), kdims=self.measurements[1], vdims=self.measurements[2])
derivative = hv.Curve(zip(x, dy)).opts(color="gray")
df = pd.DataFrame({"x": x, "y": y, "dy": dy})
'''returns voltage and: for Ana 1 fit line, for Ana 2/3 line to show where the voltage is'''
voltage, line = self.find_voltage(df, x, ana_type)
voltage = round(voltage, 4)
text_str = "voltage: " + str(voltage)
if ana_type == "Ana 3":
text_str += "\nDerivative scaled down by: \n/ (2 * 10^6)"
text = hv.Text(min(dy) * (6 / 4), max(dy) * (7 / 8), text_str, fontsize=20)
curve = curve * derivative * line * text
if ana_type == "Ana 3":
curve.opts(**self.config["FET"].get("General", {}), ylim=(min(dy) - 3 * min(y) / 20, max(dy) + max(dy) / 10))
else:
curve.opts(**self.config["FET"].get("General", {}), ylim=(min(y) - 3 * min(y) / 20, max(y) + max(y) / 10))
self.data[file][ana_type] = voltage
if self.PlotDict["All"] is None:
self.PlotDict["All"] = curve
else:
self.PlotDict["All"] = self.PlotDict["All"] + curve
def plot_flatband_v(x, y, ana_type, **kwargs):
'''
**kwargs for customizing the plot, ana_type must ether be "fit" or "derivative"
'''
x, y = list(x), list(y)
if ana_type == "fit":
voltage, middle_line, right_line = fit_analysis(x, y)
elif ana_type == "derivative":
voltage = derivative_analysis(x, y)
else:
log.error("ana_type must either be 'derivative' or 'fit'")
exit(1)
curve = hv.Curve(zip(x, y), kdims="voltage_hvsrc", vdims="capactiance")
text_str = "Flatband Voltage: " + str(voltage) + "\nAnalysis Type: " + ana_type
text = hv.Text(max(x) * (3 / 4), max(y) * (3 / 4), text_str, fontsize=20)
line = hv.VLine(voltage).opts(color="black", line_width=1.0)
curve = curve * text * line
if ana_type == "fit":
mid = hv.Curve([*middle_line]).opts(color="red", line_width=1.5)
right = hv.Curve([*right_line]).opts(color="blue", line_width=1.0)
curve = curve * text * line * mid * right
curve.opts(ylim=(min(y) - 3 * min(y) / 20, max(y) + max(y) / 10), **kwargs)
return curve
def fetch_mpl_obj(self):
group = "Gas Concentration vs Time"
plot = dict(aspect=2)
legend = dict(legend_position='best', aspect=2)
graph = None
options = hv.Store.options(backend='matplotlib')
options.Curve = hv.Options('style',
color=hv.Cycle(values=self.gases.values()),
linewidth=2)
for data in self.graph_data:
graph_inter = hv.Curve(data['data'],
vdims=['Gas Concentration'],
kdims=['Time'],
label=data['label'],
group=group)
if graph:
graph *= graph_inter
else:
graph = graph_inter
for data in self.text_labels:
graph *= hv.Text(data['key'],
data['value'],
data['date'],
fontsize=10)
opts = {'Curve': {'plot': plot}, 'Overlay': {'plot': legend}}
self.mpl_obj = graph(opts) if graph else None
def plot_flatband(self, file, ana_type, interpol):
'''plot function for both "derivative" and "fit" analysis'''
x, y = self.data[file][ana_type]["dataframe"]['x'], self.data[file][
ana_type]["dataframe"]['y']
curve = hv.Curve(zip(x, y),
kdims=self.measurements[1],
vdims=self.measurements[3])
text_str = "Flatband Voltage: " + str(
self.data[file][ana_type]["flatband"]
) + "\nAnalysis Type: " + ana_type + "\nInterpolated: " + str(interpol)
text = hv.Text(x.max() * (3 / 4),
y.max() * (3 / 4),
text_str,
fontsize=20)
line = hv.VLine(self.data[file][ana_type]["flatband"]).opts(
color="black", line_width=1.0)
curve = curve * text * line
if ana_type == "fit":
curve = curve * text * line * self.data[file]["fit"]["lines"][
0] * self.data[file]["fit"]["lines"][1]
curve.opts(**self.config["MOS_CV"].get("General", {}),
ylim=(y.min() - 3 * y.min() / 20, y.max() + y.max() / 10))
if self.PlotDict["All"] is None:
self.PlotDict["All"] = curve
else:
self.PlotDict["All"] = self.PlotDict["All"] + curve
def _legend_texts(df):
width = _max_width(df)
text_opts = LEGEND_TEXT_OPTS.copy()
text_opts.update({"xlim": (-1, width)})
texts = []
for _, r in df.iterrows():
texts.append(hv.Text(r["x"] + 1, r["y"], r["name"]).opts(**text_opts))
return texts
def integral(limit_a, limit_b, y, time):
limit_a = -3 if limit_a is None else np.clip(limit_a, -3, 3)
limit_b = 3 if limit_b is None else np.clip(limit_b, -3, 3)
curve = hv.Curve((xs, function(xs, time)))
area = hv.Area((xs, function(xs, time)))[limit_a:limit_b]
summed = area.dimension_values('y').sum() * 0.015 # Numeric approximation
return (area * curve * hv.VLine(limit_a) * hv.VLine(limit_b) *
hv.Text(limit_b - 0.8, 2.0, '%.2f' % summed))
def make_text(content):
return hv.Text(0, 0, content).opts(img_opts).opts(toolbar=None,
height=100,
width=150,
xaxis=None,
yaxis=None,
text_alpha=1.0,
bgcolor='lightgrey')
def plot_surface(obj, **args):
region = args.get('region', None)
idx = obj.tag2idx(region)
tags = args.get('tags', False)
coord = args.get('coord', None)
locator = args.get('locator', False)
fill = args.get('fill', None)
amin = np.min(obj.bbox_min[idx], axis=0)
amax = np.max(obj.bbox_max[idx], axis=0)
wh = amax - amin
if np.min(wh) < 250:
wh = wh / np.min(wh) * 250
hvobj = hv.Path([obj.boundary[i]
for i in idx]).opts(style=dict(color='k'),
plot=dict(yaxis=None,
xaxis=None,
aspect='equal',
width=int(ceil(wh[0])),
height=int(ceil(wh[1]))))
if fill is not None:
vals = np.nan * np.zeros((obj.num, ))
for k in fill.keys():
_idx = obj.tag2idx(k)
for i in _idx:
vals[i] = fill[k]
imin = np.min(obj.bbox_min[idx], axis=0).astype(int)
imax = np.max(obj.bbox_max[idx], axis=0).astype(int)
im = np.nan * np.zeros(tuple((imax - imin + 1)[::-1].tolist()))
for i in idx:
im[obj._coords[i][:, 1] - imin[1],
obj._coords[i][:, 0] - imin[0]] = vals[i]
hvobj = hv.Image(np.flipud(im),
bounds=(imin[0], imin[1], imax[0], imax[1])).opts(
plot=dict(yaxis=None, xaxis=None))
if coord is not None:
hvobj *= hv.Curve([obj.hand2pixel((0,0)),obj.hand2pixel((coord,0))]) *\
hv.Curve([obj.hand2pixel((0,0)),obj.hand2pixel((0,coord))])
if tags:
hvobj *= hv.Labels({
'x': [obj._centers[i][0] for i in idx],
'y': [obj._centers[i][1] for i in idx],
'Label': [str(i) + ' ' + ''.join(obj.tags[i]) for i in idx]
})
# show cursor position in hand coordinates (works only in bokeh)
if locator:
pointer = hv.streams.PointerXY(x=0, y=0)
dm = hv.DynamicMap(lambda x, y: hvobj * hv.Text(
x, y + 5, '(%d,%d)' % tuple(obj.pixel2hand(np.array([x, y])))),
streams=[pointer])
return dm
return hvobj
def _show_sunset_hour(df, geoloc):
hover = HoverTool(tooltips=[
('Date', '@datetime{%m/%d}'),
('Hour of Sunset', '@hour{0.1f}'),
('Length of Day', '@daylight{0.1f}'),
],
formatters={
'datetime': 'datetime',
},
mode='vline')
sunset_df = df.copy()
sunset_df = df.loc[df['sun_down'] == False, :]
sunset_df = sunset_df.assign(**{
'hour': sunset_df['hour'] - 12,
'hour_24': sunset_df['hour_24'] - 12
})
lat, lon = geoloc.latitude, geoloc.longitude
address = geoloc.address
sunset_curve = sunset_df.hvplot('datetime',
'hour',
hover_cols=['daylight'],
responsive=True)
sunset_curve = sunset_curve.opts(
invert_yaxis=True,
color='darkblue',
xlabel='Date',
ylabel='PM Hour of Sunset [Local Time]',
title=f'Yearly Sunset Hour at {address} ({lat:.1f} N, {lon:.1f} E)',
hooks=[_format_datetime_axis],
show_grid=True,
gridstyle={'ygrid_line_alpha': 0},
tools=[hover],
ylim=(4, 9))
sun_up = hv.Area(df.loc[df['sun_down'] == False], 'datetime', 'hour')
sun_up = sun_up.opts(color='tan', alpha=0.15, responsive=True)
sun_down = hv.Area(sunset_df, 'datetime', ['hour', 'hour_24'])
sun_down = sun_down.opts(color='darkblue', alpha=0.15, responsive=True)
five_pm_line = hv.HLine(5).opts(color='black',
alpha=0.1,
line_dash='dotted',
responsive=True)
five_pm_txt = hv.Text(pd.datetime(2020, 7, 4), 5, '5 PM')
five_pm_txt = five_pm_txt.opts(text_font_size='1.5em',
text_alpha=0.2,
text_baseline='bottom',
text_align='left',
responsive=True)
overlay = (sunset_curve * sun_up * sun_down * five_pm_line * five_pm_txt)
return overlay
def _timetable(self, x, y):
if self.solutions.empty:
return (hv.Points((0, 0)).opts(alpha=0) * hv.Text(
0, 0, 'No Journeys Found').opts(color='firebrick')).opts(
xlim=(-1, 1),
xaxis=None,
yaxis=None,
show_frame=False,
toolbar=None)
stop_time = self.stop_time * 10**3
time_delta = stop_time - self.solutions['start_time'].min()
boxes_opts = {
'color':
'color',
'line_width':
0,
'tools': [
HoverTool(
tooltips=[('From',
'@station_name'), ('To', '@station_name_stop'),
('Departure Time',
'@departure'), ('Arrival Time', '@arrival'),
('Travel Type',
'@transport_type'), (
'Line', '@line_text'), ('Trip',
'@trip_id')])
],
}
opts = {
'height':
int(self.solutions['path'].max() + 3) * 50,
'width':
600,
'ylim': (-1, self.solutions['path'].max() + 2),
'xlim': (self.solutions['start_time'].min() - time_delta * 0.1,
stop_time + time_delta * 0.1),
'hooks': [datetime_ticks],
'yaxis':
None,
'show_frame':
False,
'xlabel':
'',
'toolbar':
None,
'active_tools': []
}
boxes = hv.Rectangles(
self.line_aggregate, ['start_time', 'y_min', 'stop_time', 'y_max'],
[
'color', 'station_name', 'station_name_stop', 'departure',
'arrival', 'transport_type', 'trip_id', 'line_text'
]).opts(**boxes_opts)
text = self._timetable_text()
stop_line = hv.VLine(stop_time).opts(line_width=1, line_color='red')
return (boxes * text * stop_line).opts(**opts)
def _timetable_text(self):
fontsize = 8
texts = []
for _, connection in self.line_aggregate.iterrows():
start_time = connection['start_time']
y = connection['y_min']
icon = transport_icons[connection['transport_type']]
short_line = connection['line_text']
texts.append(
hv.Text(x=start_time,
y=y - 0.15,
text=f'{icon}{short_line}',
halign='left',
valign='top',
fontsize=fontsize))
for _, journey in self.journey_aggregate.iterrows():
arrival_text = journey['travel_time_str'] + ' - ' + journey[
'arrival']
probability_text = f" {journey['probability']:6.3%}"
texts.extend([
hv.Text(x=journey['start_time'],
y=journey['y_max'],
text=journey['departure'],
halign='left',
valign='bottom',
fontsize=fontsize),
hv.Text(x=journey['stop_time'],
y=journey['y_max'],
text=arrival_text,
halign='right',
valign='bottom',
fontsize=fontsize),
hv.Text(x=self.stop_time * 10**3,
y=journey['path'],
text=probability_text,
halign='left',
valign='bottom',
fontsize=fontsize)
])
return hv.Overlay(texts)
def text_box(text,
xpos,
ypos,
boxsize,
fontsize=30,
fontcolor="black",
bgcolor="white"):
"""Generates a box with text in it"""
hvtext = hv.Text(xpos, ypos, text).opts(fontsize=fontsize, color=fontcolor)
box = hv.Polygons(hv.Box(xpos, ypos,
boxsize).opts(color="black")).opts(color=bgcolor)
return box * hvtext
def map_indicators(self):
self.indicateur_dpt_plot()
for idx in self.df_indic_dpt.index:
an_indic = self.df_indic_dpt.loc[idx, 'Parametre']
color = self.determine_color(an_indic,
self.df_indic_dpt.loc[idx,
'value'], 20)
self.df_indic_dpt.at[idx, 'color'] = color
dates_to_print = [
date.strftime(format='%d-%m-%Y')
for date in [self.date_ini, self.date_final]
]
tooltips = [
("Taux d'incidence", '@tx_incid_2'),
('Facteur de reproduction', '@R_2'),
("Taux d'occupation des lits en réa", '@taux_occupation_sae_2'),
('Taux de positivité', '@tx_pos_2'), ('Région', '@libelle_reg'),
('Département', '@libelle_dep')
]
hover = HoverTool(tooltips=tooltips)
key_dimensions = ['Longitude', 'Latitude', 'Date', 'Parametre']
MapDataSet = hv.Dataset(self.df_indic_dpt,
vdims=[
'value', 'color', 'libelle_reg',
'libelle_dep', 'tx_incid_2', 'R_2',
'taux_occupation_sae_2', 'tx_pos_2'
],
kdims=key_dimensions)
MapDataSet = MapDataSet.to(geoviews.Polygons)
MapRel = MapDataSet.opts(
width=1000,
height=560,
tools=[hover],
color='color',
xaxis=None,
yaxis=None,
title=
f"Cartes des indicateurs du {dates_to_print[0]} au {dates_to_print[1]}"
)
MapRel_tot = MapRel * gvts.CartoLight
sum_map = MapRel_tot
text = hv.Curve((0, 0)).opts(xaxis=None, yaxis=None) * hv.Text(
0, 0, 'Source: Santé Publique France\nGraph: C.Houzard')
MapOutput = (geoviews.Layout(sum_map + text)).cols(1)
renderer = hv.renderer('bokeh')
#renderer.save(MapOutput, os.path.normcase(f'map'))
file_fct.save_fig(MapOutput, 'Map_France_Indic', self.date_final)
def plot_pct_of_newborns(name, years):
name_tot = _query_name(name, years)
name_tseries = _finalize_obj(name_tot.hvplot(
YEAR,
PCT_NB,
hover=False,
groupby=[NAME],
color=DEFAULT_COLORS,
hover_cols=[COUNT, PCT_FM],
).overlay(NAME),
years,
hover=False)
name_points = _finalize_obj(
name_tot.hvplot.points(
YEAR,
PCT_NB,
hover=False,
hover_cols=[NAME, COUNT, PCT_FM],
cmap='RdYlBu_r').options(
color_index=PCT_FM,
colorbar=True,
marker='o',
colorbar_opts={
'title': '%F'
},
size=15,
alpha=0.15,
line_color='lightgray',
line_alpha=0.35).redim.range(pct_female=(0, 100)), years)
top_year, top_name, top_count, top_pct_female, top_pct_newborns = (
name_tot.loc[name_tot[COUNT] == name_tot[COUNT].max()].values[0])
min_year = _decide_year(top_name, name_tot)
text_align, text_offset = _smart_align(min_year)
summary_kwds = [
top_year, top_count, top_name, newborns.loc[top_year][0],
top_pct_newborns
]
name_summary = (hv.Text(
min_year + text_offset, name_tot[PCT_NB].quantile(0.985),
SUMMARY_FMT.format(*summary_kwds)).options(color='#5B5B5B',
text_align=text_align,
text_baseline='top',
text_font_size='1.05em',
text_font='Helvetica',
text_alpha=0.65))
return (name_tseries * name_points * name_summary)
def __init__(self, data, configs):
'''removes wrong data'''
for file in list(data.keys()):
if "Linewidth".lower() not in data[file]["header"][3].lower():
data.pop(file)
self.log = logging.getLogger(__name__)
self.data = convert_to_df(data, abs=False)
self.config = configs
self.df = []
self.basePlots = None
self.analysisname = "Linewidth"
self.PlotDict = {"Name": self.analysisname}
self.measurements = self.data["columns"]
self.PlotDict["All"] = None
self.sort_parameter = self.config["Linewidth"]["Bar_chart"][
"CreateBarChart"]
self.Substrate_Type = ["P+", "N+", "P-stop"]
self.filename_df = pd.DataFrame(columns=[
"Filename", "Substrate Type", "_", "Batch", "Wafer No.", "_",
"HM location", "Test structure", "Linewidth [um]",
"Standard deviation"
])
self.limits = {"P+": 0.05, "N+": 0.00001, "P-stop": 0.0005}
self.files_to_fit = self.config["files_to_fit"]
self.sheet_dic = {
"P+": self.config["Linewidth"]["parameter"]["sheet_r_p+"],
"N+": self.config["Linewidth"]["parameter"]["sheet_r_N+"],
"P-stop": self.config["Linewidth"]["parameter"]["sheet_r_ps"]
}
self.std_dic = {
"P+": self.config["Linewidth"]["parameter"]["std_p+"],
"N+": self.config["Linewidth"]["parameter"]["std_N+"],
"P-stop": self.config["Linewidth"]["parameter"]["std_ps"]
}
hvtext = hv.Text(0, 0, self.analysisname,
fontsize=25).opts(color="black", xlabel='', ylabel='')
box = hv.Polygons(hv.Box(0, 0,
300).opts(color="black")).opts(color="white")
self.PlotDict["All"] = box * hvtext
def __init__(self, data, configs):
'''removes wrong data'''
for file in list(data.keys()):
if "van-der-pauw" not in data[file]["header"][3].lower(
) and "bulk cross" not in data[file]["header"][3].lower():
data.pop(file)
self.log = logging.getLogger(__name__)
self.data = convert_to_df(data, abs=False)
self.config = configs
self.df = []
self.basePlots = None
self.analysisname = "Van_der_Pauw"
self.PlotDict = {"Name": self.analysisname}
self.measurements = self.data["columns"]
self.PlotDict["All"] = None
self.sort_parameter = self.config["Van_der_Pauw"]["Bar_chart"][
"CreateBarChart"]
self.Substrate_Type = [
"P-stop", "Polysilicon", "N+", "P+", "Metal", "bulk"
]
'''columns have to be fitted according to sample_name + sample_type layout'''
self.filename_df = pd.DataFrame(columns=[
"Filename", "Substrate Type", "_", "Batch", "Wafer No.", "_",
"HM location", "Test structure", "_", "Sheet Resistance [Ohm/sq]",
"Standard deviation"
])
self.limits = {
"P-stop": 25000,
"Polysilicon": 3000,
"N+": 50,
"P+": 1300,
"Metal": 0.03,
"bulk": 70000
}
self.files_to_fit = self.config["files_to_fit"]
hvtext = hv.Text(0, 0, self.analysisname,
fontsize=13).opts(color="black", xlabel='', ylabel='')
box = hv.Polygons(hv.Box(0, 0,
2).opts(color="black")).opts(color="white")
self.PlotDict["All"] = box * hvtext
def value_plot(
value,
unit='°C',
palette=palettes.Plasma5,
ylim=(-20, 40),
low=0,
high=40,
):
"""
On représente les mesures environnementales (température, humidité, pression) avec un
baton et un rond dans lequel sera inscrit la valeur numérique.
"""
scat = hv.Scatter({0: value}) #le rond en position 0
spik = hv.Spikes(scat) #le baton au même endroit
lab = hv.Text(x=0, y=value,
text=f"{value}{unit}") #la valeur numérique et l'unité
mapper = linear_cmap(
field_name='y', palette=palette, low=low,
high=high) #la couleur du rond dépend de la valeur
opts = dict(
ylim=ylim, #on donne la même limite à tous
color=mapper,
responsive=True, #la dimension sera dynamique ou "responsive"
xaxis=None,
yaxis=None, #pas d'axe pour aléger
toolbar='disable', #pas besoin de zoomer ou autre...
min_width=80,
height=150,
border=0)
layout = (
spik * scat * lab
).opts( #on met le label tout au dessus, puis le rond, puis le baton caché derrière
hv.opts.Scatter(size=80, **opts),
hv.opts.Spikes(line_width=3, **opts),
hv.opts.Text(color='black'),
)
return layout.opts(shared_axes=False)
def plot_ana(x, y, dy, ana_type, **kwargs):
curve = hv.Curve(zip(x, y))
derivative = hv.Curve(zip(x, dy)).opts(color="gray")
df = pd.DataFrame({"x": x, "y": y, "dy": dy})
'''returns voltage and: for Ana 1 fit line, for Ana 2/3 line to show where the voltage is'''
voltage, line = find_voltage(df, x, ana_type)
voltage = round(voltage, 4)
text_str = "voltage: " + str(voltage)
if ana_type == "Ana 3":
text_str += "\nDerivative scaled down by: \n/ (2 * 10^6)"
text = hv.Text(min(dy) * (6 / 4), max(dy) * (7 / 8), text_str, fontsize=20)
curve = curve * derivative * line * text
if ana_type == "Ana 3":
curve.opts(ylim=(min(dy) - 3 * min(y) / 20, max(dy) + max(dy) / 10), **kwargs)
else:
curve.opts(ylim=(min(y) - 3 * min(y) / 20, max(y) + max(y) / 10), **kwargs)
return curve
def __init__(self, data, configs):
'''removes wrong data'''
for file in list(data.keys()):
if "meander" not in data[file]["header"][3]:
data.pop(file)
self.log = logging.getLogger(__name__)
self.data = convert_to_df(
data, abs=False
) ## funktioniert nicht komplet da metal und poly andere messungen haben
self.complete_df(data)
self.config = configs
self.df = []
self.basePlots = None
self.analysisname = "Meander"
self.PlotDict = {"Name": self.analysisname}
self.measurements = self.data["columns"]
self.sort_parameter = self.config["Meander"]["Bar_chart"][
"CreateBarChart"]
self.Substrate_Type = ["Polysilicon", "Metal"]
self.filename_df = pd.DataFrame(columns=[
"Filename", "Substrate Type", "_", "Batch", "Wafer No.", "_",
"HM location", "Test structure", "_", "Resistivity",
"Standard deviation", "specific Resistivity [Ohm/sq]"
])
self.PlotDict["All"] = None
self.limits = {"Polysilicon": 2 * 10**6, "Metal": 450}
self.files_to_fit = self.config["files_to_fit"]
self.squares = {
"Polysilicon": self.config["Meander"]["parameter"]["squares_poly"],
"Metal": self.config["Meander"]["parameter"]["squares_m"]
}
hvtext = hv.Text(0, 0, self.analysisname,
fontsize=13).opts(color="black", xlabel='', ylabel='')
box = hv.Polygons(hv.Box(0, 0,
2).opts(color="black")).opts(color="white")
self.PlotDict["All"] = box * hvtext
def value_plot(value,unit='°C',palette=palettes.Plasma5,ylim=(-20,40),low=0,high=40,):
scat = hv.Scatter({0:value},kdims='x',vdims='y')
spik = hv.Spikes(scat,kdims='x',vdims='y')
lab = hv.Text(x=0,y=value,text=f"{value}{unit}")
mapper = linear_cmap(field_name='y',palette=palette,low=low,high=high)
opts=dict(ylim=ylim,
responsive=True,
xaxis=None,yaxis=None,
toolbar='disable',
min_width=80,
height=150,
border=0)
layout = (spik*scat*lab).opts(
hv.opts.Scatter(size=80,color=mapper,**opts),
hv.opts.Spikes(line_width=3,**opts),
hv.opts.Text(color='black'),
)
return layout.opts(shared_axes=False)
def Histogram(dfs,
measurement,
configs,
analysisType,
bins=50,
iqr=None,
**addConfigs):
"""Generates a Points Plot with a corresponding Histogram"""
newConfigs = addConfigs
log.info("Generating histograms for measurement {}...".format(measurement))
finalplots = None
try:
for key in dfs["keys"]:
log.info("Generating histograms for measurement {} for file {}...".
format(measurement, key))
# Sanatize data
data = dfs[key]["data"][measurement].dropna() # Drop all nan
if iqr:
log.info("Outliers correction with iqr: {}".format(iqr))
data = reject_outliers(data, iqr)
mean = np.round(np.mean(data), 2)
rms = np.round(np.sqrt(np.mean(data**2)), 2)
std = np.round(np.std(data), 2)
median = np.round(np.median(data), 2)
data = np.histogram(data, bins=bins)
plt = hv.Histogram(data,
label="Histogram: {}".format(measurement),
group="Histogram: {}: {}".format(
measurement, key))
try:
xlabel = "{} [{}]".format(
measurement, dfs[dfs["keys"][0]]["units"][dfs[
dfs["keys"][0]]["measurements"].index(measurement)])
except Exception as err:
log.error(
"Label could not be generated for Histogram {}. Error: {}".
format(measurement, err))
xlabel = "X-Axis"
plt.opts(xlabel=xlabel)
# Update the plot specific options if need be
generalOptions = configs[analysisType].get("General", {})
newConfigs.update(generalOptions.copy())
data_options = configs[analysisType].get(measurement, {}).get(
"Single Histogram", {}).get("PlotOptions", {})
newConfigs.update(configs[analysisType].get(
"{}Options".format("Histogram"), {}))
newConfigs.update(data_options)
plots = customize_plot(plt, "", configs[analysisType],
**newConfigs)
# Add text
text = '\nMean: {mean} \n' \
'Median: {median} \n' \
'RMS: {rms}\n' \
'std: {std}'.format(mean=mean,
median=median,
rms=rms,
std=std)
log.info(text)
y = data[0].max()
x = data[1][int(len(data[1]) * 0.9)]
text = hv.Text(x, y, text).opts(fontsize=30)
#text = text_box(text, x, y, boxsize= (100, 150))
plots = plots * text
if finalplots:
finalplots += plots
else:
finalplots = plots
except Exception as err:
log.error(
"Unexpected error happened during Hist plot generation {}. Error: {}"
.format(measurement, err))
return None
return finalplots
def concatHistogram(dfs,
measurement,
configs,
analysisType,
bins=50,
iqr=None,
**addConfigs):
"""Concatenates dataframes and generates a Histogram for all passed columns"""
newConfigs = addConfigs
log.info("Generating concat histograms for measurements {}...".format(
measurement))
try:
df = dfs["All"]
# Sanatize data
data = df[measurement].dropna() # Drop all nan
if iqr:
log.info("Outliers correction with iqr: {}".format(iqr))
data = reject_outliers(data, iqr)
mean = np.round(np.mean(data), 2)
rms = np.round(np.sqrt(np.mean(data**2)), 2)
std = np.round(np.std(data), 2)
median = np.round(np.median(data), 2)
data = np.histogram(data, bins=bins)
plt = hv.Histogram(
data,
label="Concatenated Histogram: {}".format(measurement),
group="Concatenated Histogram: {}".format(measurement))
#plt = hv.Histogram(data, vdims=to_plot, group="Concatenated Histogram: {}".format(to_plot))
try:
xlabel = "{} [{}]".format(
measurement, dfs[dfs["keys"][0]]["units"][dfs[
dfs["keys"][0]]["measurements"].index(measurement)])
except Exception as err:
log.error(
"Label could not be genereated for concatonated Histogram {}. Error: {}"
.format(measurement, err))
xlabel = "X-Axis"
plt.opts(xlabel=xlabel)
# Update the plot specific options if need be
generalOptions = configs[analysisType].get("General", {})
newConfigs.update(generalOptions.copy())
data_options = configs[analysisType].get(measurement, {}).get(
"Concatenated Histogram", {}).get("PlotOptions", {})
newConfigs.update(configs[analysisType].get(
"{}Options".format("Histogram"), {}))
newConfigs.update(data_options)
#addConfigs.update({"xlabel": measurement})
plots = customize_plot(plt, "", configs[analysisType], **newConfigs)
# Add text
text = '\nMean: {mean} \n' \
'Median: {median} \n' \
'RMS: {rms}\n' \
'std: {std}'.format(mean=mean,
median=median,
rms=rms,
std=std)
log.info(text)
y = data[0].max()
x = data[1][int(len(data[1]) * 0.9)]
text = hv.Text(x, y, text).opts(fontsize=30)
plots = plots * text
except Exception as err:
log.error(
"Unexpected error happened during concatHist plot generation {}. Error: {}"
.format(measurement, err))
return None
return plots
def h_track(x, y): #function to track pointer
y = int(np.around(y))
text = hv.Text(x, y, str(y), halign='left', valign='bottom')
return hv.HLine(y) * text
