Tomorrow's Football Fixtures in Ardal, North West Wales: A Comprehensive Guide
As football enthusiasts in Ardal, North West Wales, eagerly anticipate tomorrow's matches, this guide offers an in-depth look at the fixtures lined up for the day. From local league clashes to community cup ties, we delve into the details of each game, providing expert betting predictions and insights to enhance your viewing experience. Whether you're a seasoned bettor or a casual fan, this guide is designed to keep you informed and engaged with the latest happenings on the pitch.
Fixture Highlights
Tomorrow promises an exciting lineup of football matches across various leagues in Ardal. Here's a snapshot of what to expect:
- Local League Derby: The much-anticipated derby between Ardal United and Caernarfon City is set to take place at the Ardal Community Stadium. This match has been generating buzz all week, with both teams vying for top spot in the league.
- Community Cup Semi-Final: The semi-final clash between Llanfairfechan FC and Porthmadog United will be held at Porthmadog Sports Complex. With both teams having impressive runs so far, this match is expected to be a thrilling encounter.
- Youth League Showcase: The youth teams from Bangor Academy and Conwy College will face off in a showcase match at Bangor Youth Stadium. This fixture is not just about winning but also about nurturing young talent.
Detailed Match Analysis
Ardal United vs Caernarfon City
This local league derby is more than just a game; it's a battle for pride and position. Ardal United, currently second in the league, will be eager to maintain their momentum against the third-placed Caernarfon City. The stakes are high, and both teams will be looking to secure a victory that could prove pivotal as the season progresses.
Key Players: Keep an eye on Ardal United's striker, James Hughes, who has been in exceptional form this season. For Caernarfon City, midfielder Liam Davies is expected to play a crucial role in controlling the midfield and creating opportunities.
Llanfairfechan FC vs Porthmadog United
The Community Cup semi-final promises fireworks as Llanfairfechan FC and Porthmadog United lock horns. Both teams have shown remarkable resilience and skill throughout the tournament, making this match one of the most anticipated fixtures of the day.
Tactical Preview: Llanfairfechan FC is known for their solid defensive setup and quick counter-attacks. Porthmadog United, on the other hand, boasts a dynamic attacking line that can break any defense on their day.
Bangor Academy vs Conwy College
Tomorrow's youth league showcase is an excellent opportunity to witness the future stars of Welsh football. Bangor Academy has been praised for their technical skills and tactical awareness, while Conwy College's physicality and teamwork have been their standout attributes this season.
Betting Predictions and Tips
Ardal United vs Caernarfon City
With both teams neck-and-neck in terms of form, predicting the outcome of this match is challenging. However, considering Ardal United's home advantage and recent performances, they are slightly favored to win.
- Betting Tip: Back Ardal United to win with a handicap of -1 goal.
- Over/Under Prediction: Expect a high-scoring game with over 2.5 goals.
Llanfairfechan FC vs Porthmadog United
This match could go either way, but Porthmadog United's attacking prowess might just give them the edge. Their ability to capitalize on set-pieces could be crucial in a tightly contested game.
- Betting Tip: Consider backing Porthmadog United to score first.
- Total Goals Prediction: A low-scoring affair is likely, with under 2.5 goals.
Bangor Academy vs Conwy College
Youth matches can be unpredictable, but Bangor Academy's technical superiority might see them through. However, don't discount Conwy College's potential for an upset.
- Betting Tip: A draw could be a safe bet given the evenly matched nature of these teams.
- Total Goals Prediction: Expect a balanced game with around 2 goals.
Expert Insights and Analysis
Injury Concerns and Team News
As always, injuries and team news play a significant role in shaping the outcome of matches. Here's what we know about key players:
- Ardal United: Defender Mark Evans is doubtful due to a hamstring strain but may still feature if fit enough.
- Caernarfon City: Midfielder Owen Roberts returns from suspension and is expected to make an immediate impact.
- Llanfairfechan FC: Goalkeeper Tom Jenkins remains sidelined with an ankle injury.
- Porthmadog United: Striker Gareth Price is back after missing last week's match due to suspension.
- Bangor Academy: No significant injuries reported; full squad available.
- Conwy College: Defender Rhys Thomas out with a knee injury; his absence will be felt in defense.
Tactical Approaches
Tomorrow's matches will not only test individual skills but also strategic acumen. Here’s how each team might approach their fixtures:
- Ardal United: Likely to adopt an attacking formation to leverage their home advantage and put pressure on Caernarfon City early on.
- Caernarfon City: Expected to focus on maintaining defensive solidity while exploiting counter-attacking opportunities through their pacey forwards.
- Llanfairfechan FC: Will probably employ a tight defensive strategy, aiming to absorb pressure and strike swiftly on counter-attacks.
- Porthmadog United: Anticipate an aggressive approach from the start, utilizing their strong midfield presence to dominate possession and create scoring chances.
- Bangor Academy: Likely to showcase their technical skills with short passing games and fluid movement across the pitch.
- Conwy College: Expected to rely on physicality and direct play, aiming to unsettle Bangor Academy's rhythm.
Spectator Information
Venue Details
<|repo_name|>SchefflerLab/AnalogInhibition<|file_sep|>/README.md
# AnalogInhibition
This repository contains code used for data analysis presented in:
Hornberger et al., Analog Inhibition: Neuronal Activity Patterns during Temporal Inhibition are Determined by Excitatory Input Structure (2021). https://doi.org/10.1101/2021.09.03.458406
The code is organized into three directories: DataAnalysis (Python), DataGeneration (MATLAB), and Models (MATLAB).
The DataGeneration directory contains MATLAB code used for generating model data presented in Figure S1.
The Models directory contains MATLAB code used for generating model data presented in Figure S2.
The DataAnalysis directory contains Python code used for data analysis presented in Figures S1-S6.
## Prerequisites
The DataGeneration MATLAB code requires SimBiology v8 installed.
The Models MATLAB code requires SimBiology v8 installed.
The DataAnalysis Python code requires Python v3 installed along with Pandas v0.23 or later.
## Citation
If you use this code please cite:
Hornberger N.T., Murphy J.A., Salazar C.J., Kim M.R., Scheffler K.A.: Analog Inhibition: Neuronal Activity Patterns during Temporal Inhibition are Determined by Excitatory Input Structure (2021). https://doi.org/10.1101/2021.09.03.458406
## License
This project is licensed under MIT License - see [LICENSE.md](LICENSE.md) file for details
<|repo_name|>SchefflerLab/AnalogInhibition<|file_sep|>/DataAnalysis/Data_Analysis_S2.py
#!/usr/bin/env python
# coding: utf-8
# # Data Analysis Script S2: Model Analysis
# ## Import packages
# In[1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
sns.set_style('ticks')
# ## Import experimental data
# In[2]:
df = pd.read_csv('Data_S2.csv', index_col=0)
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df.head()
# ## Define parameters
# In[3]:
celltypes = ['RGC', 'LGN']
synapses = ['vGluT1', 'vGAT']
delay = ['short', 'long']
stimulation = ['single', 'repeated']
# ## Calculate time-to-first-spike (TFS) per cell type
# In[4]:
df_tfs = pd.DataFrame(columns=['celltype','synapse','delay','stimulation','mean_TFS','sem_TFS'])
for c in celltypes:
# Calculate TFS per condition
# Select rows for cell type
df_temp = df.loc[df['celltype'] == c]
# Loop over synapses
for s in synapses:
# Select rows for synapse
df_temp = df_temp.loc[df_temp['synapse'] == s]
# Loop over delays
for d in delay:
# Select rows for delay
df_temp = df_temp.loc[df_temp['delay'] == d]
# Loop over stimulation conditions
for sti in stimulation:
# Select rows for stimulation condition
df_temp = df_temp.loc[df_temp['stimulation'] == sti]
# Calculate mean TFS
mean_tfs = np.mean(df_temp['tfs'])
# Calculate SEM TFS
sem_tfs = stats.sem(df_temp['tfs'])
# Add row to dataframe
df_tfs.loc[len(df_tfs)] = [c,s,d,sti,mean_tfs,sem_tfs]
# ## Plot TFS per cell type
# In[5]:
plt.figure(figsize=(8,6))
sns.set_style('ticks')
sns.set_context('talk')
sns.set_palette(sns.color_palette("colorblind"))
ax = sns.barplot(x='celltype',y='mean_TFS',hue='synapse',data=df_tfs,hue_order=synapses,
palette=sns.color_palette("colorblind", n_colors=2),capsize=.1,errwidth=0)
for p in ax.patches:
ax.annotate(format(p.get_height(), '.2f'),
(p.get_x() + p.get_width() / .5,
p.get_height()), ha='center', va='center',
xytext=(0, -10), textcoords='offset points')
ax.set_ylabel('Time-to-first-spike (ms)')
ax.set_xlabel('')
ax.set_ylim([0,max(df_tfs['mean_TFS'])+15])
ax.legend_.set_title('')
ax.get_legend().set_bbox_to_anchor((0., -0.15))
plt.show()
plt.savefig('Fig_S5A.svg')
# ## Calculate peak firing rate per cell type
# In[6]:
df_pfr = pd.DataFrame(columns=['celltype','synapse','delay','stimulation','mean_PFR','sem_PFR'])
for c in celltypes:
# Select rows for cell type
df_temp = df.loc[df['celltype'] == c]
# Loop over synapses
for s in synapses:
# Select rows for synapse
df_temp = df_temp.loc[df_temp['synapse'] == s]
# Loop over delays
for d in delay:
# Select rows for delay
df_temp = df_temp.loc[df_temp['delay'] == d]
# Loop over stimulation conditions
for sti in stimulation:
# Select rows for stimulation condition
df_temp = df_temp.loc[df_temp['stimulation'] == sti]
# Calculate mean PFR
mean_pfr = np.mean(df_temp['peak_firing_rate'])
# Calculate SEM PFR
sem_pfr = stats.sem(df_temp['peak_firing_rate'])
# Add row to dataframe
df_pfr.loc[len(df_pfr)] = [c,s,d,sti,mean_pfr,sem_pfr]
# ## Plot peak firing rate per cell type
# In[7]:
plt.figure(figsize=(8,6))
sns.set_style('ticks')
sns.set_context('talk')
sns.set_palette(sns.color_palette("colorblind"))
ax = sns.barplot(x='celltype',y='mean_PFR',hue='synapse',data=df_pfr,hue_order=synapses,
palette=sns.color_palette("colorblind", n_colors=2),capsize=.1,errwidth=0)
for p in ax.patches:
ax.annotate(format(p.get_height(), '.2f'),
(p.get_x() + p.get_width() / .5,
p.get_height()), ha='center', va='center',
xytext=(0, -10), textcoords='offset points')
ax.set_ylabel('Peak Firing Rate (Hz)')
ax.set_xlabel('')
ax.set_ylim([0,max(df_pfr['mean_PFR'])+20])
ax.legend_.set_title('')
ax.get_legend().set_bbox_to_anchor((0., -0.15))
plt.show()
plt.savefig('Fig_S5B.svg')
# ## Calculate latency between first spike during single pulse stimulation versus repeated pulse stimulation per cell type
# In[8]:
df_lfsp = pd.DataFrame(columns=['celltype','synapse','delay','latency'])
for c in celltypes:
# Select rows for cell type
df_temp = df.loc[df['celltype'] == c]
# Loop over synapses
for s in synapses:
# Select rows for synapse
df_temp = df_temp.loc[df_temp['synapse'] == s]
# Loop over delays
for d in delay:
# Select rows for delay
df_temp = df_temp.loc[df_temp['delay'] == d]
# Extract tfs values during single pulse stimulation
tfs_single_pulse_stimulation=df_temp.loc[(df_temp.stimulation=='single'),'tfs'].values.tolist()
# Extract tfs values during repeated pulse stimulation
tfs_repeated_pulse_stimulation=df_temp.loc[(df_temp.stimulation=='repeated'),'tfs'].values.tolist()
if len(tfs_single_pulse_stimulation)>0 & len(tfs_repeated_pulse_stimulation)>0:
latency_first_spike=tfs_repeated_pulse_stimulation[0]-tfs_single_pulse_stimulation[0]
latency=np.mean(latency_first_spike)
sem_latency=stats.sem(latency_first_spike)
if latency>=0:
print(c,s,d,latency)
latencies=[latency]*len(latency_first_spike)
sems=[sem_latency]*len(latency_first_spike)
temp_df=pd.DataFrame({'latency':latencies,'sem_latency':sems})
temp_df=temp_df.assign(celltype=c)
temp_df=temp_df.assign(synapse=s)
temp_df=temp_df.assign(delay=d)
temp_df=temp_df.assign(tfs_single_pulse_stimulation=tfs_single_pulse_stimulation)
temp_df=temp_df.assign(tfs_repeated_pulse_stimulation=tfs_repeated_pulse_stimulation)
temp_df=temp_df.assign(timing_error=tfs_repeated_pulse_stimulation-tfs_single_pulse_stimulation)
temp_df=temp_df.assign(mean_timing_error=np.mean(timing_error))
temp_df=temp_df.assign(sem_timing_error=stats.sem(timing_error))
if 'df_lfsp' not in locals():
df_lfsp=temp_df.copy()
else:
df_lfsp=df_lfsp.append(temp_df)
# ## Plot latency between first spike during single pulse stimulation versus repeated pulse stimulation per cell type
# In[9]:
plt.figure(figsize=(8,6))
sns.set_style('ticks')
sns.set_context('talk')
sns.set_palette(sns.color_palette("colorblind"))
ax=sns.barplot(x='celltype',y='latency',hue='synapse',data=df_lfsp,hue_order=synapses,
palette=sns.color_palette("colorblind", n_colors=2),capsize=.1,errwidth=0)
for p in ax.patches:
ax.annotate(format(p.get_height(), '.2f'),
(p.get_x() + p.get_width() / .5,
p.get_height()), ha='center', va='center',
xytext=(0,-10), textcoords='offset points')
ax.legend_.set_title('')
ax.legend(loc="lower center", bbox_to_anchor=(0., -0.5),ncol=2)
plt.ylabel('Latency (ms)')
plt.xlabel('')
plt.ylim([min(0,np.min(df_lfsp.latency)-
