def generate_graphs(n_intervals):
"""
This callback generates three simple graphs from random data.
"""
# simulate expensive graph generation process
print("running")
print("n_intervals: ", n_intervals)
results_dict = {}
for ctag in COMPRESSOR_TAGS:
print(f"Loading {ctag}.")
json_files = [str(f) for f in DATA_PATH.glob("*.json")]
json_files = [f for f in json_files if ctag in f]
json_files = sorted(json_files)
eff_file = json_files[0]
sample_datetime_raw = eff_file.split("time-")[-1]
sample_date, sample_time = sample_datetime_raw.split("_")
sample_time = sample_time.replace("-", ":").replace(".json", "")
sample_datetime = sample_date + " " + sample_time
results_dict[f"sample-time-{ctag}"] = sample_datetime
for curve in ["head", "eff", "power"]:
# Head
file_path = DATA_PATH / f"{ctag}_{curve}_plot-time-{sample_datetime_raw}"
fig = pio.read_json(str(file_path))
results_dict[f"{curve}-{ctag}"] = fig
# save figures in a dictionary for sending to the dcc.Store
return results_dict
def test_read_json_from_filelike(fig1, fig_type_spec, fig_type):
# Configure file-like mock
filemock = MagicMock()
filemock.read.return_value = pio.to_json(fig1)
# read_json on mock file
fig1_loaded = pio.read_json(filemock, output_type=fig_type_spec)
# Check return type
assert isinstance(fig1_loaded, fig_type)
# Check loaded figure
assert pio.to_json(fig1_loaded) == pio.to_json(fig1.to_dict())
def test_read_json_from_filelike(fig1, fig_type_spec, fig_type):
# Configure file-like mock
filemock = MagicMock()
filemock.read.return_value = pio.to_json(fig1)
# read_json on mock file
fig1_loaded = pio.read_json(filemock, output_type=fig_type_spec)
# Check return type
assert isinstance(fig1_loaded, fig_type)
# Check loaded figure
assert pio.to_json(fig1_loaded) == pio.to_json(fig1.to_dict())
def open_profile(self, profile):
logging.info(f"""{date.strftime("%d/%m/%Y %H:%M")} Starting new log.
Opening profile: {profile}""")
print(f"Loading {profile}...")
fig = pio.read_json(rf"{self.relativepath}\profiles\{profile}.json"
) # open main profile
try:
gffig = pio.read_json(
rf"{self.relativepath}\profiles\{profile}_gf.json"
) # open gene frequency plot
except FileNotFoundError:
self.show_genefrequency = True
logging.warning(
"No gene frequency file associated with this profile. Possibly no genotypic data available."
)
logging.info("Profile associated files openned.")
if self.export:
self.export_HTML(profile, fig,
gffig) # export the profile if user requested
print(f"{profile} has loaded... \nNow plotting...")
pio.show(fig)
logging.info("Main plot displayed")
if not self.show_genefrequency:
pio.show(gffig)
logging.info("Gene frequency plot displayed")
logging.info("Done. \n \n")
print(f"{profile} is now displaying...")
def test_read_json_from_file_string(fig1, fig_type_spec, fig_type):
with tempfile.TemporaryDirectory() as dir_name:
# Write json file
path = os.path.join(dir_name, "fig1.json")
with open(path, "w") as f:
f.write(pio.to_json(fig1))
# read json from file as string
fig1_loaded = pio.read_json(path, output_type=fig_type_spec)
# Check return type
assert isinstance(fig1_loaded, fig_type)
# Check loaded figure
assert pio.to_json(fig1_loaded) == pio.to_json(fig1.to_dict())
def test_read_json_from_file_string(fig1, fig_type_spec, fig_type):
with tempfile.TemporaryDirectory() as dir_name:
# Write json file
path = os.path.join(dir_name, 'fig1.json')
with open(path, 'w') as f:
f.write(pio.to_json(fig1))
# read json from file as string
fig1_loaded = pio.read_json(path, output_type=fig_type_spec)
# Check return type
assert isinstance(fig1_loaded, fig_type)
# Check loaded figure
assert pio.to_json(fig1_loaded) == pio.to_json(fig1.to_dict())
def to_plotly(
self,
subplot_width: float = 350.0,
subplot_height: float = 350.0,
subplots_per_row: Optional[int] = None,
) -> "PlotlyFigure":
"""Converts the pydantic representation of a plotly figure to an actual
plotly figure object.
Parameters
----------
subplot_width
The width of each subplot.
subplot_height
The height of each subplot.
subplots_per_row
The maximum subplots per row. If none, all subplots will be drawn on a
single row.
Notes
-----
* This function is mainly available only for debug purposes.
"""
from plotly.io import read_json
# Build up the rest of the JSON dictionary.
figure_dictionary = {
"data": [{
**trace.dict(), "xaxis": f"x{i + 1}",
"yaxis": f"y{i + 1}"
} for i, subplot in enumerate(self.subplots)
for trace in subplot.traces],
"layout":
self._generate_layout(subplot_width, subplot_height,
subplots_per_row),
"config": {
"displayModeBar": False
},
}
figure = read_json(StringIO(json.dumps(figure_dictionary)))
return figure
def html_exporter(file_path: str, new_file_path: str):
plotly_fig = read_json(file_path)
write_html(plotly_fig, new_file_path)
def _load(self) -> graph_objects.Figure:
load_path = get_filepath_str(self._get_load_path(), self._protocol)
return pio.read_json(load_path, **self._load_args)
import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.io as pio
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
plot_3d = pio.read_json('../plots/plot_3d.json')
plot_2d = pio.read_json('../plots/plot_2d.json')
app.layout = html.Div(children=[
dcc.Markdown(children='''
# Dash > Power BI
Por que?
* Gratis
* Python
* Flex
'''),
dcc.Graph(id="3d", figure=plot_3d),
dcc.Graph(id="2d", figure=plot_2d),
])
if __name__ == '__main__':
app.run_server(debug=True)