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adding a progress bar #215

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merged 2 commits into from
Sep 2, 2024
Merged

adding a progress bar #215

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0383d8b
adding a progress bar
K20shores Aug 29, 2024
d678904
handling merge
K20shores Sep 2, 2024
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3 changes: 2 additions & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,8 @@ dependencies = [
"musica==0.7.3",
"xarray",
"colorlog",
"pandas"
"pandas",
"tqdm"
]

[project.urls]
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134 changes: 69 additions & 65 deletions src/acom_music_box/music_box.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,9 @@
import os
import pandas as pd

from tqdm import tqdm
from tqdm.contrib.logging import logging_redirect_tqdm

import logging
logger = logging.getLogger(__name__)

Expand Down Expand Up @@ -157,71 +160,72 @@ def solve(self, output_path=None, callback=None):
next_output_time = curr_time
# runs the simulation at each timestep

while (curr_time <= self.box_model_options.simulation_length):

# iterates evolving conditions if enough time has elapsed
while (
next_conditions is not None and next_conditions_time <= curr_time):

curr_conditions.update_conditions(next_conditions)
ordered_rate_constants = self.order_reaction_rates(
curr_conditions, rate_constant_ordering)

# iterates next_conditions if there are remaining evolving
# conditions
if (len(self.evolving_conditions) > next_conditions_index + 1):
next_conditions_index += 1
next_conditions = self.evolving_conditions.conditions[next_conditions_index]
next_conditions_time = self.evolving_conditions.times[next_conditions_index]
else:
next_conditions = None

# calculate air density from the ideal gas law
BOLTZMANN_CONSTANT = 1.380649e-23
AVOGADRO_CONSTANT = 6.02214076e23
GAS_CONSTANT = BOLTZMANN_CONSTANT * AVOGADRO_CONSTANT
air_density = curr_conditions.pressure / \
(GAS_CONSTANT * curr_conditions.temperature)

# outputs to output_array if enough time has elapsed
if (next_output_time <= curr_time):
row = []
row.append(next_output_time)
row.append(curr_conditions.temperature)
row.append(curr_conditions.pressure)
row.append(air_density)
for conc in ordered_concentrations:
row.append(conc)
output_array.append(row)
next_output_time += self.box_model_options.output_step_time

# calls callback function if present
if callback is not None:
df = pd.DataFrame(output_array[:-1], columns=output_array[0])
callback(df, curr_time, curr_conditions, self.box_model_options.simulation_length)

# ensure the time step is not greater than the next update to the
# evolving conditions or the next output time
time_step = self.box_model_options.chem_step_time
if (next_conditions is not None and next_conditions_time > curr_time):
time_step = min(time_step, next_conditions_time - curr_time)
if (next_output_time > curr_time):
time_step = min(time_step, next_output_time - curr_time)

# solves and updates concentration values in concentration array
if (not ordered_concentrations):
logger.info("Warning: ordered_concentrations list is empty.")
musica.micm_solve(
self.solver,
time_step,
curr_conditions.temperature,
curr_conditions.pressure,
air_density,
ordered_concentrations,
ordered_rate_constants)

# increments time
curr_time += time_step
simulation_length = self.box_model_options.simulation_length
with tqdm(total=simulation_length, desc="Simulation Progress", unit=f" [model integration steps ({self.box_model_options.chem_step_time} s)]", leave=False) as pbar:
while curr_time < simulation_length:
# iterates evolving conditions if enough time has elapsed
while (next_conditions is not None and next_conditions_time <= curr_time):

curr_conditions.update_conditions(next_conditions)
ordered_rate_constants = self.order_reaction_rates(
curr_conditions, rate_constant_ordering)

# iterates next_conditions if there are remaining evolving
# conditions
if (len(self.evolving_conditions) > next_conditions_index + 1):
next_conditions_index += 1
next_conditions = self.evolving_conditions.conditions[next_conditions_index]
next_conditions_time = self.evolving_conditions.times[next_conditions_index]
else:
next_conditions = None

# calculate air density from the ideal gas law
BOLTZMANN_CONSTANT = 1.380649e-23
AVOGADRO_CONSTANT = 6.02214076e23
GAS_CONSTANT = BOLTZMANN_CONSTANT * AVOGADRO_CONSTANT
air_density = curr_conditions.pressure / \
(GAS_CONSTANT * curr_conditions.temperature)

# outputs to output_array if enough time has elapsed
if (next_output_time <= curr_time):
row = []
row.append(next_output_time)
row.append(curr_conditions.temperature)
row.append(curr_conditions.pressure)
row.append(air_density)
for conc in ordered_concentrations:
row.append(conc)
output_array.append(row)
next_output_time += self.box_model_options.output_step_time

# calls callback function if present
if callback is not None:
df = pd.DataFrame(output_array[:-1], columns=output_array[0])
callback(df, curr_time, curr_conditions, self.box_model_options.simulation_length)

# ensure the time step is not greater than the next update to the
# evolving conditions or the next output time
time_step = self.box_model_options.chem_step_time
if (next_conditions is not None and next_conditions_time > curr_time):
time_step = min(time_step, next_conditions_time - curr_time)
if (next_output_time > curr_time):
time_step = min(time_step, next_output_time - curr_time)

# solves and updates concentration values in concentration array
if (not ordered_concentrations):
logger.info("Warning: ordered_concentrations list is empty.")
musica.micm_solve(
self.solver,
time_step,
curr_conditions.temperature,
curr_conditions.pressure,
air_density,
ordered_concentrations,
ordered_rate_constants)

# increments time
curr_time += time_step
pbar.update(time_step)
df = pd.DataFrame(output_array[1:], columns=output_array[0])
# outputs to file if output is present
if output_path is not None:
Expand Down